{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":43086,"title":"Recursion at variable input","description":"input of any length\r\na =2\r\nb =2\r\nc =3\r\n\r\noutput = (a^b)^c = 64","description_html":"\u003cp\u003einput of any length\r\na =2\r\nb =2\r\nc =3\u003c/p\u003e\u003cp\u003eoutput = (a^b)^c = 64\u003c/p\u003e","function_template":"function y = pow2Pow(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(pow2Pow(x),y_correct))\r\n%%\r\nx = 2;\r\ny =2;\r\ny_correct = 4;\r\nassert(isequal(pow2Pow(x,y),y_correct))\r\n%%\r\nx = 2;\r\ny= 2;\r\nz=3;\r\ny_correct = 64;\r\nassert(isequal(pow2Pow(x,y,z),y_correct))\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":0,"created_by":13865,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":67,"test_suite_updated_at":"2016-10-29T17:07:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-06T01:29:47.000Z","updated_at":"2026-03-11T08:26:55.000Z","published_at":"2016-10-06T01:31:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput of any length a =2 b =2 c =3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput = (a^b)^c = 64\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60618,"title":"ICFP2024 005: Lambdaman 1, 2, 3","description":"The ICFP2024 contest was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\r\nThe ICFP Language is based on Lambda Calculus.\r\nThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\r\nThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\r\nThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\r\n\r\nThe ICFP competition is more about manual solving optimizations for each unique problem.\r\nThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 315px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 157.5px; transform-origin: 407px 157.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/task.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2024 contest\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 300px 8px; transform-origin: 300px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/icfp.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP Language\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 40.5px 8px; transform-origin: 40.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is based on \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Lambda_calculus\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eLambda Calculus\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 356.5px 8px; transform-origin: 356.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.5px 8px; transform-origin: 357.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 285.5px 8px; transform-origin: 285.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP competition is more about manual solving optimizations for each unique problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [pathbest]=Lambdaman_123(m)\r\n Lmax=Inf;\r\n %m % Wall0 Lambda1 Cheese2 Eaten3\r\n [nr,nc]=size(m);\r\n adj=[-1 1 -nr nr]; % using index requires a wall ring around maze\r\n \r\n pathn=''; %UDLR\r\n ztic=tic;tmax=35; %Recursion time limit\r\n Lbest=inf;\r\n pathbest='';\r\n mstate=m(:)'; % recursion performs maze state checks to avoid dupolication\r\n mstaten=mstate;\r\n L=0;\r\n mn=m;\r\n Lidxn=find(m==1);\r\n [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L, ...\r\n pathn,mn,Lidxn,mstaten,Lmax); %use VARn for recursion updates\r\n\r\n toc(ztic)\r\nend %Lambda_123\r\n\r\nfunction [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L, ...\r\n path,m,Lidx,mstate,Lmax)\r\n\r\n%Conditional recursion aborts\r\n if toc(ztic)\u003etmax,return;end %Recursion time-out\r\n if L\u003eLmax,return;end % Limit recursion trials to known Lmax\r\n if L\u003e=Lbest,return;end % Bail on long solutions\r\n \r\n m(Lidx)=3;\r\n if nnz(m==2)==0 % Solution case. Better solution found\r\n Lbest=L;\r\n pathbest=path;\r\n toc(ztic)\r\n fprintf('Lbest=%i ',Lbest);fprintf('Path=%s',pathbest);fprintf('\\n\\n');\r\n return;\r\n end\r\n \r\n UDLR='UDLR';\r\n \r\n mn=m;\r\n Cadj=m(adj+Lidx);\r\n for i=1:4 % UDLR\r\n if Cadj(i)\u003e0 % Ignore into wall Cadj==0 movement\r\n Lidxn=Lidx+adj(i);\r\n mn(Lidxn)=1;\r\n mn_state=mn(:)';\r\n \r\n if nnz(sum(abs(mstate-mn_state),2)==0) % Pre-exist state check\r\n mn(Lidxn)=m(Lidxn); % Reset mn\r\n continue; %Abort when create an existing prior state\r\n end\r\n \r\n mstaten=[mstate;mn_state]; % When update walls re-init mstate\r\n pathn=[path UDLR(i)];\r\n \r\n [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L+1, ...\r\n pathn,mn,Lidxn,mstaten,Lmax);\r\n \r\n mn(Lidxn)=m(Lidxn); % reset mn in fastest way\r\n end\r\n end % for UDLR\r\nend %maze_rec","test_suite":"%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 1 optimal solution L15 LLLDURRRUDRRURR\r\nms=['###.#...'\r\n '...L..##'\r\n '.#######'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 2 optimal solution L26 RDURRDDRRUUDDLLLDLLDDRRRUR\r\nms=['L...#.'\r\n '#.#.#.'\r\n '##....'\r\n '...###'\r\n '.##..#'\r\n '....##'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 3 optimal solution L40 DRDRLLLUDLLUURURLLURLUURRDRDRDRDUUUULDLU\r\nms=[ '......'\r\n '.#....'\r\n '..#...'\r\n '...#..'\r\n '..#L#.'\r\n '.#...#'\r\n '......'];\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-07-13T05:35:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-07-13T05:05:28.000Z","updated_at":"2026-03-11T09:46:10.000Z","published_at":"2024-07-13T05:35:58.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/task.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2024 contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/icfp.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP Language\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is based on \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lambda_calculus\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLambda Calculus\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP competition is more about manual solving optimizations for each unique problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1827,"title":"Negation the hard way","description":"Write a function that has the following property: f(f(x)) = -x for any numeric array x.\r\n\r\nNote that there is no restriction on the output for f(x). How bizarre can you make it?","description_html":"\u003cp\u003eWrite a function that has the following property: f(f(x)) = -x for any numeric array x.\u003c/p\u003e\u003cp\u003eNote that there is no restriction on the output for f(x). How bizarre can you make it?\u003c/p\u003e","function_template":"function y = neg2(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = rand(2,2,2);\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = [];\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = sqrt(-5);\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%% What does a single call give?\r\nx = rand;\r\ndisp(neg2(x))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":1011,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":71,"test_suite_updated_at":"2013-08-15T02:58:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-08-14T22:59:34.000Z","updated_at":"2026-02-26T12:24:27.000Z","published_at":"2013-08-14T22:59:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that has the following property: f(f(x)) = -x for any numeric array x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that there is no restriction on the output for f(x). How bizarre can you make it?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44543,"title":"Normie Function","description":"So, I built a function and gave it a name- _Normie_.\r\n*Find the nth term of Normie function:*\r\n_f(n)= 1*f(n-1)+ 2*f(n-3)+ 3_ , *when n\u003e3* and _0_ , *when n\u003c=3*.","description_html":"\u003cp\u003eSo, I built a function and gave it a name- \u003ci\u003eNormie\u003c/i\u003e. \u003cb\u003eFind the nth term of Normie function:\u003c/b\u003e \u003ci\u003ef(n)= 1*f(n-1)+ 2*f(n-3)+ 3\u003c/i\u003e , \u003cb\u003ewhen n\u0026gt;3\u003c/b\u003e and \u003ci\u003e0\u003c/i\u003e , \u003cb\u003ewhen n\u0026lt;=3\u003c/b\u003e.\u003c/p\u003e","function_template":"function y = nth_term(n)\r\n y = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 2;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 3;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 4;\r\ny_correct = 3;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 5;\r\ny_correct = 6;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 10;\r\ny_correct = 93;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 11;\r\ny_correct = 162;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 20;\r\ny_correct = 18753;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 35;\r\ny_correct = 51651090;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 50;\r\ny_correct = 142236278205;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 70;\r\ny_correct = 5490159117130629;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 75;\r\ny_correct = 76953534045721408;\r\nassert(isequal(nth_term(n),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":104442,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":"2018-03-28T11:14:13.000Z","rescore_all_solutions":false,"group_id":61,"created_at":"2018-03-21T19:10:33.000Z","updated_at":"2026-03-16T11:15:12.000Z","published_at":"2018-03-21T19:30:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo, I built a function and gave it a name-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNormie\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFind the nth term of Normie function:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ef(n)= 1*f(n-1)+ 2*f(n-3)+ 3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewhen n\u0026gt;3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewhen n\u0026lt;=3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":58748,"title":"Fibonacci Sequence","description":"Write a MATLAB function called fibonacci_sequence(n) that takes an integer n as input and returns the first n terms of the Fibonacci sequence as an array.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 42px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407.5px 21px; transform-origin: 407.5px 21px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 21px; text-align: left; transform-origin: 384.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a MATLAB function called \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003efibonacci_sequence(n)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e that takes an integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as input and returns the first \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e terms of the Fibonacci sequence as an array.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = fibonacci_sequence(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = [0];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n%%\r\nx = 2;\r\ny_correct = [0, 1];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n%%\r\nx = 6;\r\ny_correct = [0, 1, 1, 2, 3, 5];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3494988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":23,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-18T20:37:26.000Z","updated_at":"2026-03-11T08:32:57.000Z","published_at":"2023-07-18T20:37:26.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a MATLAB function called \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efibonacci_sequence(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that takes an integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as input and returns the first \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e terms of the Fibonacci sequence as an array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":48990,"title":"Solve the recursion","description":"Solve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\r\nf(1)=4;\r\nf(2)=8;","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 81px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 40.5px; transform-origin: 407px 40.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 141px 8px; transform-origin: 141px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSolve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef(1)=4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef(2)=8;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rec(n)\r\n y = n;\r\nend","test_suite":"%%\r\nfiletext = fileread('rec.m');\r\nillegal = contains(filetext, 'switch') || contains(filetext, 'else') \r\nassert(~illegal)\r\n%%\r\nn = 1;\r\ny_correct = 4;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 5;\r\ny_correct = 44;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 13;\r\ny_correct = 2204;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 20;\r\ny_correct = 64076;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 34;\r\ny_correct = 54018518;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 37;\r\ny_correct = 228826124;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 40;\r\ny_correct = 969323026;\r\nassert(isequal(rec(n),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":698530,"edited_by":223089,"edited_at":"2022-12-12T06:11:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":38,"test_suite_updated_at":"2022-12-12T06:11:46.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-22T17:50:06.000Z","updated_at":"2026-03-23T17:38:43.000Z","published_at":"2020-12-31T01:21:21.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef(1)=4;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef(2)=8;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1836,"title":"Negation and new variables","description":"Inspired by Problem 1827 by Andrew Newell.\r\n\r\nWrite a function that has the following property: \r\n\r\n(x~=y)\r\n\r\nneg3(x)=x;\r\n\r\nneg3(y)=y;\r\n\r\nneg3(x)=-x;\r\n\r\nneg3(y)=-y;\r\n\r\n...\r\n\r\nThe test suite is simple. The goal is just to obtain a general method.","description_html":"\u003cp\u003eInspired by Problem 1827 by Andrew Newell.\u003c/p\u003e\u003cp\u003eWrite a function that has the following property:\u003c/p\u003e\u003cp\u003e(x~=y)\u003c/p\u003e\u003cp\u003eneg3(x)=x;\u003c/p\u003e\u003cp\u003eneg3(y)=y;\u003c/p\u003e\u003cp\u003eneg3(x)=-x;\u003c/p\u003e\u003cp\u003eneg3(y)=-y;\u003c/p\u003e\u003cp\u003e...\u003c/p\u003e\u003cp\u003eThe test suite is simple. The goal is just to obtain a general method.\u003c/p\u003e","function_template":"function y = neg3(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny1=-1;\r\nneg3(neg3(x));\r\nassert(isequal(ans,y1))\r\n\r\n%%\r\nx=2;\r\ny=3;\r\nz=4;\r\nneg3(x);\r\nassert(isequal(ans,2))\r\nneg3(y);\r\nassert(isequal(ans,3))\r\nneg3(x);\r\nassert(isequal(ans,-2))\r\nneg3(z);\r\nassert(isequal(ans,4))\r\nneg3(x);\r\nassert(isequal(ans,2))\r\nneg3(z);\r\nassert(isequal(ans,-4))\r\n\r\n%%\r\nx=0;\r\ny=10;\r\nneg3(x);\r\nassert(isequal(ans,0))\r\nneg3(x);\r\nassert(isequal(ans,0))\r\nneg3(y);\r\nassert(isequal(ans,10))\r\nneg3(y);\r\nassert(isequal(ans,-10))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-08-19T17:05:10.000Z","updated_at":"2013-08-19T17:18:06.000Z","published_at":"2013-08-19T17:09:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by Problem 1827 by Andrew Newell.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that has the following property:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(x~=y)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(x)=x;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(y)=y;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(x)=-x;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(y)=-y;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe test suite is simple. The goal is just to obtain a general method.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":292,"title":"Infernal Recursion","description":"Consider the recursion relation:\r\n\r\nx_n = (x_(n-1)*x_(n-2))^k\r\n\r\nGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\r\n\r\nFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\r\n\r\n R = get_recurse(exp(1),pi,5,4/9)\r\n\r\n R =\r\n 2.31187497992966\r\n","description_html":"\u003cp\u003eConsider the recursion relation:\u003c/p\u003e\u003cp\u003ex_n = (x_(n-1)*x_(n-2))^k\u003c/p\u003e\u003cp\u003eGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\u003c/p\u003e\u003cp\u003eFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eR = get_recurse(exp(1),pi,5,4/9)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eR =\r\n 2.31187497992966\r\n\u003c/pre\u003e","function_template":"function xn = get_recurse(x1,x2,n,k)\r\n xn = x1+x2+n+k;\r\nend","test_suite":"%%\r\nassert(abs(get_recurse(exp(1),pi,5,7/9)-20.066097534719034)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(1.01,1.02,5,pi)-4.1026063901404743)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(3.3,1,5,1/2)-1.5647419554132411)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(8,9,35,5/11)-1.3002291773509134)\u003c1000*eps)\r\n%%\r\nassert(abs(get_recurse(2,5,50,10/21)-1.3133198875358512)\u003c1000*eps)\r\n%%\r\nassert(abs(get_recurse(1.5,2,600,1/2)-1.8171205928321394)\u003c1000*eps)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":459,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":84,"test_suite_updated_at":"2012-02-09T03:52:52.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-09T01:44:32.000Z","updated_at":"2025-11-20T19:38:34.000Z","published_at":"2012-02-09T04:26:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the recursion relation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_n = (x_(n-1)*x_(n-2))^k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[R = get_recurse(exp(1),pi,5,4/9)\\n\\nR =\\n 2.31187497992966]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46591,"title":"Ackerman Function","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 111px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 55.5px; transform-origin: 407px 55.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 193.5px 8px; transform-origin: 193.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite the ackerman function, which conforms to 3 constraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eack(0, n) = n+1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eack(m, 0) = ack(m-1, 1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 105.5px 8px; transform-origin: 105.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"perspective-origin: 48.5px 8px; transform-origin: 48.5px 8px; \"\u003eack(m, n) = ack\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2.5px 8.5px; transform-origin: 2.5px 8.5px; \"\u003e(\u003c/span\u003e\u003cspan style=\"perspective-origin: 52px 8px; transform-origin: 52px 8px; \"\u003em-1, ack(m, n-1)\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2.5px 8.5px; transform-origin: 2.5px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function a = ack(m, n)\r\n a = 1;\r\nend","test_suite":"%%\r\nm = 1;\r\nn = 1;\r\na_correct = 3;\r\nassert(isequal(ack(m,n),a_correct))\r\n\r\n%%\r\nm = 3;\r\nn = 4;\r\na_correct = 125;\r\nassert(isequal(ack(m,n),a_correct))\r\n\r\n%%\r\nm = 3;\r\nn = 2;\r\na_correct = 29;\r\nassert(isequal(ack(m,n),a_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":515893,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-09-17T20:53:54.000Z","updated_at":"2020-09-17T20:53:54.000Z","published_at":"2020-09-17T20:53:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite the ackerman function, which conforms to 3 constraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(0, n) = n+1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(m, 0) = ack(m-1, 1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(m, n) = ack(m-1, ack(m, n-1))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60642,"title":"ICFP2024 010: Lambdaman Optimal-Crawler-Backfill","description":"The ICFP2024 contest was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\r\nThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\r\nThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\r\nShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\r\n\r\nThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\r\nMaze#/Crawler/OptimalCrawler \r\n1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\r\nThese are believed to be optimal solutions. Post in comments if any are beat.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 786px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 393px; transform-origin: 407px 393px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/task.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2024 contest\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 300px 8px; transform-origin: 300px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 317.5px 8px; transform-origin: 317.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.5px 8px; transform-origin: 357.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371px 8px; transform-origin: 371px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 420px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 210px; text-align: left; transform-origin: 384px 210px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: middle;width: 560px;height: 420px\" 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\" data-image-state=\"image-loaded\" width=\"560\" height=\"420\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 98.5px 8px; transform-origin: 98.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMaze#/Crawler/OptimalCrawler \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 313.5px 8px; transform-origin: 313.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 242.5px 8px; transform-origin: 242.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThese are believed to be optimal solutions. Post in comments if any are beat.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [pathbest]=crawler_fill(m)\r\n% This is Optimal crawler_fill\r\n% Optimal Crawler with backfill will solve non-loop mazes with path width of 1\r\n% At intersections the path, UDLR, that is least deep to fill is selected\r\n% Recursive fast move if only one cheese adjacent or one open path\r\n% Backfill L spot if 3 adj are Wall 0\r\n\r\n%crawler 1/15 2/33 4/394/.09s\r\n%11[103x103]/9988/.33s 12[101x101]/9992 13/9976 14/9994 15/9986/.33s\r\n%Optimal crawler 1/15 2/26 4/348 11/9622/.9s 12/9626 13/9562 14/9478 15/9584\r\n\r\n pathv=zeros(1000,1); pathvptr=0;\r\n %zmap=[0 0 0;1 0 0;0 1 0;0 0 1]; \r\n \r\n [nr,nc]=size(m);\r\n adj=[-1 1 -nr nr]; % UDLR 1234\r\n \r\n %figure(1);image(reshape(m+1,nr,nc));colormap(zmap);axis equal;axis tight\r\n %pause(0.05)\r\n \r\n Lidx=find(m==1);\r\n ztic=tic;\r\n while nnz(m==2)\u003e0\r\n %if toc(ztic)\u003e120\r\n % fprintf('ztic Timeout\\n');\r\n % break;\r\n %end\r\n \r\n vadj=m(adj+Lidx);\r\n m(Lidx)=3;\r\n \r\n [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr);\r\n \r\n if nnz(m==2)==0 %All cheezy bits eaten check post evolve\r\n break;\r\n end\r\n \r\n %Create path lengths in parallel to UDLR pu,pd,pl,pr\r\n %evolve all four until one active path has no growth. This is branch to take\r\n %dir will be called dptr 1 2 3 4\r\n mUDLR=m;\r\n mUDLR(m\u003e0)=inf;\r\n mUDLR(Lidx)=1;\r\n mU=mUDLR;\r\n \r\n % Use cell arrays mUDLRc{1} mU {2} mD {3}mL {4}mR \r\n mUDLRc{4}=[];\r\n Mdepth=zeros(1,4);\r\n depth=2;\r\n for i=1:4 % Initialize mUDLRc{i}\r\n mUDLRc{i}=mU; % all same start UDLR\r\n mUDLRc{i}(Lidx+adj(i))=min(mUDLRc{i}(Lidx+adj(i)),depth);\r\n if nnz(mUDLRc{i}==depth)\r\n Mdepth(i)=depth;\r\n end\r\n end\r\n \r\n % depth=2 at entry with at least 2 at depth 2\r\n nnzMdepth=nnz(Mdepth); % Base active paths\r\n while nnz(Mdepth==depth)==nnzMdepth % 0012 stop 1223 stop 0022 0022 stop\r\n pdepth=depth;\r\n depth=depth+1;\r\n for i=1:4\r\n if Mdepth(i)==0,continue;end % matrix i never grew\r\n gptr=find(mUDLRc{i}==pdepth)';\r\n for j=gptr\r\n mUDLRc{i}(j+adj)=min(mUDLRc{i}(j+adj),depth); % grow UDLR from each new point\r\n end % j gptr\r\n if nnz(mUDLRc{i}==depth) % Search for any new placements, cant use max as use Inf for path\r\n Mdepth(i)=depth;\r\n end\r\n end % i mUDLRc\r\n \r\n if nnz(Mdepth==depth)\u003cnnzMdepth % Some path group ended\r\n dptr=find(Mdepth==depth-1,1,'first'); %New direction\r\n end\r\n \r\n end % while nnz Mdepth depth\r\n \r\n Lidx=Lidx+adj(dptr);\r\n m(Lidx)=1;\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=dptr;\r\n \r\n end % while m==2\r\n \r\n UDLR='UDLR';\r\n if nnz(m==2)\u003e0\r\n pathbest=UDLR(pathv(1:pathvptr));\r\n fprintf('BestPath:');fprintf('%s',pathbest);fprintf(' Uneaten:%i\\n',nnz(m==2));fprintf('\\n')\r\n else\r\n pathbest=UDLR(pathv(1:pathvptr));\r\n fprintf('Solved Path:');fprintf('%s',pathbest);fprintf('\\nLength:%i\\n',length(pathbest));\r\n end\r\n \r\n %figure(4);image(reshape(m+1,nr,nc));colormap(zmap);axis equal;axis tight\r\nend % crawler_fill\r\n \r\nfunction [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr)\r\n vadj=m(adj+Lidx);\r\n update=0;\r\n while nnz(vadj==0)==3 % serial cul-de-sac exit; speed\r\n m(Lidx)=0; % cul-de-sac Backfill\r\n ptr=find(vadj\u003e0,1,'first');\r\n Lidx=Lidx+adj(ptr);\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=ptr;\r\n vadj=m(adj+Lidx);\r\n update=1;\r\n end % while cul-de-sac\r\n \r\n while nnz(vadj==2)==1 % serial tunnel cul-de-sac; speed\r\n m(Lidx)=3; % movement update\r\n ptr=find(vadj==2,1,'first');\r\n Lidx=Lidx+adj(ptr);\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=ptr;\r\n vadj=m(adj+Lidx);\r\n update=1;\r\n end % while cul-de-sac\r\n m(Lidx)=3;\r\n \r\n if update\r\n [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr); \r\n end\r\n \r\nend % evolve\r\n ","test_suite":"%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 1 optimal solution L15\r\n ms=['###.#...'\r\n '...L..##'\r\n '.#######'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=15 % Lambda1 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=15 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 2 optimal solution L26\r\n ms=[ ...\r\n 'L...#.'\r\n '#.#.#.'\r\n '##....'\r\n '...###'\r\n '.##..#'\r\n '....##'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=26 % Lambda2 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=26 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 4 optimal solution L348 DDLLRRUULLUUUUDDDDLLUULLUURRLLDDRRDDRRRRRRLLUUUURRRRRRDDRRRRUURRLLDDRRLLLLLLUURRLLLLLLLLDDRRRRDDDDLLRRDDLLDDLLUUDDRRDDRRLLDDLLDDDDUUUUUULLDDDDDDLLRRUULLLLUURRUUDDLLDDDDUURRRRUUUUUULLUUUUDDRRLLDDLLUUUUUUDDDDDDDDUURRRRDDRRDDRRDDDDUURRDDUURRDDUULLLLUUUUUUUURRUURRRRLLUURRRRRRLLDDRRDDDDDDDDLLRRUULLRRUUUULLLLRRDDLLLLUUDDLLRRDDRRDDLLLLRRRRDDDDRRRRLLUURR\r\n ms=[ ...\r\n'...#.#.........#...'\r\n'.###.#.#####.###.##'\r\n'...#.#.....#.......'\r\n'##.#.#.###.########'\r\n'.#....L..#.#.......'\r\n'.#####.###.#.###.##'\r\n'.#.#...#.......#...'\r\n'.#.#######.#######.'\r\n'.#...#.#...#.#.....'\r\n'.#.###.#.###.###.#.'\r\n'.....#...#.......#.'\r\n'.###.###.###.#####.'\r\n'.#.#...#...#...#...'\r\n'##.#.#.#.#####.###.'\r\n'...#.#...#.....#...'\r\n'.###.#.#.#####.####'\r\n'.....#.#.....#.#...'\r\n'.###.#.#.#.#.#.#.##'\r\n'.#...#.#.#.#.#.....'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=348 % Lambda4\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=348 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 11[103x103]/9988/.33s crawler\r\n%Lambdaman 11 optimal solution 9622\r\n ms=[ 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.#.......#.#...#...#.#.#.#...#...........#.#.#.#...#.......#'\r\n'#.#.###.#####.#.#######.###.#######.#.#.#.#####.###.#######.#.###.#####.#.###.#####.#######.#####.#.#'\r\n'#.#.....#.#.......#.........#.......#.#.#...#.........#.....#...#...#...#.#.#...#.........#...#...#.#'\r\n'###.###.#.#######.#.###.#######.#.#.#.###.#.#######.###.#.#.###.#.#########.#.#.###.#####.#####.###.#'\r\n'#...#...#...#...#...#...#.#.....#.#.#...#.#.........#...#.#...#.#...#.........#...#.#.#.#.#.....#.#.#'\r\n'###.#.###.###.#.###.#####.#######.#.#.###.###.#######.###########.#.#.###.#######.#.#.#.#.###.#.#.###'\r\n'#...#.........#.#...........#.....#.#.#.....#...#...............#.#...#.....#.....#...#.......#.....#'\r\n'#####################################################################################################'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9622 % Lambda11 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9622 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 12[101x101]/9994\r\n%Lambdaman 12 optimal solution 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##.#.###.####.........#...#.......#.......#.....#.....#.#.....#.....#.#.....#...#.....#.....#.....#...#...#.....######################################################################################################';\r\nms=reshape(s12,101,101)';\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9626 % Lambda12 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9626 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 13[101x101]/9994\r\n%Lambdaman 13 optimal solution 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%Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9562 % Lambda13 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9562 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 14[101x101]/9994\r\n%Lambdaman 14 optimal solution 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##.#.#.###.##.......#.#.........#.#...#.....#.........#...#.#...#...........#...#.....#...#...#...#.....#.....#.######################################################################################################';\r\nms=reshape(s14,101,101)';\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9478 % Lambda14 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9478 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 15[101x101]/9986/.33s\r\n%Lambdaman 15 optimal solution 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%Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9584 % Lambda15 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9584 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-07-17T17:24:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-07-17T16:18:16.000Z","updated_at":"2025-12-12T15:14:10.000Z","published_at":"2024-07-17T17:24:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/task.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2024 contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"420\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"560\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"middle\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMaze#/Crawler/OptimalCrawler \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese are believed to be optimal solutions. Post in comments if any are beat.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 81.7333px; transform-origin: 404px 81.7333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 284px 8.5px; tab-size: 4; transform-origin: 284px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e Generate \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 244px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 244px 8.5px; \"\u003ethe first k terms in the sequence a(n) define recursively by \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 116px 8.5px; transform-origin: 116px 8.5px; \"\u003e a(n+1)=p*a(n)+(1+a(n)) with \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 72px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 72px 8.5px; \"\u003ep=0.9 and a(1)=0.5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003eTest \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003eCase\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e n = 2;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 160px 8.5px; tab-size: 4; transform-origin: 160px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e a = [a(1) a(2)] = [0.5000 1.9500]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function out = rec_fun(x)\r\n p = 0.9;\r\n out(1) = 0.5;\r\nend","test_suite":"%%)\r\nx = 1;\r\ny_correct = 0.5;\r\nassert(abs(rec_fun(x) - y_correct) \u003c 1e-4)\r\n\r\n%%\r\nx = 5;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851];\r\nassert(sum(abs(rec_fun(x) - y_correct)) \u003c 1e-4)\r\n\r\n%%\r\nx = 7;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851 38.7816 74.6850];\r\nassert(sum(abs(rec_fun(x) - y_correct)) \u003c 1e-4)\r\n\r\n%%\r\nx = 9;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851 38.7816 74.6850 142.9016 272.5130];\r\nassert(all(abs(rec_fun(x) - y_correct) \u003c 1e-4))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":7,"created_by":44015,"edited_by":223089,"edited_at":"2023-03-07T10:35:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":45,"test_suite_updated_at":"2023-03-07T10:33:48.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-10-03T06:35:03.000Z","updated_at":"2025-12-12T06:32:21.000Z","published_at":"2015-10-03T06:36:26.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Generate the first k terms in the sequence a(n) define recursively by \\n\\n a(n+1)=p*a(n)+(1+a(n)) with p=0.9 and a(1)=0.5\\n\\nTest Case\\n\\n n = 2;\\n a = [a(1) a(2)] = [0.5000 1.9500]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2040,"title":"Additive persistence","description":"Inspired by Problem 2008 created by Ziko.\r\n\r\nIn mathematics, the persistence of a number is the *number of times* one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\r\n\r\nProblem 2008 is an example of multiplicative persistence.\r\nCan you code an additive persistence ?\r\n\r\n2718-\u003e2+7+1+8=18-\u003e1+8=9. So the persistence of 2718 is 2.\r\n\r\nYou can use the tips : num2str(666)-'0'=[6 6 6].\r\n\r\n","description_html":"\u003cp\u003eInspired by Problem 2008 created by Ziko.\u003c/p\u003e\u003cp\u003eIn mathematics, the persistence of a number is the \u003cb\u003enumber of times\u003c/b\u003e one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\u003c/p\u003e\u003cp\u003eProblem 2008 is an example of multiplicative persistence.\r\nCan you code an additive persistence ?\u003c/p\u003e\u003cp\u003e2718-\u0026gt;2+7+1+8=18-\u0026gt;1+8=9. So the persistence of 2718 is 2.\u003c/p\u003e\u003cp\u003eYou can use the tips : num2str(666)-'0'=[6 6 6].\u003c/p\u003e","function_template":"function y = add_persistence(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 0;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=18;\r\ny_correct = 1;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=2718;\r\ny_correct = 2;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=199;\r\ny_correct = 3;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=100;\r\ny_correct = 1;\r\nassert(isequal(add_persistence(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":184,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":38,"created_at":"2013-12-11T08:33:55.000Z","updated_at":"2026-03-31T17:48:57.000Z","published_at":"2013-12-11T08:33:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by Problem 2008 created by Ziko.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn mathematics, the persistence of a number is the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enumber of times\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2008 is an example of multiplicative persistence. Can you code an additive persistence ?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2718-\u0026gt;2+7+1+8=18-\u0026gt;1+8=9. So the persistence of 2718 is 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can use the tips : num2str(666)-'0'=[6 6 6].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44073,"title":"Fractal: area and perimeter of Koch snowflake","description":"Starting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration? \r\n\r\nFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\r\n\r\nRef: \u003chttps://en.wikipedia.org/wiki/Koch_snowflake\u003e","description_html":"\u003cp\u003eStarting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration?\u003c/p\u003e\u003cp\u003eFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\u003c/p\u003e\u003cp\u003eRef: \u003ca href = \"https://en.wikipedia.org/wiki/Koch_snowflake\"\u003ehttps://en.wikipedia.org/wiki/Koch_snowflake\u003c/a\u003e\u003c/p\u003e","function_template":"function [Area,Perimeter] = KochSnowFlake(side,nth)\r\n Area = 1;\r\n Perimeter = 1;\r\nend","test_suite":"%%\r\nside = 10; nth = 0;\r\nArea_correct = 43;\r\nPerimeter_correct = 30;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n\r\n%%\r\nside = 10; nth = 1;\r\nArea_correct = 58;\r\nPerimeter_correct = 40;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n\r\n%%\r\nside = 10; nth = 20;\r\nArea_correct = 69;\r\nPerimeter_correct = 9460;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":115733,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":49,"test_suite_updated_at":"2017-02-16T21:55:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-02-14T01:44:26.000Z","updated_at":"2026-01-07T20:59:16.000Z","published_at":"2017-02-14T01:44:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStarting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRef:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Koch_snowflake\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Koch_snowflake\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47385,"title":"Find Logic 28","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 251.571px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 174px 125.786px; transform-origin: 174px 125.786px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGuess the logic!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(1) = 21\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(2) = 25\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(3) = 22\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(4) = 38\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(5) = 33\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(6) = 69\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.9048px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 20.9524px; text-align: left; transform-origin: 151px 20.9524px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMake a function logic(x) which will written 'x' th term of sequence.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = logic(x)\r\n y = 21;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 21;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 22;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 4;\r\ny_correct = 38;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 33;\r\nassert(isequal(logic(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":293792,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":200,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-06T17:21:56.000Z","updated_at":"2026-02-19T09:52:52.000Z","published_at":"2020-11-06T17:21:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGuess the logic!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(1) = 21\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(2) = 25\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(3) = 22\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(4) = 38\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(5) = 33\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(6) = 69\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMake a function logic(x) which will written 'x' th term of sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":54390,"title":"That's not my hat! ","description":"There exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees n\u003e0, the function c computes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 126px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 63px; transform-origin: 407px 63px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThere exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u0026gt;0\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ec \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ecomputes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = c(n)\r\n y = 0;\r\nend","test_suite":"%%\r\nn = 7;\r\ny_correct = 1854;\r\nassert(isequal(c(n),y_correct))\r\n\r\n%%\r\nn = 10;\r\ny_correct = 1334961;\r\nassert(isequal(c(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct = 9;\r\nassert(isequal(c(n),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2242415,"edited_by":2242415,"edited_at":"2022-04-29T13:46:27.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2022-04-29T13:46:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-04-29T13:43:22.000Z","updated_at":"2025-12-02T19:48:02.000Z","published_at":"2022-04-29T13:46:28.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ec \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ecomputes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58946,"title":"Count block fountains","description":"A block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \r\nWrite a function to compute the number of block fountains with circles on the first row. For example, there are five block fountains with three circles on the first row. \r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 429.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 214.85px; transform-origin: 407px 214.85px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 364.85px 8px; transform-origin: 364.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 195.125px 8px; transform-origin: 195.125px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the number of block fountains with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.958px 8px; transform-origin: 176.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e circles on the first row. For example, there are five block fountains with three circles on the first row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 327.7px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 163.85px; text-align: left; transform-origin: 384px 163.85px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"543\" height=\"322\" style=\"vertical-align: baseline;width: 543px;height: 322px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = blockFountain(n)\r\n y = factorial(n);\r\nend","test_suite":"%%\r\nassert(isequal(blockFountain(3),5))\r\n\r\n%%\r\nassert(isequal(blockFountain(5),34))\r\n\r\n%%\r\nassert(isequal(blockFountain(8),610))\r\n\r\n%%\r\nassert(isequal(blockFountain(14),196418))\r\n\r\n%%\r\nassert(isequal(blockFountain(14),196418))\r\n\r\n%%\r\nassert(isequal(blockFountain(23),1134903170))\r\n\r\n%%\r\nassert(isequal(blockFountain(28),139583862445))\r\n\r\n%%\r\nassert(isequal(blockFountain(33),17167680177565))\r\n\r\n%%\r\nassert(isequal(blockFountain(35),117669030460994))\r\n\r\n%%\r\nfiletext = fileread('blockFountain.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'switch'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":46909,"edited_by":46909,"edited_at":"2023-09-03T17:54:36.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2023-09-03T17:54:19.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-09-02T14:47:44.000Z","updated_at":"2026-01-26T19:21:38.000Z","published_at":"2023-09-02T14:47:49.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the number of block fountains with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e circles on the first row. For example, there are five block fountains with three circles on the first row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"322\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"543\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56030,"title":"Pay up","description":"You live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by s. However, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list L contains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function pay takes as arguments L and s and returns boolean true if the exchange is possible and false otherwise. Complete the definition of pay.\r\nConstraints:\r\n(1) s ∈ Z\r\n(2) L ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ Z, i ∈ N } ∩ { ∅ }","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 216px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 108px; transform-origin: 407px 108px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYou live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003es. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHowever, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003econtains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e takes as arguments \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eand \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and returns boolean \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003etrue \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eif the exchange is possible and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efalse \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eotherwise. Complete the definition of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eConstraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(1)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e s\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eZ\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(2) \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eZ,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e i ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eN \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e} ∩ { ∅ }\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = pay(L,s)\r\n y = 0;\r\nend","test_suite":"%%\r\nL = [967, 605, -249, -255, 894, -199];\r\ns = -504;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-22];\r\ns = -467;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-637, -402];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [473, -498, -16, -288];\r\ns = 169;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-384, -715, 651, 874, -13];\r\ns = 759;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [186, -745];\r\ns = -166;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-186, -266, 914, 122, -289, 912];\r\ns = 359;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [437, -832, 572, -361, 877, 548];\r\ns = 548;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-803, 95, 503];\r\ns = 503;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [62, -867, -222];\r\ns = -805;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [394];\r\ns = -333;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-836, -353];\r\ns = 519;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [740, 952, -150];\r\ns = -765;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [137, -973, 620];\r\ns = 757;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [483, 772, 458];\r\ns = 109;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [212, 869, 10, -468, 523];\r\ns = -905;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-851];\r\ns = 822;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-667, -308, 549, 792, -128];\r\ns = 674;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -8;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [206, -246, -545, -402, 18, 150];\r\ns = -929;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-104, -710, -565, 448, -717];\r\ns = -669;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [672, -991, -467, 527, -111, -14];\r\ns = -65;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [384];\r\ns = -371;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-436, -662, -530, 279, -469, -415];\r\ns = 257;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [844, 288];\r\ns = 956;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-709, 570, 896, 293, -925];\r\ns = 403;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [11, -626, -258, -537, -961, -341];\r\ns = -864;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [710, -256, 63];\r\ns = -136;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-336, -238, 127, 193, 437, -220];\r\ns = 56;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-446, 274];\r\ns = -172;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [648, 931, 234, -218];\r\ns = 737;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [447, 109, 157, -252, -512, 134];\r\ns = -208;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-581, -916];\r\ns = -133;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [801, 621, -437, 580];\r\ns = -437;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [617, -424, -690, 9, -783];\r\ns = -64;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [864, -98, 179, -443, 505, 515];\r\ns = 922;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-48];\r\ns = -469;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [581, 372, -712, -154, 126, 80];\r\ns = 787;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [655, -167, 357, 289, -891];\r\ns = -701;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [941, -67, -764, -202, -919, -210];\r\ns = -809;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-361, -905, 437, 955, -40, 907];\r\ns = 915;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-506, -724];\r\ns = 157;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-432, -652, 860, -115, 579];\r\ns = -497;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 735;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-141, 334, -315, -866];\r\ns = -141;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [874, -980, -112];\r\ns = 246;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [768, 962, 245];\r\ns = 245;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [207, -281, 543, 462, -440, 995];\r\ns = 462;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 397;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [438, -548, -295, -270, 721, 750];\r\ns = -405;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [583, -617, -567];\r\ns = 307;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-590];\r\ns = -343;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-582];\r\ns = -526;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-634];\r\ns = -470;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-341, -761, -641];\r\ns = -341;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [929, -448, -53, 951, -932];\r\ns = -53;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [23, -338, -653, 107, -533, 802];\r\ns = 61;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-297, -331, -150, 481, -112, 895];\r\ns = -262;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-992, 401, -467];\r\ns = 339;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [592, 611, 660];\r\ns = 900;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-135, -544, -857];\r\ns = -523;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-259, -466, 976, 635, -607];\r\ns = 369;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-876, -852, -430, 370, -489, 468];\r\ns = -971;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-804, 96, -655, -253, 462];\r\ns = 209;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-418, 193, 353, 810, -780];\r\ns = 546;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-383, 116, 814, -359, 106];\r\ns = -359;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -327;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [317, -825, 299, 674];\r\ns = 991;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 977;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [873, -44, 505, 352, -794, -609];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 300;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-60, -817, 431, 411, -229, 625];\r\ns = -386;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-830];\r\ns = 786;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-341, 711, -155, 531, -317];\r\ns = -155;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -429;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 596;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [121, 915, 610, -114, -957, -594];\r\ns = 938;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [633, -465, -282, 413, -58];\r\ns = -110;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-946, -349, 290, 228, 565, 399];\r\ns = -331;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-104, 91, 452];\r\ns = 167;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [374, -547, -796, 603, 18];\r\ns = 448;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-503, 440, 604, 897, -390, 597];\r\ns = -33;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-762, -748, -55, -542, -927, -339];\r\ns = -799;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-129, -575, 221, 926, 927, -44];\r\ns = 798;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-669, 999, 316, 594, -40, 625];\r\ns = -75;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-857];\r\ns = 824;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [810, 680, 180];\r\ns = 680;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-554, 478];\r\ns = -926;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-562, 736, 114, 855];\r\ns = -562;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [478, -853, -694, -868, 666];\r\ns = -881;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-790, -131, -419, -669, 956];\r\ns = -673;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-19, 702, -57, -771];\r\ns = 728;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-188];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -469;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-491, 617, 718, 503, 452];\r\ns = -341;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [290, 697, -644, 345, 230, -494];\r\ns = -494;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [289];\r\ns = 650;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-426, -610, -85, 191, -544, -385];\r\ns = 618;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [833, 403, -426];\r\ns = -426;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":2242415,"edited_by":2242415,"edited_at":"2022-09-29T09:49:41.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2022-09-29T08:37:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-23T11:47:19.000Z","updated_at":"2022-09-29T09:49:41.000Z","published_at":"2022-09-23T11:50:25.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econtains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e takes as arguments \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and returns boolean \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eif the exchange is possible and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eotherwise. Complete the definition of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eConstraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e s\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eZ\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(2) \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eZ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e i ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e} ∩ { ∅ }\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56215,"title":"Calculate pi using the Mandelbrot Set.","description":"The Mandelbrot Set is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point remains bounded. The iterative equation is .\r\nFor any complex , we can continue this iteration until either diverges, meaning , or it converges such that . To visualize this set, all those values of in which converge are plotted in the complex plane. Thus having the following image:\r\n\r\nWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and . In 1991, Dave Boll was trying to convince himself that the single point (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form went through before scaping the set (meaning ), with being a small number. This same procedure works when approaching a small real number to the point (also called the Mandelbrot Set's Cusp). You will see that as decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that for or approaches the digits of .\r\nTo find out more information about this discovery, check out the following article: π IN THE MANDELBROT SET.\r\nIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input corresponding to the number of times we will decrement by 100, beginning with (no decrement). It outputs the number of times it will take for to be greater than two when iterating the recursive equation with with . For example:\r\nif x == 1\r\n epsilon = 1e-3;\r\nelseif x == 2\r\n epsilon = 1e-5;\r\n...\r\nend\r\nThen we will iterate the following:\r\nz = 0;\r\nz = z + 1/2 + epsilon;\r\nUntil it diverges or becomes greater than 2. Finally, we will output the number of times it took it.\r\nProblem based upon: Problem 81. Mandelbrot Numbers and Problem 785. Mandelbrot Number Test [Real+Imaginary].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1245.23px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 622.614px; transform-origin: 407px 622.614px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 44.0455px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 22.0227px; text-align: left; transform-origin: 384px 22.0227px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Mandelbrot_set\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot Set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"35\" height=\"18\" style=\"width: 35px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e remains bounded. The iterative equation is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-8px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"80\" height=\"22\" style=\"width: 80px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.0909px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5455px; text-align: left; transform-origin: 384px 31.5455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor any complex \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, we can continue this iteration until either \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADYAAAAnCAYAAABEz7vjAAAAAXNSR0IArs4c6QAAAr1JREFUaEPt10mojWEYB/DflShDMmWjTAusiLJA2YkFNhZEKFFSMhTJnMjCEGVayTzskGSjJFOGQrGRuaQoC0Om6Kn33I7bvfd85957zndx3uX3Pd/7Pv/n/3//z/PV+UdX3T+KSw3Y38ZsjbEaY+2kAjUpthMiMqdRYyxzqdpJYI2xdkJE5jT+O8aGYyu6NFOiYziauYRVDmyKsVmIxJta+7ASn6qcb+bjGgPWKbH1ECfxrWi3YHIF1uBt5lNyCGwMWF/MQbDypSinftiCHXicQ65lHZnVPLpiM87iSlkn5BScBVhHLMfrJM1fOeVaODaKPA9D0pUoVlV9aqWAxfuZ6I+d+JEjqM6YgvmYhINY1uC6ZAY2AdOwrkIO2AE98B0fSxQtlBOFHozTuNlSYNVwwN44jmvpDmcRxNDWAKuWA1YVWDkO2CdJdRRGYjVeYj2mYy824msTVFQNWOg4GvA23MJhHClxv6LvncBn7MLkJK/od7GiJ77JE1jBAQ+ge1EixeNTsBnWH5YfiYfVFjR/F++wPc2YAfY2VuXNWE+Mx2XEHVuIBeiFpdiTesepBHoGnmBqatz3U3yAGYeLWJTYi08KBRiRxSGasfNWmUfh7IHYhLD9GHovJbnFnBh3J/paTCRxt5Zgf3oW/SWKEuAfpM1CBQEq5F5Y8SzmzogJhovXB8S8+rPB8zYBFnuGRKNBT0Swegc3krzCPOLXJSQad+l5kuHuJOfFeN8MQ1Uzj4wqqQ8bjfM4hA2JrUFp9Iq5MnpUNzzK0zzKBRXx8d8W8puNc2mDwp2biwHJVV+0IbBhOIPrLZ08SgGN/7ZoC2PTPPksfRDGEUzdw9pm2IrwcqQYs+KYdFaYUhQrVHIVTxsmW2oILgWute9DpsH2K1xo7WbF3+cNrC2x/LFXDVjFSluhjWuMVaiwFdv2NxLXqih5/DKIAAAAAElFTkSuQmCC\" width=\"27\" height=\"19.5\" style=\"width: 27px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e diverges, meaning \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, or it converges such that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. To visualize this set, all those values of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e in which \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADYAAAAnCAYAAABEz7vjAAAAAXNSR0IArs4c6QAAAr1JREFUaEPt10mojWEYB/DflShDMmWjTAusiLJA2YkFNhZEKFFSMhTJnMjCEGVayTzskGSjJFOGQrGRuaQoC0Om6Kn33I7bvfd85957zndx3uX3Pd/7Pv/n/3//z/PV+UdX3T+KSw3Y38ZsjbEaY+2kAjUpthMiMqdRYyxzqdpJYI2xdkJE5jT+O8aGYyu6NFOiYziauYRVDmyKsVmIxJta+7ASn6qcb+bjGgPWKbH1ECfxrWi3YHIF1uBt5lNyCGwMWF/MQbDypSinftiCHXicQ65lHZnVPLpiM87iSlkn5BScBVhHLMfrJM1fOeVaODaKPA9D0pUoVlV9aqWAxfuZ6I+d+JEjqM6YgvmYhINY1uC6ZAY2AdOwrkIO2AE98B0fSxQtlBOFHozTuNlSYNVwwN44jmvpDmcRxNDWAKuWA1YVWDkO2CdJdRRGYjVeYj2mYy824msTVFQNWOg4GvA23MJhHClxv6LvncBn7MLkJK/od7GiJ77JE1jBAQ+ge1EixeNTsBnWH5YfiYfVFjR/F++wPc2YAfY2VuXNWE+Mx2XEHVuIBeiFpdiTesepBHoGnmBqatz3U3yAGYeLWJTYi08KBRiRxSGasfNWmUfh7IHYhLD9GHovJbnFnBh3J/paTCRxt5Zgf3oW/SWKEuAfpM1CBQEq5F5Y8SzmzogJhovXB8S8+rPB8zYBFnuGRKNBT0Swegc3krzCPOLXJSQad+l5kuHuJOfFeN8MQ1Uzj4wqqQ8bjfM4hA2JrUFp9Iq5MnpUNzzK0zzKBRXx8d8W8puNc2mDwp2biwHJVV+0IbBhOIPrLZ08SgGN/7ZoC2PTPPksfRDGEUzdw9pm2IrwcqQYs+KYdFaYUhQrVHIVTxsmW2oILgWute9DpsH2K1xo7WbF3+cNrC2x/LFXDVjFSluhjWuMVaiwFdv2NxLXqih5/DKIAAAAAElFTkSuQmCC\" width=\"27\" height=\"19.5\" style=\"width: 27px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e converge are plotted in the complex plane. Thus having the following image:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 542.455px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 271.227px; text-align: left; transform-origin: 384px 271.227px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"731\" height=\"536\" style=\"vertical-align: baseline;width: 731px;height: 536px\" 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alt=\"Mandelbrot Set\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168.091px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 84.0455px; text-align: left; transform-origin: 384px 84.0455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eπ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. In 1991, Dave Boll was trying to convince himself that the single point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"89.5\" height=\"18\" style=\"width: 89.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAALIAAAAkCAYAAAAttWA2AAAAAXNSR0IArs4c6QAABvlJREFUeF7tnHWoLVUUxn92N4oKFnYX2ImtqCh2d3cndoLdLbYoBioWFmIrFqKiGNhYqKgoFr/nmsfcuXPO2XPveYcz582G98fl7JnZe61v1l7r+9a8CWhGY4EBsMAEA7CHZguNBWiA3IBgICzQALn/3KhPFgK+BH7qv+X154oaIPefX+YCrgSOAN7pv+X154oaIPefXzYH1gUOBX7vv+X154oaIPeXXyYDzgWeAe7pr6X192oaII/MPxMCywO7A2sDpgOC727g5lHktgsC5wEHAh8nLk0fbhvpyAbAc4nX1XHaqsBlwHtho2+yTTRAru7OKYDDgaOAD4FvA8iC0PEQsA/wefVbj3kx5gVOBP5OvH5h4HZgSWCVAQeydjk17LIDcGsD5ESUFKb54h8U0fiEiJr/AkZoo6HRwuh8GnBKBTD6mGmAS4GbgCcSlzcDcBWwZcwfdCBvA1wNfATsBLzVADkRKYVpRt29gJOBXwq/TRQAPjai8o7AjxUes2yAf0/gq4TrJo6TYTlgcWD+LkfkyYHjAe9/HPBawpp6MWXqCBBDCuEmtahmeiPC68D7LS7bHrglcuU9KuTK+kGWYkrgDMAo32msDuwNXAFcMg5SC1OoC+IZfR/pGyB3gku13wWWHLA5tCBIAaRPMEUQkOcDLyc8ctYA7zlxMtzZADnBas2UJAtMG+CaLqLY2Io64eqVgYMB04pOap4UnVH7jSh2FgDGByCbUswHLAK8Hf/GmjYlIlvILAVsAcwZlbp/66jTgc8SHDXoU7TRdsBmwGHApxU2bG59EvBFFG7tLs2otpWAo4FfAfP2OgJ5KmDTKFQ/AATqHAFQf7PW+A1YC7De2BiYEXgT2LqY3nUCsjcWrFJCh0TC73G5DvAYcFdEn5SiJk+dVPDzkKmlmxjpzbp03cyRSuwHfAKcFWLGH4n3nz3SCguqTpK0VJv318nvxv3rCORlgIuDfZDG/BrGNLCtCVwP3FagID3tZGesUeTp9y8W2+2AbBUtneTYNWe4SeJoOzKA7c3lUzuNQQOyoNKgihFGivy4PHhmI2ansUlE8gMiwraarzMvAu4D7s9NqhuQV4wXV57dglgQZyNTNg0I1hjZkNIU3J5Ennj538bMaQVknXRDpBISz08WrCvI5fEeBh6tUNR0cmodf5807GRqYZ6bgVonXddhQ6mStOmHDpw+0pC/agpkKUIjqnmuqaqnenGYNjhMl7KxWiinUp6l6mUZkKVd1PuNEIZ/Q3/qMdlvQMzosNGsqwr1tBhwbQgmpUdgYSHzBFMhSNtJ0lJt+8aLUiwiRxORe3lKynsrEplC3Rj4SjmxNJnUpIzO04A+tcV1yCgDsui/I5Jvi5dUlWk0YBlX1/YayNrTSKz69HwUgO0KPyXpRYFjgD9bGCGj2lT97OcojroA2X1aU3naD5GXOzhfxdMU14LPlKLUVkUg+7dqjhJriiPGFQDrfN8lQhCx4h5WXec2ZmUuOM39Hm+zYV9G6TYr+39K5nmCyiLp8BeBn2POaNW4bgsiWVRttdXS3DcoNwOr6aw9LBZ9w0YRyHn0pxyNVQDXy2Osyrq6PTeLkBYz7WRqAe9RK9vRTpIe6alSJSUqs0E3gZy/1yNhl+8SDZ8xZNpICq5UKi8CeaYg2dcL5HezuXt8AbKR44HIlds1Dmlbo3KqJN3K76NJLdphqZtAHg2upBrPBNq+AEUgq0pZrFhRtovI8ss/dKCLEl+4gZqW9UzYIacNX22xu6qSdDsj1QHI7lc8bRT4kt0x9SoOG5VkZDJWJp8hyJ8bDG1XlXt/MH9xEciZyuQFEu62B+ZJeuevH4KIc1KrzkFB6wohflh02QP8fWFjdqFJW1ph+3urXgslaQUmO+lSxKS6AzmPq5eCe8+zNOJKjngXwPbYjJkRsPYcrxEqoIKYL4E9JkPYmzLWIuOQ/QJCZ3hjK29JaSkgq+uzS9oYBwWs7faRHXPOeQW4MMfqbBgshZHjqTYgzpwqgIcR+yMwYh0istvK4+qaqA8UQwTrzsAs0R7rSZ+N/N5MweShtfkLRTu1EkS8+W5xFBiFbF18Nmg536iy6nkEPqjdJYodRtKtosfBDcgU2LF2bxi4E+c+W9BJnmidJOkUA9UFyO5l7vhESSl66ZztZCXKcGWebn6szbWvX4fYLDVsdOq1SDFkM6eaBazCVQE7SdLV7tr92f3aWF+60wbI3QdAuztmkrQRfOz3Zr1dwmA+rQFyb/2aKkn3dlUD8LQGyL11ouKG38DVuX+ltxZLfFoD5ERDdWFaqiTdhUeNf7dogNw7n9s0Yw+LPcwpX0n3bmUD8KQGyL1zoqqptKb/S07qR6m9W13Nn9QAueYObJb/vwUaIDdIGAgL/AdleZg0nMGkZAAAAABJRU5ErkJggg==\" width=\"89\" height=\"18\" style=\"width: 89px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e went through before scaping the set (meaning \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e being a small number. This same procedure works when approaching a small real number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to the point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"79.5\" height=\"18\" style=\"width: 79.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (also called the Mandelbrot Set's Cusp). You will see that as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"89\" height=\"18\" style=\"width: 89px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"74.5\" height=\"18\" style=\"width: 74.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e approaches the digits of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eπ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo find out more information about this discovery, check out the following article: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://doi.org/10.1142/S0218348X01000828\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eπ IN THE MANDELBROT SET\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 86.0909px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 43.0455px; text-align: left; transform-origin: 384px 43.0455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e corresponding to the number of times we will decrement \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e by 100, beginning with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAF4AAAAkCAYAAAAAa43JAAAAAXNSR0IArs4c6QAAA81JREFUaEPt2WnobWMUx/HPRVwZ7zUUJVII5YYX5uINyRxChqvoUshUMmaK5AUlY+YMEZGSEO/IlBciJTKUMVOSMVPrWqd2x/+c/977PPec9H927Ren/TzPWvt71rOetX57kXrNhMCimVitRlXwMwqCCr6CnxGBGZmtEV/Bz4jAjMwulIhfDfvgRHyHtbEhnsAz+K0A/7CxJ1bgGrw3bs2FAH4prsauOANvJpBtcTs+xfn4uif8AfDzcATewjELHXxE9vU4MyPxLvzdALw/HscDuAA/dYS/I07AZzgEsV4Fj8MS6rs4Dh8NgV2CuzNSA+BDHcGvid9zzkm4v4JnHdyMk3EHzsUvQ2Aj1V6SqehJnILvO8IfDD8eD1bw7JRpZBtE/r1xBNBD8RR+xIF4uYLvSSCnDYDGz2Px6Ijl9sJL+ez03B19LBeP+NiykSuPxvtYF1vgbf9u54vwcx9PV/Gc8OvatLH3mEjeLv+UZbkrLmzk7S4uFgW/C27Ch3nqf8lKYW0/3IOHcRn+bOFhjLuqxbhxQ1pVDOnj5Yg7rrbgR50FbdwuBn4P3JZ17qkI6INrrSzTPh6TO4ednSb4KCMjp5/WEfxzCIDftiE9NKYI+DiQorbdAUfh+TkciSYhrlG5s4fvxaZEmXddVjJdIj5q+giyH3p4MjH4NXAlLsZ92Xx0bSx6+F18SnOH/S9STXRjj2H77Mq6NhXFCfZcMNJMSAJdIv7WlA9+7WFz4oiPRuOGMYbH1cQ9/F1lU9qWk7tnKl1vnnp/PkcnAt88lJ5NNe+b+Sy2fD7NwzVcapaJbRuoELlebPk+w8MmAr9R6hUHjGmze/q1suycVjkZPjYlg6hwRtXng3r/BYTe8kXPF5wIfIhGUc0chFDyzh7RHC3GH3n39HMq0wbqY0jBAebzIasb5HtG5XZO9ixN9XJ9bJ0S77DOUzTiV8+mI6LztTkUvWieQuwP4elSfDUVfP2NNGXhudTHwR8T0R6HcVOT3zKrun1xZ5am46q7iSI+XjGqmXuxWxqM0jKap82xHJviivyS0x/J9GZukjV9VGtRo7+TpgPsLSkPREHxyZBLzUM3dkzoPR+McDtK8PjqFJp+pKrD8fq4Vxz1BWornJXSwM54NRd6JHfCX9PjVsRSdNohc0RUhq4UaWOzVC+fnkMuHpwRATM+F8bujqiP9Nq8NsaRODjvwbM3cu1Xktt/Pi0uhE9/Rf650otU8KWJtlyvgm8JqvSwCr400ZbrVfAtQZUeVsGXJtpyvQq+JajSwyr40kRbrlfBtwRVetg/S0buJZCLRe0AAAAASUVORK5CYII=\" width=\"47\" height=\"18\" style=\"width: 47px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; \"\u003e(no decrement)\u003c/span\u003e\u003cspan style=\"\"\u003e. It outputs the number of times \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e it will take for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEoAAAAnCAYAAACout71AAAAAXNSR0IArs4c6QAAAyNJREFUaEPt2kuoVVUcx/GPEgVahKk4kZ6DbJQkNNCgWdQgnTQwChOiQIooA8VK7UHSoJQkKwcRPbRsViHSRAjphRakoAhiakEEBg56YCbFL9aJw6Vzzzp2zzn3XPYa7vPf66z13b//a+09TTOqCEyrsmqMdAP1cGH0yhRmdSU24Qmc6rTPbqDWlxufm8KgrsdWPIKjDajOT7oBVekFDagGVCWBSrNGUQ2oSgKVZo2iGlCVBCrNGkU1oCoJVJoNRFE3lD5pxjiLehfvVC56GGYDAXUPAqLTeBVr8OswCFT+Z99BXVzUdAjv4Y+2hUVpj+NJ/FS54GGZ9R3UXKxAVPN72y7n4Xm8hCPD2n0P/9t3UP+1lpnIkcyH+LSHxQ7TdOCgLsJq/FBc8a9h7h55aCtxXQkB7apvX9pAQeUA8G7Mx2b8OURIl+BO3I/bsR2PjQkPQwN1K5YhJ6L9yHDTcTnO4ZcuDyHKzoO7Frvw5WQBNYgMNxs78FmJgTWCjUtNGlCDynAjDaqXDDenuOZNWIh15Y3HBtyFbXgaZztIZWRBJQ6koHwBX+EtvN0lPqXu2onfsAV3FHdKvZWRmuzHqQSqleFex2VtG2tvV6K2lAopEQIiqbkVM77GabyI9IiBtx9rp5qiZuEW7EVi1IN4AFfg0fKOLLXL+wXichzD0lKIflvsA2cJ9mBVUVduaQG9sSZij5P+J1Uwb+3lajyDlAlpgj8p7pU+L7EndVUq9sSmvGh8rVxLfRPIgXmwTBaVBlLcuzVyLX1jbKLA9nEG6TfPj7k+KUFljXHJFJy3Iao7gC+KOyWY56glLplYdKK43cvFfR/Cz+MoaGSDeaVX/Gu2CB/jTWwsarqmtDrpC1MjXYrDoxrM85FGAvEbvZIZY59zq7jbvfio/NaKWffhqpI1T04gqAX4AJ93qczzkcazJSx0yrpdv2b5n3z+uT3nVikjFpd+8LsyaQJ5lPQNnhpHTTHvxfXS691c/itJIvCj4n04fqEb6vY1y4XOO9H3xS2jxu+xe6Inr5lvVEDV7KWvNg2oSrwNqAZUJYFKs0ZRlaD+Bnx4BTfcnlnyAAAAAElFTkSuQmCC\" width=\"37\" height=\"19.5\" style=\"width: 37px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to be greater than two when iterating the recursive equation \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-8px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"80\" height=\"22\" style=\"width: 80px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"40\" height=\"19.5\" style=\"width: 40px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"74.5\" height=\"18\" style=\"width: 74.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.591px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.2955px; transform-origin: 404px 61.2955px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eif \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex == 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e epsilon = 1e-3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eelseif \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex == 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e epsilon = 1e-5;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eend\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThen we will iterate the following:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8636px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4318px; transform-origin: 404px 20.4318px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ez = 0;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ez = z + 1/2 + epsilon;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUntil it diverges or becomes greater than 2. Finally, we will output the number of times \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e it took it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eProblem based upon: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://la.mathworks.com/matlabcentral/cody/problems/81-mandelbrot-numbers\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 81. Mandelbrot Numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://la.mathworks.com/matlabcentral/cody/problems/785\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 785. Mandelbrot Number Test [Real+Imaginary]\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = piInMandelbrot(x)\r\n n = pi;\r\n % Is it really equal to pi?\r\nend","test_suite":"%%\r\nx = 0;\r\nassert(isequal(piInMandelbrot(x),2))\r\n\r\n%%\r\nx = 1;\r\nassert(isequal(piInMandelbrot(x),30))\r\n\r\n%%\r\nx = 2;\r\nassert(isequal(piInMandelbrot(x),312))\r\n\r\n%%\r\nx = 4;\r\nassert(isequal(piInMandelbrot(x),31414))\r\n\r\n%%\r\nx = 6;\r\nassert(isequal(piInMandelbrot(x),3141625))","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":2620230,"edited_by":2620230,"edited_at":"2022-10-10T05:30:25.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-10-04T21:53:11.000Z","updated_at":"2022-10-10T05:30:25.000Z","published_at":"2022-10-10T05:30:25.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mandelbrot_set\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot Set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e remains bounded. The iterative equation is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1} = z_n^2 +c\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor any complex \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, we can continue this iteration until either \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e diverges, meaning \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \u0026gt; 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, or it converges such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \\\\leq 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. To visualize this set, all those values of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e in which \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e converge are plotted in the complex plane. Thus having the following image:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"536\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"731\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Mandelbrot Set\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. In 1991, Dave Boll was trying to convince himself that the single point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = -3/4 + 0i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = -3/4 + \\\\epsilon{i}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e went through before scaping the set (meaning \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \u0026gt; 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e being a small number. This same procedure works when approaching a small real number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to the point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + 0i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (also called the Mandelbrot Set's Cusp). You will see that as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}|\u0026gt;2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec=-3/4 + \\\\epsilon{i}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + \\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e approaches the digits of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo find out more information about this discovery, check out the following article: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://doi.org/10.1142/S0218348X01000828\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eπ IN THE MANDELBROT SET\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e corresponding to the number of times we will decrement \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e by 100, beginning with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon = 0.1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e(no decrement). It outputs the number of times \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e it will take for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}|\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to be greater than two when iterating the recursive equation \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1} = z_n^2 +c\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_0 = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + \\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[if x == 1\\n epsilon = 1e-3;\\nelseif x == 2\\n epsilon = 1e-5;\\n...\\nend]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen we will iterate the following:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[z = 0;\\nz = z + 1/2 + epsilon;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUntil it diverges or becomes greater than 2. Finally, we will output the number of times \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e it took it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem based upon: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://la.mathworks.com/matlabcentral/cody/problems/81-mandelbrot-numbers\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 81. Mandelbrot Numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://la.mathworks.com/matlabcentral/cody/problems/785\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 785. Mandelbrot Number Test 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Sequences 93: Recursive Polynomial Function","description":"For a natural number and a polynomial function , we define a recursive function , as follows:\r\n , and\r\n , for .\r\nFor example, if (or in Matlab array form, P = [1 2 1]):\r\n \r\n \r\n \r\n \r\n ...\r\n \r\n and so on...\r\nWe can see that can grow very quickly. Therefore, we will instead calculate: , that is, is equal to the logarithm (base-) of the absolute value of .\r\nGiven a polynomial array , and an integer , find the value of , rounded-off to 4 decimal places.\r\n----------------------\r\nNOTE: To encourage vectorization , FOR and WHILE loops are disabled. If you know the math, this problem can be solved in less than 15 lines of code. However, solutions up to 50 lines of code will still be accepted. The semicolon (;), shall be considered as an end-of-line character. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 505px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 252.5px; transform-origin: 407px 252.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69px 8px; transform-origin: 69px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a natural number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.5px 8px; transform-origin: 84.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and a polynomial function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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style=\"width: 31.5px; height: 18.5px;\" width=\"31.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100px 8px; transform-origin: 100px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, we define a recursive function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36.5px 8px; transform-origin: 36.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 79px; height: 18.5px;\" width=\"79\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 167.5px; height: 18.5px;\" width=\"167.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16.5px 8px; transform-origin: 16.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36.5px; height: 18px;\" width=\"36.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.75px; text-align: left; transform-origin: 384px 10.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48.5px 8px; transform-origin: 48.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 118.5px; height: 19.5px;\" width=\"118.5\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (or in Matlab array form, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 44px 8px; transform-origin: 44px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003eP = [1 2 1]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e):\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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style=\"width: 326px; height: 19.5px;\" width=\"326\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38px 8px; transform-origin: 38px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 118px; height: 18.5px;\" width=\"118\" height=\"18.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69.5px 8px; transform-origin: 69.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and so on...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 53.5px 8px; transform-origin: 53.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWe can see that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 185px 8px; transform-origin: 185px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e can grow very quickly. Therefore, we will instead calculate:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 108.5px; height: 18.5px;\" width=\"108.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, that is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26.5px 8px; transform-origin: 26.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis equal to the logarithm (base-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ee\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.5px 8px; transform-origin: 80.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) of the absolute value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 88px 8px; transform-origin: 88px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGiven a polynomial array \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eP\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57px 8px; transform-origin: 57px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, and an integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63.5px 8px; transform-origin: 63.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, find the value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117px 8px; transform-origin: 117px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, rounded-off to 4 decimal places.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55px 8px; transform-origin: 55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e----------------------\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23px 8px; transform-origin: 23px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNOTE: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91px 8px; transform-origin: 91px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo encourage vectorization , \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 121px 8px; transform-origin: 121px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFOR and WHILE loops are disabled\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 53px 8px; transform-origin: 53px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. If you know the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.cs.mtsu.edu/~xyang/3080/recurrenceRelations.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"font-weight: 700; text-decoration-line: underline; \"\u003emath\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 68px 8px; transform-origin: 68px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, this problem can be solved in less than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e15\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 76.5px 8px; transform-origin: 76.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e lines of code. However, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54.5px 8px; transform-origin: 54.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esolutions up to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; text-decoration-line: underline; \"\u003e50\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 121px 8px; transform-origin: 121px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e lines of code will still be accepted\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52px 8px; transform-origin: 52px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The semicolon (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; font-weight: 700; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 159px 8px; transform-origin: 159px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), shall be considered as an end-of-line character. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function t = T(P,x)\r\n t = round(log(abs(R(x))),4);\r\n function r = R(x)\r\n if x == 0\r\n r = polyval(P,x);\r\n else\r\n r = x * R(x-1) + polyval(P,x);\r\n end\r\n end\r\nend","test_suite":"%%\r\nP = [1 2 1]; x = 0:10;\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [0.0000 1.6094 2.9444 4.2905 5.7589 7.3908 9.1876 11.1344 13.2140 15.4113 17.7139];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [1 0 1]; x = [10 100 1000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [17.2030 365.8380 5914.2268];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [-1 1 -1 1]; x = [10 100 1000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [17.2030 365.8380 5914.2268];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [1 2 3 4 5]; x = [10 100 1000 10000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [19.7933 368.4283 5916.8171 82113.6167];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [11 -22 33 -44 55 -66]; x = 12345;\r\nt = T(P,x);\r\nt_correct = 103969.6836;\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = 2:2:10; x = P.*200;\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [2005.8827 4557.3328 7317.9383 10214.4041 13211.9064];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = 2:2:100; x = P./2;\r\nt = arrayfun(@(i) T(P,i),x);\r\ns = round([mean(t) median(t) mode(t) std(t)],4);\r\ns_correct = [163.8877 167.4357 7.8823 59.1051];\r\nassert(isequal(s,s_correct))\r\n%%\r\nfiletext = fileread('T.m');\r\nnot_allowed = contains(filetext, 'for') || contains(filetext, 'while') || contains(filetext, 'java') || contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'regexp') || contains(filetext, 'eval') || contains(filetext, 'assignin');\r\nassert(~not_allowed)\r\nassert(count(filetext, 'function')==1)\r\n\r\nc = 0;\r\nfor s = deblank(strtrim(splitlines(filetext)))'\r\n if ~isempty(s{1}) \u0026\u0026 ~isequal(s{1}(1),'%')\r\n c = c + numel(find(s{1}==';'));\r\n if ~isequal(s{1}(end),';')\r\n c = c + 1;\r\n end\r\n end\r\nend\r\nassert(c\u003c=50)","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":255988,"edited_by":255988,"edited_at":"2023-02-14T06:56:22.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2023-02-07T04:59:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-02-03T04:56:19.000Z","updated_at":"2025-11-26T02:41:15.000Z","published_at":"2023-02-06T08:01:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a natural number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and a polynomial function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, we define a recursive function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(0)=P(0)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x) = x\\\\cdot R(x-1) + P(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP(x) = x^2+2x+1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (or in Matlab array form, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP = [1 2 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(0) = P(0)=0^2+2\\\\cdot 0 + 1=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(1) = 1\\\\cdot R(0)+P(1)=1\\\\cdot 1+1^2+2\\\\cdot 1 + 1=5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(2) = 2\\\\cdot R(1)+P(2)=2\\\\cdot 5+2^2+2\\\\cdot 2 + 1=19\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(3) = 3\\\\cdot R(2)+P(3)=3\\\\cdot 19+3^2+2\\\\cdot 3 + 1=73\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e ...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(10) = 49320491\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e and so on...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe can see that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e can grow very quickly. Therefore, we will instead calculate:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x) = \\\\text{log}|R(x)|\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis equal to the logarithm (base-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) of the absolute value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGiven a polynomial array \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and an integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, find the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, rounded-off to 4 decimal places.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e----------------------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eTo encourage vectorization , \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFOR and WHILE loops are disabled\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. If you know the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.cs.mtsu.edu/~xyang/3080/recurrenceRelations.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emath\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, this problem can be solved in less than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e15\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e lines of code. However, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esolutions up to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e50\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e lines of code will still be accepted\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The semicolon (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), shall be considered as an end-of-line character. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":734,"title":"Ackermann's Function","description":"Ackermann's Function is a recursive function that is not 'primitive recursive.'\r\n\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Ackermann_function Ackermann Function\u003e\r\n\r\nThe first argument drives the value extremely fast.\r\n\r\nA(m, n) =\r\n\r\n* n + 1 if m = 0\r\n* A(m − 1, 1) if m \u003e 0 and n = 0\r\n* A(m − 1,A(m, n − 1)) if m \u003e 0 and n \u003e 0\r\n\r\n \r\nA(2,4)=A(1,A(2,3)) = ... = 11.\r\n\r\n % Range of cases\r\n % m=0 n=0:1024\r\n % m=1 n=0:1024\r\n % m=2 n=0:128\r\n % m=3 n=0:6\r\n % m=4 n=0:1\r\n\r\nThere is some deep recusion.\r\n\r\n*\r\nInput:* m,n\r\n\r\n*Out:* Ackerman value\r\n\r\n\r\nAckermann(2,4) = 11\r\n\r\nPractical application of Ackermann's function is determining compiler recursion performance.","description_html":"\u003cp\u003eAckermann's Function is a recursive function that is not 'primitive recursive.'\u003c/p\u003e\u003cp\u003e\u003ca href=\"http://en.wikipedia.org/wiki/Ackermann_function\"\u003eAckermann Function\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe first argument drives the value extremely fast.\u003c/p\u003e\u003cp\u003eA(m, n) =\u003c/p\u003e\u003cul\u003e\u003cli\u003en + 1 if m = 0\u003c/li\u003e\u003cli\u003eA(m − 1, 1) if m \u003e 0 and n = 0\u003c/li\u003e\u003cli\u003eA(m − 1,A(m, n − 1)) if m \u003e 0 and n \u003e 0\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eA(2,4)=A(1,A(2,3)) = ... = 11.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Range of cases\r\n% m=0 n=0:1024\r\n% m=1 n=0:1024\r\n% m=2 n=0:128\r\n% m=3 n=0:6\r\n% m=4 n=0:1\r\n\u003c/pre\u003e\u003cp\u003eThere is some deep recusion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,n\u003c/p\u003e\u003cp\u003e\u003cb\u003eOut:\u003c/b\u003e Ackerman value\u003c/p\u003e\u003cp\u003eAckermann(2,4) = 11\u003c/p\u003e\u003cp\u003ePractical application of Ackermann's function is determining compiler recursion performance.\u003c/p\u003e","function_template":"function vAck = ackermann(m,n)\r\n set(0,'RecursionLimit',512);\r\n\r\n vAck=0;\r\n\r\nend","test_suite":"%%\r\nm=0;\r\nn=1;\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=0;\r\nn=1024;\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=0;\r\nn=randi(1024)\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=1;\r\nn=1024\r\nAck = n+2;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=1;\r\nn=randi(1024)\r\nAck = n+2;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=2;\r\nn=randi(128)\r\nAck = 2*n+3;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=3;\r\nn=6;\r\nAck = 509;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=3;\r\nn=randi(6)\r\nAck = 2^(n+3)-3;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=4;\r\nn=0;\r\nAck = 13;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=4;\r\nn=1; % Fails at RecursionLimit 1030; Create Special\r\nAck = 65533;\r\nassert(isequal(ackermann(m,n),Ack))","published":true,"deleted":false,"likes_count":4,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":25,"created_at":"2012-06-02T02:10:19.000Z","updated_at":"2026-02-15T03:26:49.000Z","published_at":"2012-06-02T02:36:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann's Function is a recursive function that is not 'primitive recursive.'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Ackermann_function\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann Function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first argument drives the value extremely fast.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m, n) =\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en + 1 if m = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m − 1, 1) if m \u0026gt; 0 and n = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m − 1,A(m, n − 1)) if m \u0026gt; 0 and n \u0026gt; 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(2,4)=A(1,A(2,3)) = ... = 11.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Range of cases\\n% m=0 n=0:1024\\n% m=1 n=0:1024\\n% m=2 n=0:128\\n% m=3 n=0:6\\n% m=4 n=0:1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere is some deep recusion.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOut:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Ackerman value\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann(2,4) = 11\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePractical application of Ackermann's function is determining compiler recursion performance.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":43963,"title":"Finding operators in a MATLAB function in a string.","description":"The aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\r\n\r\nFor example:\r\n\r\n 'min(var1+var2,2)' =\u003e true\r\n\r\n 'min(1,2)+min(3,4)' =\u003e false\r\n\r\n 'min(min(1,2),3))' =\u003e false\r\n\r\n 'min(min(1,var1+2),3))' =\u003e true\r\n\r\n '4*var1' =\u003e false, there is no MATLAB function\r\n\r\nYou can assume that all opening brackets are closed.","description_html":"\u003cp\u003eThe aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\u003c/p\u003e\u003cp\u003eFor example:\u003c/p\u003e\u003cpre\u003e 'min(var1+var2,2)' =\u0026gt; true\u003c/pre\u003e\u003cpre\u003e 'min(1,2)+min(3,4)' =\u0026gt; false\u003c/pre\u003e\u003cpre\u003e 'min(min(1,2),3))' =\u0026gt; false\u003c/pre\u003e\u003cpre\u003e 'min(min(1,var1+2),3))' =\u0026gt; true\u003c/pre\u003e\u003cpre\u003e '4*var1' =\u0026gt; false, there is no MATLAB function\u003c/pre\u003e\u003cp\u003eYou can assume that all opening brackets are closed.\u003c/p\u003e","function_template":"function y = FindingOperators( x )\r\n y = x;\r\nend","test_suite":"%%\r\nx = 'min(var1+var2,2)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(1,2)+min(3,4)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(1,2),3))';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(1,var1+2),3))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n\r\n%%\r\n\r\nx = '4*var1';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4*(var1+2)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n\r\n%%\r\n\r\nx = 'max(min(1,var1-2),3))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2/var1)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2+min(1+5,1))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+max(2,min(1+5,1))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+max((2,min(1+5,1)))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(2-1,1)+abs(-5)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(var1,1)+abs(-5)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(var1,var2),1+3)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":55046,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-12-22T09:44:46.000Z","updated_at":"2016-12-22T15:57:10.000Z","published_at":"2016-12-22T09:44:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 'min(var1+var2,2)' =\u003e true\\n\\n 'min(1,2)+min(3,4)' =\u003e false\\n\\n 'min(min(1,2),3))' =\u003e false\\n\\n 'min(min(1,var1+2),3))' =\u003e true\\n\\n '4*var1' =\u003e false, there is no MATLAB function]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume that all opening brackets are closed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); \r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); \r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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Hole-In-Wall: Solve Problem 47, Score=0, Figure Vertices 11, Hole Vertices 10","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \r\nThis Challenge is to solve ICFP problems 47 according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u003c= epsilon/1000000. Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon) \r\nThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\r\nThe function template includes routines to read ICFP problem files and to write ICFP solution files.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 775px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 387.5px; transform-origin: 407px 387.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8.05px; transform-origin: 14px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 146.65px 8.05px; transform-origin: 146.65px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.95px 8.05px; transform-origin: 29.95px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 8.05px; transform-origin: 1.95px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 283px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 141.5px; text-align: left; transform-origin: 384px 141.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.9px 8.05px; transform-origin: 379.9px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 541px;height: 262px\" 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\" data-image-state=\"image-loaded\" width=\"541\" height=\"262\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 191.767px 8.05px; transform-origin: 191.767px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 47 according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 138.367px 8.05px; transform-origin: 138.367px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8.05px; transform-origin: 384px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u0026lt;= epsilon/1000000. Lsqr is length squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250.1px 8.05px; transform-origin: 250.1px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 152.783px 8.05px; transform-origin: 152.783px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 8.05px; transform-origin: 3.88333px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8.05px; transform-origin: 384px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 303.4px 8.05px; transform-origin: 303.4px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files and to write ICFP solution files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.883px 8.05px; transform-origin: 375.883px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42.7833px 8.05px; transform-origin: 42.7833px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems Page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.35px 8.05px; transform-origin: 256.35px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP047(hxy,pxy,mseg,epsilon)\r\n%Problem 47 shows potential for recursion but a brute force with reduction can quickly solve\r\n% nH equals nP-1 and Score=0 optimally, nH is before repeating row 1\r\n% Since Score=0 then all hole vertices are covered. \r\n% Know that only 1 figure vertex not on a hole vertex\r\n% Assume that the longest segment spans two hole vertices not necessarily sequential hole nodes\r\n% Identify longest segment and associated hole vertices\r\n% Try all permutations of nchoosek(1:nP,nP-1) after reduced by Long segment nodes and hole nodes \r\n% Verify segments where both nodes are in nck set are correct length\r\n% For unselected vertex find all segments containing and create valid pt sets\r\n% for each segment constraint. Find point common to all constraint sets\r\n\r\n npxy=pxy;\r\n nseg=size(mseg,1);\r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %hplot4(hxy,npxy,mseg,nseg,4,msegMM);\r\n \r\n hxy1=hxy(1:end-1,:);\r\n np=size(npxy,1); %\r\n vpn=zeros(np,1);\r\n pnchk=nchoosek(1:np,np-1);\r\n \r\n % Note: *** Indicates line was changed from working program\r\n ptrLseg=find(msegMM(:,2)==0,1,'first'); % *** Find max L segment\r\n nodesL=mseg(ptrLseg,:); % figure nodes of longest figure segment\r\n nodesLMM=msegMM(ptrLseg,:); % Min and Max of selected long figure segment\r\n found=0;\r\n nh=size(hxy,1);\r\n for hi=1:nh-2 % search all hole vertices hi to hj that matches long figure segment\r\n for hj=hi+1:nh-1\r\n if prod([0 0])\u003c=0 % *** Find pair of valid hole vertices\r\n found=1;\r\n break;\r\n end\r\n end %hj nh\r\n if found,break;end\r\n end %hi nh\r\n % hi,hj Hole indices that are nodes that fit longest segment\r\n % that will need to be either of nodesL\r\n \r\n % remove nchoosek vectors that omit the long segment of nodesL\r\n pnchkval=sum([0 0],2)\u003e1; % ***\r\n pnchk=pnchk(pnchkval,:);\r\n Lpnchk=size(pnchk,1); % Length of final nchoosek matrix\r\n \r\n mperms=perms(1:np-1); % fast repetitve perms method, create a mapping array\r\n for ipnchk=1:Lpnchk %subset of figure vertices to place onto hole vertices\r\n vpnchk=pnchk(ipnchk,:);\r\n phset=vpnchk(mperms); \r\n % remove matrix rows that lack nodesL in hi,hj columns\r\n % Massive reduction in phset matrix \r\n permvalid=phset(:,hi)==0 | phset(:,hi)==0; % *** match nodesL\r\n phset=phset(permvalid,:);\r\n permvalid=phset(:,hj)==0 | phset(:,hj)==0; % *** match nodesL\r\n phset=phset(permvalid,:); % Final reduced permutation set that must have nodesL in cols hi,hj\r\n \r\n nphset=size(phset,1); % greatly reduced from 10! for each nchoosek vector\r\n \r\n for i=1:nphset\r\n npxy=npxy*0;\r\n vphset=phset(i,:); \r\n npxy(vphset,:)=hxy1; % load hole vertices into figure vertex matrix, one row is [0 0] unset\r\n vpn=0*vpn;\r\n vpn(vphset)=1; % vpn is vector that indicates used figure vertices\r\n fail=0;\r\n for segptr=1:nseg\r\n if prod(vpn(mseg(segptr,:)))\r\n L2seg=sum((npxy(mseg(segptr,1),:)-npxy(mseg(segptr,2),:)).^2);\r\n if prod([0 0])\u003e0 % *** Verify L2seg is valid length squared\r\n fail=1;\r\n break;\r\n end\r\n end\r\n end\r\n if fail,continue;end %length of subset placed vertices placed on hole ver failed\r\n %Hole Points covered. Have 1 free point to place constrained by its segments\r\n node=find(vpn==0); % Free node to place\r\n \r\n cptr=1;\r\n for fseg=1:nseg\r\n if prod(vpn(mseg(fseg,:))),continue;end % Both seg vertices placed\r\n MM=msegMM(fseg,:); % Create [Min Max] vector\r\n Node2=mseg(fseg,:);\r\n Node2(Node2==node)=[]; % Reduce Node2 to a single value of the set vertex\r\n \r\n if cptr==1 % create an initial list of all in range and then inpolygon\r\n Lmm=ceil(MM(2)^.5);\r\n dmap=(0:Lmm).^2;\r\n dmap=repmat(dmap,Lmm+1,1);\r\n dmap=dmap+dmap'; % Create a 2D map of distance squared from [0,0]. dmap(1,1) is [0,0]\r\n % This 2D map is of the Positive XY quadrant. The goal will be to find all valid [dx dy]\r\n dmap(dmap\u003cMM(1))=0; % Remove Points less than Min Seg length\r\n dmap(1,:)=0; % *** Remove Points greater than Max Seg length\r\n [dx,dy]=find(dmap);\r\n dx=dx-1; dy=dy-1; % remove 1,1 offset from grid\r\n dxy=[dx dy;dx -dy;-dx dy;-dx -dy];% Create all valid deltas by symmetry about [0,0]\r\n mxy=dxy+npxy(Node2,:);% Create matrix of all points in the valid region\r\n % remove negatives from hole comparison as hole is all positive\r\n mxy=mxy(mxy(:,1)\u003e=0,:); % Speed option remove all points with neg x values\r\n mxy=mxy(1,:); % *** Speed option remove all points with neg y values\r\n in=inpolygon(mxy(:,1),mxy(:,2),hxy(:,1),hxy(:,2));\r\n mxy=mxy(in,:); % reduce to in-hole points\r\n cptr=2;\r\n else % test points from m for additional new fseg constraint and prune\r\n Lmxy=size(mxy,1);\r\n vmxy=ones(Lmxy,1); % Valid mxy vector\r\n for ptrmxy=1:Lmxy\r\n d2=sum((mxy(ptrmxy,:)-npxy(Node2,:)).^2); % Calc dist squared from mxy to Node2\r\n if d2\u003cMM(1),vmxy(ptrmxy)=0;end % clear vmxy for too short\r\n if d2\u003eMM(2),vmxy(ptrmxy)=0;end % clear vmxy for too long\r\n end\r\n mxy=mxy(vmxy\u003e0,:);\r\n if isempty(mxy) %If no points left in mxy then vertex could not reach from set nodes\r\n fail=1;\r\n break;\r\n end\r\n end % cptr==1\r\n end % fseg 1:nseg\r\n if fail,continue;end\r\n \r\n npxy(node,:)=mxy(1,:); % solution found are all valid??? Possible seg fail\r\n \r\n fprintf('Solution found\\n');\r\n %hplot4(hxy,npxy,mseg,nseg,4,msegMM);\r\n return;\r\n \r\n end % nphset\r\n end % ipnchk\r\n \r\n fprintf('No solution found\\n');\r\nend %Solve_ICFP047\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2]. The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n delta=floor(epsilon*Lseg/1000000);\r\n msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n\r\n%These routines can be used to read ICFP problems, write ICFP text file, and visualize the data\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n% fid=fopen([num2str(pid) '.problem'],'r');\r\n% pstr=fgetl(fid);\r\n% fclose(fid)\r\n% \r\n% Lpstr=length(pstr);\r\n% holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n% epsidx=findstr('\"epsilon\":',pstr);\r\n% figidx=findstr(',\"figure\"',pstr);\r\n% edgidx=findstr('\"edges\":[[',pstr);\r\n% veridx=findstr('\"vertices\":[[',pstr);\r\n% epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n% \r\n% hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n% hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n% \r\n% pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n% \r\n% mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n% function write_submission(npxy,pid)\r\n% fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n% fid=fopen(fname,'wt'); % t for notepad editing\r\n% \r\n% %Create ICFP submission\r\n% fprintf('{\"vertices\": [');\r\n% fprintf(fid,'{\"vertices\": [');\r\n% for i=1:size(npxy,1)-1 \r\n% fprintf('[%i,%i],',npxy(i,:));\r\n% fprintf(fid,'[%i,%i],',npxy(i,:));\r\n% end \r\n% fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n% fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n% fclose(fid);\r\n% end\r\n\r\n\r\n% function hplot(vxy,qxy,mseg,Lmseg,id)\r\n% %Need check of segment crossing a hole segment but ignore endpoint\r\n% [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n% figure(id)\r\n% plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n% hold on\r\n% plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n% plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n% for i=1:size(qxy,1)%length(xq)\r\n% text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i),'FontSize',12);\r\n% end\r\n% \r\n% for i=1:Lmseg\r\n% if in(mseg(i,1))+in(mseg(i,2))\u003c2\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment to OOB pt\r\n% else\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-')\r\n% end\r\n% text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n% end\r\n% \r\n% %o+*.x_|sd^v\u003e\u003cph\r\n% %colors ymcrgbwk\r\n% \r\n% axis tight\r\n% axis ij\r\n% hold off \r\n% end % hplot\r\n\r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n% segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n% [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n% figure(id)\r\n% plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n% hold on\r\n% plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n% plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n% for i=1:size(qxy,1) %length(xq)\r\n% text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n% end\r\n% \r\n% for i=1:Lmseg\r\n% d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n% if d2seg\u003csegMNM(i,1)\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n% elseif d2seg\u003esegMNM(i,3)\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n% else\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n% end\r\n% text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n% end\r\n% \r\n% %o+*.x_|sd^v\u003e\u003cph\r\n% %colors ymcrgbwk\r\n% \r\n% axis tight\r\n% axis ij\r\n% hold off\r\n% end % hplot3\r\n\r\n\r\n","test_suite":"%%\r\n% ICFP Problem 47 \r\n% 75% of hole edges not covered in solution. All hole vertices covered.\r\n% possible method force longest fig segment onto pair hole vertices then perms\r\n% brute force processing will take 180 seconds so not part of this cody challenge\r\ntic\r\n% ICFP Problem Id 47\r\n% nh 10 np 11\r\nepsilon=41323;\r\nhxy=[6 14;36 19;40 17;69 0;79 21;41 36;36 33;16 44;7 34;0 28;6 14];\r\npxy=[0 11;1 85;8 56;11 0;14 45;14 59;14 88;30 37;30 56;56 85;67 64];\r\nmseg=[1 4;4 8;8 5;5 1;8 11;11 10;10 9;9 8;5 6;6 9;9 7;7 2;2 6;6 3;3 5];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP047(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\nfor i=1:nseg\r\n L2pxyseg = sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n L2npxyseg = sum((npxy(mseg(i,1),:)-npxy(mseg(i,2),:)).^2);\r\n if abs(L2npxyseg/L2pxyseg-1)*1000000 \u003e epsilon\r\n valid = 0;\r\n break;\r\n end\r\nend\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-07-21T17:55:33.000Z","updated_at":"2021-07-22T01:54:06.000Z","published_at":"2021-07-22T01:54:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"262\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"541\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to solve ICFP problems 47 according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u0026lt;= epsilon/1000000. Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes routines to read ICFP problem files and to write ICFP solution files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2026,"title":"Skyscrapers - Puzzle","description":"The Skyscraper puzzle challenge comes from \u003chttp://logicmastersindia.com/home/ Logic Masters India\u003e and \u003chttp://www.conceptispuzzles.com/ Games' Concept is Puzzles\u003e. \r\n\r\nCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\r\n\r\n*Input:* [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\r\n\r\n*Output:* M an NxN matrix\r\n\r\n*Example:*\r\n\r\n vr=[0 0 3 0 0]';\r\n vL=[3 0 0 1 0]';\r\n vd=[0 0 0 0 0];\r\n vu=[5 2 0 0 0];\r\n\r\n M\r\n 5 4 2 1 3\r\n 4 5 1 3 2\r\n 3 2 4 5 1\r\n 2 1 3 4 5\r\n 1 3 5 2 4\r\n\r\n*Algorithm Discussion:*\r\n\r\n 1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n 2) Calc Skyscraper count from Left and Right\r\n 3) Determine subset of SkyVectors possible for each Row and Column\r\n 4) Sort the Qty of 2*N possible solutions\r\n 5) Recursion from least to most valid SkyVectors\r\n 6) In recursion verify valid overlay or return\r\n","description_html":"\u003cp\u003eThe Skyscraper puzzle challenge comes from \u003ca href = \"http://logicmastersindia.com/home/\"\u003eLogic Masters India\u003c/a\u003e and \u003ca href = \"http://www.conceptispuzzles.com/\"\u003eGames' Concept is Puzzles\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M an NxN matrix\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003evr=[0 0 3 0 0]';\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eM\r\n 5 4 2 1 3\r\n 4 5 1 3 2\r\n 3 2 4 5 1\r\n 2 1 3 4 5\r\n 1 3 5 2 4\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm Discussion:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n2) Calc Skyscraper count from Left and Right\r\n3) Determine subset of SkyVectors possible for each Row and Column\r\n4) Sort the Qty of 2*N possible solutions\r\n5) Recursion from least to most valid SkyVectors\r\n6) In recursion verify valid overlay or return\r\n\u003c/pre\u003e","function_template":"function m=solve_skyscrapers(vr,vL,vd,vu)\r\n m=[];\r\nend","test_suite":"%%\r\n%Games Feb 2014 #1\r\nvr=[0 0 1 0 5]'; %1\r\nvL=[0 4 4 0 0]';\r\nvd=[2 2 0 1 3];\r\nvu=[3 0 0 2 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd; % view down check\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view Left check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m); % view Up check\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #2\r\nvr=[0 4 0 2 0]'; %2\r\nvL=[5 1 0 0 0]';\r\nvd=[0 0 3 0 0];\r\nvu=[4 1 2 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #3\r\nvr=[5 2 2 0 0]'; %3\r\nvL=[0 3 0 3 4]';\r\nvd=[5 0 0 0 0];\r\nvu=[0 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #4\r\nvr=[0 0 4 5 0]'; %4\r\nvL=[0 0 0 0 0]';\r\nvd=[2 0 2 3 0];\r\nvu=[0 0 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #5\r\nvr=[3 5 0 0 0]'; %5\r\nvL=[0 0 4 0 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[2 0 1 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\n\r\n%%\r\nvr=[0 0 3 0 0]'; %Games Feb 2014 #6\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-29T19:42:36.000Z","updated_at":"2026-01-08T14:21:06.000Z","published_at":"2013-11-29T22:09:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Skyscraper puzzle challenge comes from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://logicmastersindia.com/home/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames' Concept is Puzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M an NxN matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[vr=[0 0 3 0 0]';\\nvL=[3 0 0 1 0]';\\nvd=[0 0 0 0 0];\\nvu=[5 2 0 0 0];\\n\\nM\\n 5 4 2 1 3\\n 4 5 1 3 2\\n 3 2 4 5 1\\n 2 1 3 4 5\\n 1 3 5 2 4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Discussion:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\\n2) Calc Skyscraper count from Left and Right\\n3) Determine subset of SkyVectors possible for each Row and Column\\n4) Sort the Qty of 2*N possible solutions\\n5) Recursion from least to most valid SkyVectors\\n6) In recursion verify valid overlay or return]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1978,"title":"Sokoban: Puzzle 10.45","description":"The \u003chttp://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 Sokoban Site\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55. \u003chttp://en.wikipedia.org/wiki/Sokoban wiki Sokoban reference\u003e. \r\n\r\nThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\r\n\r\nSokoban can not jump blocks or move diagonally.\r\n\r\nThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7). \r\n\r\nSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\r\n\r\n*Input:* Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\r\n\r\n*Output:* Moves, Vector of [-1 +1 -nr +nr] values\r\n\r\n*Scoring:* Sum of Moves and Pushes\r\n\r\n*Examples:* \r\n\r\nMap\r\n\r\n 11111111\r\n 11111111 Moves=[5] push right for a 5 row array\r\n 11042311\r\n 11111111\r\n 11111111\r\n\r\n*Test Suite Visualization:* A visualization option is provided.\r\n\r\n*Algorithms:* Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid. \r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138\"\u003eSokoban Site\u003c/a\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55. \u003ca href = \"http://en.wikipedia.org/wiki/Sokoban\"\u003ewiki Sokoban reference\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/p\u003e\u003cp\u003eSokoban can not jump blocks or move diagonally.\u003c/p\u003e\u003cp\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/p\u003e\u003cp\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Sum of Moves and Pushes\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMap\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e11111111\r\n11111111 Moves=[5] push right for a 5 row array\r\n11042311\r\n11111111\r\n11111111\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eTest Suite Visualization:\u003c/b\u003e A visualization option is provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithms:\u003c/b\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/p\u003e","function_template":"function moves=solve_Sokoban(m)\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n\r\n moves=[];\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\nvisualize=0;\r\nif visualize\r\n figure(1); % Start\r\n map=[.5 .5 .5;0 0 0;.5 .5 .5;0 1 0;0 0 1;\r\n 1 0 0;1 1 0;0 0 0;1 0 1;.5 .5 .5];\r\n colormap(map);\r\n figure(2); % Move map\r\n% -1 0 1 2 3 4 5 6 7 8\r\n% -1 color limit, 8 color limit\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n colormap(map)\r\nend\r\n\r\n%Sokoban map http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 \r\n%Puzzle 45 \r\nsmap=[0 0 0 0 0 0;0 3 2 2 4 0;3 3 2 0 2 0;3 5 0 0 1 1];\r\n[nr,nc]=size(smap);\r\nm=ones(nr+4,nc+4);\r\nm(3:end-2,3:end-2)=smap;\r\n\r\nif visualize\r\n im=m;\r\n mend=size(map,1)-2;\r\n im(1)=-1;im(end)=mend;\r\n figure(1);imagesc(im)\r\n m\r\nend\r\n\r\ntic\r\nmoves=solve_Sokoban(m);\r\ntoc\r\n\r\n% Check Solution\r\n valid=1;\r\n ptr=find(m==4);\r\n pushes=0;\r\n if isempty(ptr),ptr=find(m==7);end\r\n for i=1:length(moves)\r\n mv=moves(i);\r\n mvptr=m(ptr+mv);\r\n mvptr2=m(ptr+2*mv);\r\n if mvptr==1 % Illegal run into wall\r\n valid=0;\r\n break;\r\n end\r\n if (mvptr2==5 || mvptr2==2 || mvptr2==1) \u0026\u0026 (mvptr==5 || mvptr==2) % Illegal double block push\r\n valid=0;\r\n break;\r\n end\r\n if mvptr==0 || mvptr==3\r\n m(ptr)=m(ptr)-4;\r\n m(ptr+mv)=m(ptr+mv)+4;\r\n ptr=ptr+mv;\r\n elseif mvptr==2 || mvptr==5\r\n m(ptr)=m(ptr)-4;\r\n m(ptr+2*mv)=m(ptr+2*mv)+2;\r\n m(ptr+mv)=m(ptr+mv)-2+4;\r\n ptr=ptr+mv;\r\n pushes=pushes+1;\r\n end\r\n end\r\n \r\n fprintf('Moves %i Pushes %i\\n',length(moves),pushes)\r\n valid=valid \u0026\u0026 nnz(m==3)==0 \u0026\u0026 nnz(m==7)==0;\r\n assert(valid)\r\n\r\nif visualize \u0026\u0026 valid\r\n % display moves\r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n ptr=find(im==4);\r\n if isempty(ptr),ptr=find(im==7);end\r\n for i=1:length(moves)\r\n mv=moves(i);\r\n mvptr=im(ptr+mv);\r\n if mvptr==0 || mvptr==3\r\n im(ptr)=im(ptr)-4;\r\n im(ptr+mv)=im(ptr+mv)+4;\r\n ptr=ptr+mv;\r\n elseif mvptr==2 || mvptr==5\r\n im(ptr)=im(ptr)-4;\r\n im(ptr+2*mv)=im(ptr+2*mv)+2;\r\n im(ptr+mv)=im(ptr+mv)-2+4;\r\n ptr=ptr+mv;\r\n end\r\n \r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n end\r\n \r\nend % vis and valid\r\n\r\n\r\nmovs=length(moves);\r\nassignin('caller','score',min(200,max(0,movs+pushes)));","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2013-11-11T01:51:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-10T23:21:50.000Z","updated_at":"2025-12-03T12:16:08.000Z","published_at":"2013-11-11T01:51:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.game-sokoban.com/index.php?mode=level\u0026amp;lid=16138\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sokoban\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ewiki Sokoban reference\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban can not jump blocks or move diagonally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sum of Moves and Pushes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[11111111\\n11111111 Moves=[5] push right for a 5 row array\\n11042311\\n11111111\\n11111111]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTest Suite Visualization:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A visualization option is provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithms:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2005,"title":"BattleShip - Seaman (1) thru Admiral(6) : CPU Time Scoring(msec)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThis Challenge is to complete three full sets of Battleship in minimal time.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Scoring:* Total Time (msec)\r\n\r\n*Example:*\r\n\r\n r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n \r\n m b\r\n 000000000000 000000000000\r\n 077757777770 000011000000\r\n 077777777770 000000000000\r\n 077777777770 000100010000\r\n 077777777770 000100010000\r\n 077777777770 010000010000\r\n 077777777770 010000010010\r\n 027777777760 010000000010\r\n 077777777770 000101000010\r\n 077777777770 000000000000\r\n 077777477770 010001100100\r\n 000000000000 000000000000\r\n\r\n*Algorithm:* \r\n\r\n 1) Initialize processing array based upon input matrix.\r\n 2) Implement a cycling check of driven array changes\r\n 3) Quick Test of Change every single Unknown serially\r\n 4) Evolve and check if complete solution created\r\n 5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003eGames Magazine Battleships\u003c/a\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/p\u003e\u003cp\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/p\u003e\u003cp\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/p\u003e\u003cp\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/p\u003e\u003cp\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e b; A binary 12x12 array\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total Time (msec)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003em b\r\n000000000000 000000000000\r\n077757777770 000011000000\r\n077777777770 000000000000\r\n077777777770 000100010000\r\n077777777770 000100010000\r\n077777777770 010000010000\r\n077777777770 010000010010\r\n027777777760 010000000010\r\n077777777770 000101000010\r\n077777777770 000000000000\r\n077777477770 010001100100\r\n000000000000 000000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Initialize processing array based upon input matrix.\r\n2) Implement a cycling check of driven array changes\r\n3) Quick Test of Change every single Unknown serially\r\n4) Evolve and check if complete solution created\r\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs\r\n\u003c/pre\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"assignin('caller','score',2000);\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 1-Seaman\r\nr=[0 2 2 3 1 1 1 1 2 2 5 0]';\r\nc=[0 1 0 1 1 2 6 0 5 0 4 0];\r\nm(2,2)=1;\r\nm(2,6)=1;\r\nm(4,9)=3;\r\n\r\n%tz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\n%tt=tz+cputime-time0\r\ntt=cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 2-Petty Officer\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 3-Ensign\r\nr=[0 3 0 4 1 0 0 1 2 1 8 0]';\r\nc=[0 5 1 1 3 1 1 1 1 3 3 0];\r\nm(4,7)=1;\r\nm(4,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 4-Captain\r\nr=[0 1 2 2 2 2 5 0 5 0 1 0]';\r\nc=[0 5 0 0 0 2 1 4 2 1 5 0];\r\nm(4,8)=0;\r\nm(7,10)=4;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 5-Commodore\r\nr=[0 1 1 5 0 3 1 3 2 1 3 0]';\r\nc=[0 2 2 1 0 2 1 6 0 5 1 0];\r\nm(6,4)=1;\r\nm(6,8)=0;\r\nm(7,10)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 6-Admiral\r\nr=[0 5 1 4 2 3 1 1 0 3 0 0]';\r\nc=[0 4 0 1 2 4 2 1 1 5 0 0];\r\nm(5,2)=1;\r\nm(10,7)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 1-Seaman\r\nr=[0 1 1 1 1 2 3 3 3 1 4 0]';\r\nc=[0 3 2 0 1 6 0 3 1 4 0 0];\r\nm(2,3)=1;\r\nm(8,5)=1;\r\nm(7,8)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 3-Ensign\r\nr=[0 3 0 0 2 4 3 2 1 4 1 0]';\r\nc=[0 2 2 5 2 3 0 3 0 2 1 0];\r\nm(7,2)=1;\r\nm(7,4)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 4-Captain\r\nr=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\nm(8,2)=2;\r\nm(2,5)=5;\r\nm(11,7)=4;\r\nm(8,11)=6;\r\n\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 5-Commodore\r\nr=[0 3 2 3 1 1 1 3 3 2 1 0]';\r\nc=[0 1 2 4 1 4 1 1 0 5 1 0];\r\nm(2,10)=5;\r\nm(8,4)=6;\r\nm(8,6)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 6-Admiral\r\nr=[0 5 1 0 3 0 1 5 2 3 0 0]';\r\nc=[0 0 4 2 5 2 1 2 1 1 2 0];\r\nm(2,10)=0;\r\nm(8,7)=0;\r\nm(10,5)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 1-Seaman\r\nr=[0 1 1 2 4 1 0 2 2 5 2 0]';\r\nc=[0 1 1 1 1 4 0 7 0 2 3 0];\r\nm(2,8)=0;\r\nm(8,3)=1;\r\nm(9,6)=0;\r\nm(5,11)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 3-Ensign\r\nr=[0 3 0 2 3 1 1 2 2 2 4 0]';\r\nc=[0 1 1 0 6 1 4 0 3 1 3 0];\r\nm(4,3)=0;\r\nm(5,6)=4;\r\nm(7,9)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 4-Captain\r\nr=[0 0 6 0 2 2 4 1 3 2 0 0]';\r\nc=[0 3 1 3 1 2 2 2 2 0 4 0];\r\nm(5,2)=0;\r\nm(9,4)=0;\r\nm(3,5)=4;\r\nm(6,11)=2;\r\nm(8,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 5-Commodore %\r\nr=[0 5 2 1 1 7 1 2 0 0 1 0]';\r\nc=[0 2 3 1 2 1 3 1 2 0 5 0];\r\nm(8,2)=1;\r\nm(5,11)=2;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 6-Admiral % Solved with with Bship HV .10 \r\n% solved recur .023\r\nr=[0 0 2 4 1 4 1 0 2 0 6 0]';\r\nc=[0 3 1 3 1 3 2 1 2 1 3 0];\r\nm(3,2)=0;\r\nm(4,5)=4;\r\nm(9,9)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\ntt\r\nassignin('caller','score',min(2000,floor(1000*tt)));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-17T23:26:01.000Z","updated_at":"2013-11-18T00:27:11.000Z","published_at":"2013-11-18T00:27:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total Time (msec)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm b\\n000000000000 000000000000\\n077757777770 000011000000\\n077777777770 000000000000\\n077777777770 000100010000\\n077777777770 000100010000\\n077777777770 010000010000\\n077777777770 010000010010\\n027777777760 010000000010\\n077777777770 000101000010\\n077777777770 000000000000\\n077777477770 010001100100\\n000000000000 000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created\\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":57555,"title":"Easy Sequences 91: Generalized McCarthy-91 Recursive Function","description":"The McCarthy 91 function is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\r\nThe McCarthy 91 function is defined for integer as: \r\n \r\nRemarkably, the function yields for all (hence, the function name).\r\nA more generalized form of the McCarthy's recursive function is defined below: \r\n \r\nFor positive integers , , , , and . \r\nThe expression , means applying the function , number of times:\r\n \r\nThis means that the 'original' McCarthy-91 function is actually , with , , and .\r\nGiven integers, , , , , and , evaluate the value of .\r\n--------------------\r\nNOTE: The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 454px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 227px; transform-origin: 407px 227px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/McCarthy_91_function\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMcCarthy 91 function\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 286.5px 8px; transform-origin: 286.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 167.5px 8px; transform-origin: 167.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThe McCarthy 91 function is defined for integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e as:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 366.5px; height: 18.5px;\" width=\"366.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100px 8px; transform-origin: 100px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRemarkably, the function yields \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 70.5px; height: 18.5px;\" width=\"70.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.5px 8px; transform-origin: 21.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for all \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 51.5px; height: 18px;\" width=\"51.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 89px 8px; transform-origin: 89px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (hence, the function name).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 280px 8px; transform-origin: 280px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eA more generalized form of the McCarthy's recursive function is defined below: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 291px; height: 19.5px;\" width=\"291\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.5px 8px; transform-origin: 66.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor positive integers \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ea\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eb\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50.5px 8px; transform-origin: 50.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe expression \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 61px; height: 19.5px;\" width=\"61\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 94.5px 8px; transform-origin: 94.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, means applying the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eG\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54.5px 8px; transform-origin: 54.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e number of times:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 52px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 26px; text-align: left; transform-origin: 384px 26px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-20px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 165px; height: 52px;\" width=\"165\" height=\"52\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 162px 8px; transform-origin: 162px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis means that the 'original' McCarthy-91 function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 37px; height: 18.5px;\" width=\"37\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35px 8px; transform-origin: 35px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is actually \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 33px; height: 18.5px;\" width=\"33\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 51.5px; height: 18px;\" width=\"51.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 44px; height: 18px;\" width=\"44\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFcAAAAkCAYAAAD8fqYDAAACGklEQVRoQ+2Yu0oEQRBFZz9A8BWaGQmCgoiJpipGCoqGgqDoBxgJBmpgaiB+gKBGpj7AREMTYx+IuQ/8Ab0F3TA0M7NdPVPbu0wNXNx1u6q7ztb03p5GopcYgYZYZk2cKFzBJlC4CleQgGBq7VyFK0JgBFmfAjN7xdaxc1cAdB96hOg152LF1gnuHCgeQYOG5gUDblBsXeAeAOSHgXrChBscWxe46Vv/jwk3OFbhcnbcJGF9MQq3jeD2YS1DZj0PvHW1zWhW9zmrZsX6dC4BXYPWoRvoHVqCeqBVyBcyecOuChD7zpc3FQuQJFyyIKfQFzQNvZnJjvF3EyKvOAt9ekC7wpgZj3FFQ77xYW/JHG0BlwzzGfQKTTgAX/Ce/CKnWLI0YyXB/CCea/zdKaPDncSK7s2qpjJuferoYegaCj1CluQcHB4dru1MzikmuNoWB0aFa7cDqjmra1vMovLposI9RznLpqR+Z68tU6m6BdAjZ0A2i5zAeBmaTqy6BQCxt03VcNUtAK79MSvysBsYdwtZ31thg4unirrn2gMCVTnqWC06re2Z8rfEMchMEBUuAXxO7bu7eP0LDUD0BH8Hoh+9Try2sehDs3A6HKVPnc3qYcfmPVsgwIvQQmrGyw7eCshezkPdGQTv8L+iw1BwrM+Dm2bfqH6eQ0DhCraGwlW4ggQEU2vnKlxBAoKptXMVriABwdTauYJw/wHrU5AlYbeBLQAAAABJRU5ErkJggg==\" style=\"width: 43.5px; height: 18px;\" width=\"43.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36px; height: 18px;\" width=\"36\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 56.5px 8px; transform-origin: 56.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGiven integers, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ea\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eb\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19px 8px; transform-origin: 19px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.5px 8px; transform-origin: 80.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, evaluate the value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 33px; height: 18.5px;\" width=\"33\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50px 8px; transform-origin: 50px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e--------------------\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNOTE:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355px 8px; transform-origin: 355px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function m = M(a,b,c,k,n)\r\n m = G(n);\r\n function g = G(x)\r\n if x \u003e a\r\n g = x - b;\r\n else\r\n g = G(G(G(G(x+c)))); % k-times\r\n end\r\n end\r\nend","test_suite":"%%\r\na = 100; b = 10; c = 11; k = 2; n = [1:20 201:220];\r\nm_correct = [repelem(91,20) 191:210];\r\nassert(isequal(arrayfun(@(i) M(a,b,c,k,i),n),m_correct))\r\n%%\r\na = 333; b = 3; c = 33; k = 3; n = 33;\r\nm_correct = 354;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 4444; b = 444; c = 444; k = 4; n = 444444;\r\nm_correct = 444000;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 55555; b = 555; c = 5555; k = 5; n = 5;\r\nm_correct = 56145;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 7777777; b = 77777; c = 777777; k = 7; n = 777;\r\nm_correct = 7700875;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 999999999; b = 9999; c = 999999; k = 99; n = 999;\r\nm_correct = 999997623;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 9999999999; b = 9; c = 99999999; k = 99999; n = 9999;\r\nm_correct = 10009111707;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 7654321; b = 891011; c = 171615141312; k = 181920; n = 1:100;\r\ns_correct = 952242024250;\r\nassert(isequal(sum(arrayfun(@(i) M(a,b,c,k,i),n)),s_correct))\r\n%%\r\na = 10000000000; b = 1000000; c = 1000000000000; k = 1000000; n = 1:1000;\r\nm = arrayfun(@(i) M(a,b,c,k,i),n);\r\ns_correct = [9999000500 9999000500 9999000001 288];\r\nassert(isequal(floor([mean(m) median(m) mode(m) std(m)]),s_correct))\r\n%%\r\nfiletext = fileread('M.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'regexp') || contains(filetext, 'eval') || contains(filetext, 'assignin');\r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":255988,"edited_by":255988,"edited_at":"2023-02-19T04:00:26.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-01-17T23:39:34.000Z","updated_at":"2025-08-23T09:20:38.000Z","published_at":"2023-01-18T08:07:36.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/McCarthy_91_function\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMcCarthy 91 function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe McCarthy 91 function is defined for integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e as:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n) = n-10 \\\\ \\\\text{if} \\\\ n \u0026gt; 100 \\\\ \\\\text{and} \\\\ \\nM(M(n+11)) \\\\ \\\\text{if} \\\\ n \\\\le 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRemarkably, the function yields \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n) = 91\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for all \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en \\\\le 101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (hence, the function name).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA more generalized form of the McCarthy's recursive function is defined below: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n) = n-b \\\\ \\\\text{if} \\\\ n \u0026gt; a \\\\ \\\\text{and} \\\\ \\nG^k(n+c) \\\\ \\\\text{if} \\\\ n \\\\le a\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor positive integers \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe expression \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG^k(n+c) \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, means applying the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e number of times:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG^k(x) = \\\\underbrace{G(G(G(...G(x))))}\\\\\\\\_{\\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\text{k times}}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis means that the 'original' McCarthy-91 function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is actually \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea=100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb=10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec=11\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek=2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGiven integers, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, evaluate the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e--------------------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2838,"title":"Optimum Egyptian Fractions","description":"Following problem was inspired by \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm this problem\u003e and \u003chttp://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542 that comment\u003e.\r\n\r\nGiven fraction numerator _A_ and denominator _B_, find denominators _C_ for \u003chttps://en.wikipedia.org/wiki/Egyptian_fraction Egyptian fraction\u003e. The goal of this problem is to minimize the sum of the list.\r\n\r\nExample:\r\n \r\n A = 16;\r\n B = 63;\r\n % 16/63 == 1/7 + 1/9\r\n C = [7, 9];\r\n\r\n_C_ may be _[4, 252]_ or _[5, 19, 749, 640395]_ or _[5, 27, 63, 945]_ or _[6, 12, 252]_ or _[7, 9]_ or almost any else of infinite more other options. The best one is _[7, 9]_ with sum 16.\r\n\r\n* You may assume _A\u003cB_,\r\n* Your score will be based on sum of answers,\r\n* No cheating, please,\r\n* While greedy algorithm usually solves this problem, score may not be satisfying,\r\n* Class of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\r\n* I'm open for proposals to improve test, i.e. verification of output which is far from perfect.","description_html":"\u003cp\u003eFollowing problem was inspired by \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm\"\u003ethis problem\u003c/a\u003e and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542\"\u003ethat comment\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven fraction numerator \u003ci\u003eA\u003c/i\u003e and denominator \u003ci\u003eB\u003c/i\u003e, find denominators \u003ci\u003eC\u003c/i\u003e for \u003ca href = \"https://en.wikipedia.org/wiki/Egyptian_fraction\"\u003eEgyptian fraction\u003c/a\u003e. The goal of this problem is to minimize the sum of the list.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e A = 16;\r\n B = 63;\r\n % 16/63 == 1/7 + 1/9\r\n C = [7, 9];\u003c/pre\u003e\u003cp\u003e\u003ci\u003eC\u003c/i\u003e may be \u003ci\u003e[4, 252]\u003c/i\u003e or \u003ci\u003e[5, 19, 749, 640395]\u003c/i\u003e or \u003ci\u003e[5, 27, 63, 945]\u003c/i\u003e or \u003ci\u003e[6, 12, 252]\u003c/i\u003e or \u003ci\u003e[7, 9]\u003c/i\u003e or almost any else of infinite more other options. The best one is \u003ci\u003e[7, 9]\u003c/i\u003e with sum 16.\u003c/p\u003e\u003cul\u003e\u003cli\u003eYou may assume \u003ci\u003eA\u0026lt;B\u003c/i\u003e,\u003c/li\u003e\u003cli\u003eYour score will be based on sum of answers,\u003c/li\u003e\u003cli\u003eNo cheating, please,\u003c/li\u003e\u003cli\u003eWhile greedy algorithm usually solves this problem, score may not be satisfying,\u003c/li\u003e\u003cli\u003eClass of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\u003c/li\u003e\u003cli\u003eI'm open for proposals to improve test, i.e. verification of output which is far from perfect.\u003c/li\u003e\u003c/ul\u003e","function_template":"function C = egyptian(A,B)\r\n A = uint64(A);\r\n B = uint64(B);\r\n C = idivide(A,B,'ceil'); % not likely\r\nend","test_suite":"%%\r\nA = 1;\r\nB = 4;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),4);\r\n%%\r\nA = 2;\r\nB = 6;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),3);\r\n%%\r\nA = 3;\r\nB = 7;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),59);\r\n%%\r\nA = 11;\r\nB = 30;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),11);\r\n%%\r\n% random\r\nfor k = 3:7;\r\n C_min = unique(randi([2 30],1,k));\r\n A = 0; B = 1;\r\n for l = C_min\r\n A = round((A*l + B)/gcd(l,B));\r\n B = lcm(B,l);\r\n end\r\n C = egyptian(A,B);\r\n fra = sum(1./double(C));\r\n fra_correct = A/B;\r\n assert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\n fprintf('Choosen A: %d, B: %d\\nbased on random C: [%s\\b]\\n Sum of C: %d, best is %d or less\\n',A,B,sprintf(' %d,',C_min),sum(C),sum(C_min));\r\nend\r\n%%\r\nA = 2;\r\nB = 101;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),1212);\r\n%%\r\nA = 11;\r\nB = 28;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),11);\r\n%%\r\nA = 17;\r\nB = 24;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),15);\r\n%%\r\nA = 25;\r\nB = 36;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),16);\r\n%%\r\nA = 1805;\r\nB = 1806;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),55);\r\n%%\r\n\r\nA = 287;\r\nB = 396;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),49);\r\n%%\r\n% Scoring.\r\n% by courtesy of LY Cao\r\nfid = fopen('score.p','wb');\r\nfwrite(fid,sscanf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x'));\r\nfclose(fid);\r\n% Those lists may be extended and scoring mechanism may be changed a bit\r\nlistA = [2 2 2 2 3 3 3 3 4 5 5 13 31 1805];\r\nlistB = [5 7 21 25 5 7 8 71 71 121 17 42 311 1806];\r\nS = 0;\r\ntry\r\n for k = 1:numel(listA),\r\n A = listA(k);\r\n B = listB(k);\r\n C = egyptian(A,B);\r\n fra = sum(1./double(C));\r\n fra_correct = A/B;\r\n assert(~any(mod(C,1)) ...\r\n \u0026\u0026 all(C\u003e1) ...\r\n \u0026\u0026 isequal(sort(C),unique(C)) ...\r\n \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\n S = S + sum(C);\r\n end\r\n score(round(20*log10(double(S))));\r\ncatch\r\n score(1e4);\r\n error+1;\r\nend\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":12,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2016-04-12T22:03:10.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-18T23:21:47.000Z","updated_at":"2025-11-29T19:57:57.000Z","published_at":"2016-04-11T13:38:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing problem was inspired by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethis problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethat comment\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven fraction numerator\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and denominator\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eB\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, find denominators\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Egyptian_fraction\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eEgyptian fraction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The goal of this problem is to minimize the sum of the list.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ A = 16;\\n B = 63;\\n % 16/63 == 1/7 + 1/9\\n C = [7, 9];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e may be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[4, 252]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[5, 19, 749, 640395]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[5, 27, 63, 945]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[6, 12, 252]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[7, 9]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or almost any else of infinite more other options. The best one is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[7, 9]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with sum 16.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou may assume\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA\u0026lt;B\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour score will be based on sum of answers,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNo cheating, please,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhile greedy algorithm usually solves this problem, score may not be satisfying,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eClass of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI'm open for proposals to improve test, i.e. verification of output which is far from perfect.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":579,"title":"Spiral In","description":"Create an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\r\nFor example:\r\n\u003e\u003e spiralIn(4,5)\r\nans =\r\n 1 14 13 12 11\r\n 2 15 20 19 10\r\n 3 16 17 18 9\r\n 4 5 6 7 8","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"baseline-shift: 0px; block-size: 190px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 95px; transform-origin: 468.5px 95px; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 434.5px 8px; transform-origin: 434.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCreate an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 40.8417px 8px; transform-origin: 40.8417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 108px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.5px 54px; transform-origin: 464.5px 54px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 61.6px 8.5px; tab-size: 4; transform-origin: 61.6px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u0026gt;\u0026gt; spiralIn(4,5)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 19.25px 8.5px; tab-size: 4; transform-origin: 19.25px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 14 13 12 11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 2 15 20 19 10\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3 16 17 18 9\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4 5 6 7 8\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = spiralIn(m,n)\r\n s = zeros(m,n);\r\nend","test_suite":"%%\r\nm = 3;\r\nn = 5;\r\ns_correct = [1 12 11 10 9; 2 13 14 15 8; 3 4 5 6 7];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 5;\r\nn = 3;\r\ns_correct = [1 12 11; 2 13 10; 3 14 9; 4 15 8; 5 6 7];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n\r\n%%\r\nm = 1;\r\nn = 1;\r\ns_correct = 1;\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 5;\r\nn = 0;\r\ns_correct = zeros(5,0);\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 2;\r\nn = 2;\r\ns_correct = [1 4; 2 3];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n\r\n%%\r\n%Test case added on 4/4/26\r\nm = 2*randi(10)+1;\r\ns_correct = m^2+1-rot90(spiral(m));\r\nassert(isequal(spiralIn(m,m),s_correct))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":4,"created_by":3117,"edited_by":223089,"edited_at":"2026-04-04T09:55:47.000Z","deleted_by":null,"deleted_at":null,"solvers_count":120,"test_suite_updated_at":"2026-04-04T09:55:47.000Z","rescore_all_solutions":false,"group_id":18,"created_at":"2012-04-13T13:50:35.000Z","updated_at":"2026-04-04T09:56:24.000Z","published_at":"2012-04-13T13:50:35.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e spiralIn(4,5)\\nans =\\n 1 14 13 12 11\\n 2 15 20 19 10\\n 3 16 17 18 9\\n 4 5 6 7 8]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPEAAAB/CAIAAACxE7P2AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAB3RJTUUH5AsMABUVJm9/YgAAACR0RVh0U29mdHdhcmUATUFUTEFCLCBUaGUgTWF0aFdvcmtzLCBJbmMuPFjdGAAAACJ0RVh0Q3JlYXRpb24gVGltZQAxMS1Ob3YtMjAyMCAxNjoyMToyMaHVoXMAAAxGSURBVHic7Z1tSFPvG8evpotJLQslF0WeIn492hShZ2q/JJOKNd/ki4JmikGFQlFRkS7oRUKQPYA9UP4L60UPZCRJ0cM07WFRMdIyk5qVYcNSi9LM6f/FqbHfzmY2z+772tn1YS/sPtt9vl77enef+5z7uob09fUBQSgIFW8BBCEz5GlCaZCnCaVBniaUBnmaUBqRwe3eag1u/4TCMBgG38eQ4K7lDRkSxM4JxVFptep0uri4uJEjRwbcSZDHaYL4S1paWrq6ugbjaZpPE+hob2/v6uoK+ONBmHtYrfDvvzL3SYQbJSVgNgf2URqnCaVBniaUBqtrRLMZFi1idC4P/nf6dKVkPbHAYhHi49mL2bNnj8Ph8GosKSlhrwQAMjMzvVoWGQzmtWs5SGlqAotFzg77ZOfOnT4A71dJifwnGgAWX8F68+YNFzEGyeKrIAhclPT5uogym818pMhtGJp7EEqDPE0oDfI0oTRC4z5ibW3tixcvenp6oqOjU1NTIyP5y3748GFDQ8Pq1atVKv7jAgYxDQ0Nz58/7+zsjIyMTEhImDJlCi8l/M3RP48fP87KyrLb7e6W6Ojo7du379ixg6Oqjx8/Go1Gp9OZkZExdOhQjkowiKmpqdm4caPndwQAer2+uLh47ty57PWg9vTdu3eXLl3a2dkZGxublpam0Wjq6+urq6t37txZW1t79uxZLqra2tqWL1/udDq5nN0L7mKuXbtmNBpdLtewYcOWLVsWHR3d0dFRXl5ut9sXLVpUXl6emprKWpN8SzK/kWlpxuVyTZw4EQCSk5M/f/7sbj927Jio/PLly3/sRPa1vLdv3yYkJLi7+vHjx8A/K/ta3mDESMMSwFret2/fYmNjAWDevHmtra3u9g8fPkyfPh0AdDpdZ2fnH3oJn7W8srKy169fA0BpaemoUaPc7Tk5OQsWLBDbGUs6efJkQkLCs2fPGJ/XJxjEXLhwobW1NSIi4vz58zExMe72MWPGnD9/HgBaWlquXLnCWBVeT7e1tc2ZMycpKUl6tTFp0iQAqK2tZSamp6dn5syZ2dnZHR0dycnJhw8fZnZqzGJu3LgBAPPnzx87dqzXoWnTpmm1WgB4+PAhY1V4PZ2VlXX//v0nT55ID3369AkA/vnnH2Zient7nz17ptVq9+3bZ7PZxo0bx+zUmMVs3br1ypUre/fu9Xm0t7cXANhftqK+RvRJZWXl1atXASA9PZ3ZSVUqlcViyc3N9ZwF8QKPmMTExMTERJ+Hampqvn37BgBz5sxhKyp0PF1ZWdnR0XH16lXxoR+z2Sx9Cid4REZGFhQUMDtd/6AS44/NmzcDgE6nW7FiBeNTh4ane3t7U1JSXC6X+M/09PTi4mK+koh+WL9+vc1mA4CioiL2N8jwzqc9+f79u8lkMpvN6enparX68uXLEyZMuHv3Lm9dhA+2bdt2/PhxAMjLy8vIyGAvIDTG6eHDh1+8eFH8uampaenSpS9fvly5cmVdXd2YMWP4aiM8ycrKOnXqFACYzeaioiIuGkJjnPYkPj6+rKwMANra2o4ePcpbDvGLL1++LFmyRDT0li1beO11gFD0NABMmTIlOTkZALyeMSB40dzcPGvWrJs3bwLAwYMH9+/fz1EM3rnH69ev6+vrY2JiZs+eLT2q0+kAoKenh7kuwpva2tqUlBSn06nVai9evMjhAY//gtfTmzZtqqioWLx48a1bt6RHxRE6OjqauS7iPzQ2NoqG1ul0FRUV/parWYJ37iE+pnj79u3nz597HSorK3v//j0ApKWlcVBG/Ka7u3vFihVOp3P06NEPHjzAYGjAPE5v2LDh0KFDra2tq1atun79uvuJghs3bqxbtw4AEhISVq9ezVVjuFNYWPjy5UsAyMzMrKurq6urk75n/PjxM2bMYKkKr6djYmLOnj1rMpnq6uomTJhgNBqjoqIaGhrExXxBECoqKjDsMQlnjhw5Iv5QWFhYWFjo8z3Z2dknTpxgKArx3AMAUlNTbTbbggULfv78eenSpdLSUpvNplar8/Lynjx5In0WjGBJY2Mjko0RXuAdp0VmzJiB8H6hyWTqQ1OvjJeYSZMm4QmCJ6jHaYIIAPI0oTgC3vXlF5/by9C8BN4BJ7ww+PymFLkfMUgYyNZKJ+w8ze3JGoIVYedpQvGQpwmlIb+nrQBDfr8Igj3Bvedi/f2DIAiCIAT1XL5xOECSmt9gMHg3McHqqwKqNDkTG6RieH1HBpC7Nqx8a3i/uHPnjvQsJZzqBPRZLD7WiahOANUJIIgQgjxNKA3yNKE0sD+XJ4InB70bDKn5m5ubHz161NnZGRUVtXjx4hEjRvBS4gn/yMg31f+FvNeI1dXVer3eqze9Xn/v3r0BfT4414gtLS2jR48GfvmnOzo61qxZ49mVWq3evn27y+UaYA/S70iWa8RAIiP3NSLqcRpjDnoEqfm7u7uXLFlis9kiIiKMRmNMTIxYPqGwsLCxsdGd3Ic93CPzi4D/Gvwh1zgtTw56ucdpDHUC8vPzAUCr1T548MDdKCaLAYBz584NpBPpdzTIcTrwyITPWh7CHPQYUvP39PQcOHAAAPLz8z0zn2RmZmZnZwOAv32BQQVDZNzg9TSqHPR4UvOXl5d//foVfBUMz8nJAQC73V5fX89MD57IuMHraVQ56PGk5hfvaQuC4Pl/l0hycrJarQa25SbwRMYN3mtEVDno8aTmf/v2LQAkJSVJD6lUKkEQXr16VV1dvXbtWjZ68ETGDd5xuh/Y56AXU/Nj+Nq+f/8O/pOqiTVuxPewAU9k3ISep/nmoOfO06dPAcDfLy62//jxg6kmZISYp7nnoOeOeCHhD/HWXf/vUTyhNM5hyEHPHY1G089R0c1hnnItNH55PDnouSPeb+ru7vZ5VGyPiopiqgkZITBONzc3p6SkiPkzDx48mJuby1sRT4YPHw4A4hK1lBcvXgCAdJkvrMDuaWw56LkzefJkAGhoaJAe6u3tfffuHQD4rKwQPqCee3jmoK+qqiJDA8D8+fMBoL6+XixQ7UlNTY1YQtLn6nX4gNfTOHPQcyctLS02NtblconrP56ILUlJSRieL+cI3rkHzhz03FGpVLm5ufn5+bt37546darJZBLbi4qKSktLAUB8ai+cwetpnDnoMbBr166bN29WVVWlp6cvXLhQEAS73S4WbcrOzna7PGxBOvdAm4MeAyqV6vr163l5eWq1uqqq6syZM3a7XavV7t27Nwz/wqUgHafR5qAX4V4nQKPRFBUVIbzxxD0ygHacJoiAIU8TSoPR3OP06dOVlZVszuXJWofDIGncs2ePg7kSAHBIMveBr+0qvLBarVzECA5Hgbw9BryT0R8+99jywkLlL9BjoNoXBNE/5GlCaZCnCaVBniaUBnmaUBqMPM2rTkCBxSIV84bqBGCqEyD7QhmN04TSIE8TSoM8TSgN8jShNJA+a+oP7nUVEFbhAARhAUyRCSVPf/z40Wg0Op3OjIwMZhlN3dTU1GzcuFHcTuJGr9cXFxfPnTuXsRhP+IYF8EUmZDzNt64CziocwDssgDMybJYbB7k+HXhdBTlqX8hThSMI69ODKcQh/Y4CWJ+WJzLhU/vCDfe6CgircACCsADWyKD2NJK6CqiqcACasAC+yIig9jSSugqoqnAAmrAAvsiIoL5GRFJXAVUVDkATFsAXGRHUnhbrKvBW0R/sq3BAKIQFOEVGBPXcAzlhXoWjH/hGhjwdIFSFwx/cI0OjSyBQFQ5/YIgMjdN/B1Xh8AeeyNA4/RdQFQ5/oIoMeXqgUBUOf2CLDHl6QHhW4aioqKCiBW4QRoY8/We8qnDEx8fzVoQFnJEhT/8ZqsLhD5yRIU//GarC4Q+ckaG1vD9AVTj8gTYyoTROc6mrgLwKB/ArN4E2MjROE0qD0Tjd1NRktVrZnMsTweEQpK1WKwg+moONQdIiAACPsIBPMQ4HHzGyF5AIeNeXP/DXCaBXCLyUvR+RIP4K8jShNIb0yX3p2tXV5d5WKd39zx4LQAFvDcQAqfw9odfr9SNHjgysE/nHaY1Go9PpZO+WCB90Ol3AhoYgzT3i4+PJ1kRgaDSauLi4wfQg/9zDTXt7e1lZWZA6HziCr3UrAiftJtNgRmiRIHqaILhA6x6E0iBPE0qDPE0oDfI0oTTI04TSIE8TSoM8TSgN8jShNMjThNL4P3ncDDWW+w8KAAAAAElFTkSuQmCC\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n %show_pfig(s,p,c,emap,pmap,1)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2020-11-12T23:27:09.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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Solver - Standard 9x9","description":"Solve a Standard 9x9 \u003chttp://en.wikipedia.org/wiki/Sudoku Sudoku\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner. \u003chttp://www.free-sudoku.com/sudoku.php?dchoix=evil Sudoku practice site\u003e.\r\n\r\n*Input:* m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\r\n\r\n*Output:* mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\r\n\r\n*Scoring:* Time (msec) to solve the Hard Sudoku\r\n\r\n*Example:*\r\n\r\n m mout\r\n 390701506 398721546\r\n 042890701 542896731\r\n 106540890 176543892\r\n 820600150 829674153\r\n 400138009 457138269\r\n 031002087 631952487\r\n 065087304 965287314\r\n 703065920 713465928\r\n 204309075 284319675\r\n\r\n*Sudoku Variations:*\r\n\r\nFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\r\n\r\n*Algorithm Spoiler:*\r\nSudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.","description_html":"\u003cp\u003eSolve a Standard 9x9 \u003ca href = \"http://en.wikipedia.org/wiki/Sudoku\"\u003eSudoku\u003c/a\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner. \u003ca href = \"http://www.free-sudoku.com/sudoku.php?dchoix=evil\"\u003eSudoku practice site\u003c/a\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Time (msec) to solve the Hard Sudoku\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003em mout\r\n390701506 398721546\r\n042890701 542896731\r\n106540890 176543892\r\n820600150 829674153\r\n400138009 457138269\r\n031002087 631952487\r\n065087304 965287314\r\n703065920 713465928\r\n204309075 284319675\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eSudoku Variations:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithm Spoiler:\u003c/b\u003e\r\nSudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.\u003c/p\u003e","function_template":"function mout=sudoku_solver(m)\r\n% m is a 9x9 Sudoku array with 0 for unknown values\r\n% create mout a consistent sudoku array\r\n mout=m;\r\nend","test_suite":"assignin('caller','score',500);\r\n%%\r\n% Test 1\r\nmstr=['012300007'; '040600010'; '078900020'; '000000040'; '100000002'; '060000000'; '080001230'; '090004060'; '300007890']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n%%\r\n% Test 2\r\nmstr=['000004500'; '000003600'; '432008700'; '867000000'; '000000000'; '000000417'; '001900854'; '006400000'; '003700000']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n%%\r\n% Test 3\r\nmstr=['120034000'; '000000056'; '000200000'; '007800002'; '600000001'; '500006300'; '000008000'; '340000000'; '000560078']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n\r\n%%\r\n% Timed Test on a Hard Sudoku\r\n% Non-Valid answer creates a Max score but not a fail\r\n% Hard Sudoku\r\nmstr=['005700009'; '030090010'; '100005300'; '600004700'; '040010050'; '002500001'; '004600002'; '080020040'; '200008600']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntime0=cputime;\r\n mout=sudoku_solver(m)\r\netime=(cputime-time0)*1000 % msec\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\n% Not Asserting for Valid answer\r\nif ~valid,etime=500;end\r\nif ~valid2,etime=500;end\r\nassignin('caller','score',min(500,floor(etime)));","published":true,"deleted":false,"likes_count":8,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":51,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-01-02T00:31:17.000Z","updated_at":"2025-12-15T20:03:47.000Z","published_at":"2014-01-02T01:30:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve a Standard 9x9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sudoku\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSudoku\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.free-sudoku.com/sudoku.php?dchoix=evil\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSudoku practice site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time (msec) to solve the Hard Sudoku\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[m mout\\n390701506 398721546\\n042890701 542896731\\n106540890 176543892\\n820600150 829674153\\n400138009 457138269\\n031002087 631952487\\n065087304 965287314\\n703065920 713465928\\n204309075 284319675]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSudoku Variations:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Spoiler:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60840,"title":"4D Hypercube Validation with Prime-Indexed Symmetry and Modular Trace Constraints","description":"Design a function that generates and validates a 4D hypercube matrix where: \r\n1. Prime-Indexed Symmetry: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \r\n2. Recurrence Relation: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \r\n3. Trace Validation: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 132px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 66px; transform-origin: 408px 66px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDesign a function that generates and validates a 4D hypercube matrix where: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ePrime-Indexed Symmetry\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRecurrence Relation\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTrace Validation\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [isValid, hypercube] = hyperValidator(dims, prime_threshold)\r\n % Inputs:\r\n % dims: [n1,n2,n3,n4] hypercube dimensions\r\n % prime_threshold: max prime for trace checks (p ≤ min(dims))\r\n % Outputs:\r\n % isValid: true/false if constraints are satisfied\r\n % hypercube: 4D matrix (NaN if invalid)\r\n \r\n isValid = false;\r\n hypercube = nan(dims);\r\nend","test_suite":"%% Test 1 - Base Case \r\ndims = [2,2,2,2]; \r\nprime_threshold = 2; \r\n[isValid, A] = hyperValidator(dims, prime_threshold); \r\nassert(isValid \u0026\u0026 all(size(A) == [2,2,2,2])); \r\n\r\n%% Test 2 - Prime Trace Check \r\ndims = [5,5,5,5]; \r\nprime_threshold = 3; \r\n[~, A] = hyperValidator(dims, prime_threshold); \r\nassert(mod(trace(A(:,:,3,3)), 6) == 0); % 3! = 6 \r\n\r\n%% Test 3 - Invalid Input Handling \r\ndims = [0,5,5,5]; \r\n[isValid, ~] = hyperValidator(dims, 5); \r\nassert(~isValid); ","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4805930,"edited_by":4805930,"edited_at":"2025-04-07T18:30:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-04-07T18:27:38.000Z","updated_at":"2025-04-13T09:48:40.000Z","published_at":"2025-04-07T18:27:38.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003eDesign a function that generates and validates a 4D hypercube matrix where: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e1. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePrime-Indexed Symmetry\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRecurrence Relation\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e3. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTrace Validation\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61089,"title":"Find Solutions to Edge-Matching Puzzles","description":"I was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\" (Wikipedia). For more information, see the full article: https://en.wikipedia.org/wiki/Edge-matching_puzzle. In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\r\n\r\nSource (spoilers!): https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch \r\nIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\r\nYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\r\ndeck = [2, 1,-3, -4\r\n -2, 1, 4, -3\r\n -3, 2, 4, -1\r\n -1, 2, 3, -4\r\n -4, 1, 3, -2\r\n -4, 1, 3, -1\r\n 2, 4,-1, -3\r\n -3, 2, 4, -2\r\n -3, 1, 4, -2];\r\nYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \r\nsolution = [2, 0\r\n 3, 0\r\n 8, 0\r\n 1, 1\r\n 5, 0\r\n 6, 0\r\n 4, 0\r\n 9, 0\r\n 7, 1];\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1217.61px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.494px 608.807px; transform-origin: 468.494px 608.807px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105.043px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 52.5142px; text-align: left; transform-origin: 444.51px 52.5213px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\" (Wikipedia). For more information, see the full article: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Edge-matching_puzzle.\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003ehttps://en.wikipedia.org/wiki/Edge-matching_puzzle.\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 439.545px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 219.773px; text-align: left; transform-origin: 444.51px 219.773px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" width=\"433\" height=\"434\" style=\"vertical-align: baseline;width: 433px;height: 434px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 10.4972px; text-align: left; transform-origin: 444.51px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSource (spoilers!): \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003ehttps://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84.0341px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 42.017px; text-align: left; transform-origin: 444.51px 42.017px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105.043px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 52.5142px; text-align: left; transform-origin: 444.51px 52.5213px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 161.974px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.503px 80.9801px; transform-origin: 464.503px 80.9872px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003edeck = [2, 1,-3, -4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -2, 1, 4, -3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 2, 4, -1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -1, 2, 3, -4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -4, 1, 3, -2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -4, 1, 3, -1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 2, 4,-1, -3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 2, 4, -2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 1, 4, -2];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 21.0085px; text-align: left; transform-origin: 444.51px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 161.974px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.503px 80.9801px; transform-origin: 464.503px 80.9872px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003esolution = [2, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 8, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1, 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 5, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 6, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 7, 1];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 10.4972px; text-align: left; transform-origin: 444.51px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function solution = EdgeMatchPuzzleSolver(deck)\r\n\r\n solution = zeros(9,2);\r\n\r\nend","test_suite":"%% Test Case 1\r\n%==========================================================================\r\n\r\ndeck = [ 2, 1,-3, -4\r\n -2, 1, 4, -3\r\n -3, 2, 4, -1\r\n -1, 2, 3, -4\r\n -4, 1, 3, -2\r\n -4, 1, 3, -1\r\n 2, 4,-1, -3\r\n -3, 2, 4, -2\r\n -3, 1, 4, -2];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n\r\n\r\n%% Test Case 2\r\n%==========================================================================\r\n\r\ndeck = [-1,-4, 2, 3;\r\n -4,-2, 3, 4;\r\n -1,-4,-3,-2;\r\n -3,-1, 4, 1;\r\n -3,-2, 4, 1;\r\n -3, 2, 1, 3;\r\n 3,-2, 4, 1;\r\n 4,-2,-3, 1;\r\n 4,-1, 3, 2];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n\r\n\r\n%% Test Case 3\r\n%==========================================================================\r\n\r\ndeck = [ 1,-2,-3, 4\r\n -1, 2, 1, 4\r\n 4,-3,-2,-1\r\n -4, 1, 2,-3\r\n -3, 4,-2,-4\r\n 2, 1,-4, 3\r\n -2,-4, 3,-1\r\n -2,-3, 4,-1\r\n 3,-2, 1,-3];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4910069,"edited_by":4910069,"edited_at":"2026-03-06T17:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-30T05:11:39.000Z","updated_at":"2026-03-06T17:13:00.000Z","published_at":"2025-11-30T06:33:57.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \\\"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\\\" (Wikipedia). For more information, see the full article: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Edge-matching_puzzle.\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttps://en.wikipedia.org/wiki/Edge-matching_puzzle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"434\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"433\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSource (spoilers!): \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttps://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[deck = [2, 1,-3, -4\\n -2, 1, 4, -3\\n -3, 2, 4, -1\\n -1, 2, 3, -4\\n -4, 1, 3, -2\\n -4, 1, 3, -1\\n 2, 4,-1, -3\\n -3, 2, 4, -2\\n -3, 1, 4, -2];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1301,"title":"RISK Calculator - Large Armies, High Accuracy, Fast","description":"This Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u003e= 2 and Defense \u003e=1 ].\r\n\r\nRelated to \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map Cody 1260 RISK Board Game Battle Simulation\u003e\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Risk_%28game%29#Official Link to official Risk Rules\u003e\r\n\r\n*Simplified explanation of the dice play:*\r\n \r\n Attacker with 2 armies will throw one die.\r\n Attacker with 3 armies will throw two die.\r\n Attacker with 4 or more armies will throw three die.\r\n \r\n Defense with 1 army will use one die.\r\n Defense with 2 or more armies will throw 2 die.\r\n \r\n The attacker High is compared to the Defender High.\r\n If Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\r\n If the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \r\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\r\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose). \r\n\r\n*Input:* a,d where a is number of attacking armies and d is number defending\r\n\r\n*Output:* pwin, the probability of the Attacker Winning\r\n\r\n*Accuracy:* Accurate to +/- 1e-6\r\n\r\n*Scoring:* Time (msec) to solve 10 Battle Scenarios \r\n\r\n\r\n\u003chttp://recreationalmath.com/Risk/ Risk Calculator\u003e","description_html":"\u003cp\u003eThis Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u003e= 2 and Defense \u003e=1 ].\u003c/p\u003e\u003cp\u003eRelated to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map\"\u003eCody 1260 RISK Board Game Battle Simulation\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Risk_%28game%29#Official\"\u003eLink to official Risk Rules\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSimplified explanation of the dice play:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAttacker with 2 armies will throw one die.\r\nAttacker with 3 armies will throw two die.\r\nAttacker with 4 or more armies will throw three die.\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eDefense with 1 army will use one die.\r\nDefense with 2 or more armies will throw 2 die.\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eThe attacker High is compared to the Defender High.\r\nIf Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\r\nIf the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \r\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\r\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose). \r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e a,d where a is number of attacking armies and d is number defending\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e pwin, the probability of the Attacker Winning\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy:\u003c/b\u003e Accurate to +/- 1e-6\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Time (msec) to solve 10 Battle Scenarios\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://recreationalmath.com/Risk/\"\u003eRisk Calculator\u003c/a\u003e\u003c/p\u003e","function_template":"function pwin = risk_prob(a, d)\r\n pwin=0;\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',5000); % msec\r\n%%\r\na=[100 99 100 10 9 2 2 10 30 70];\r\nd=[100 100 99 9 10 1 2 2 30 80];\r\ny_c=[0.8079031789315619 0.7888693135658454 0.8230449788340404 0.5580697529719042 0.3798720048109818 0.4166666666666667 0.10609567901234569 0.9901146432872121 0.633266311153744 0.5011352886279803];\r\n\r\ntsum=0;\r\nfor i=1:length(a)\r\n ta=clock;\r\n y=risk_prob(a(i), d(i));\r\n t1=etime(clock,ta)*1000; % time in msec\r\n tsum=tsum+t1;\r\n assert(abs(y - y_c(i)) \u003c= 1e-6,sprintf('A=%i D=%i Expect=%.9f pwin=%.9f',a(i),d(i),y_c(i),y))\r\n fprintf('A %3i D %3i Time(msec) %7.3f\\n',a(i),d(i),t1);\r\nend\r\n\r\nfeval( @assignin,'caller','score',floor(min( 5000,tsum )) );","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-25T00:07:52.000Z","updated_at":"2026-02-15T07:40:59.000Z","published_at":"2013-02-25T04:24:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u0026gt;= 2 and Defense \u0026gt;=1 ].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRelated to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody 1260 RISK Board Game Battle Simulation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Risk_%28game%29#Official\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLink to official Risk Rules\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSimplified explanation of the dice play:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Attacker with 2 armies will throw one die.\\nAttacker with 3 armies will throw two die.\\nAttacker with 4 or more armies will throw three die.\\n\\nDefense with 1 army will use one die.\\nDefense with 2 or more armies will throw 2 die.\\n\\nThe attacker High is compared to the Defender High.\\nIf Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\\nIf the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e a,d where a is number of attacking armies and d is number defending\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e pwin, the probability of the Attacker Winning\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAccuracy:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Accurate to +/- 1e-6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time (msec) to solve 10 Battle Scenarios\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://recreationalmath.com/Risk/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRisk Calculator\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47478,"title":"Slitherlink V: Assert/Evolve/Check (large)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 678.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 339.333px; transform-origin: 407px 339.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 210px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 105px; text-align: left; transform-origin: 384px 105px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 168.4px 7.91667px; transform-origin: 168.4px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink V: Assert/Evolve/Check(large size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 207.3px 7.91667px; transform-origin: 207.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method. The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board. The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 314.917px 7.91667px; transform-origin: 314.917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n \r\n %Implement Assert/Check/Evolve\r\n [p]=assert(p,bsegs,s,c,emap,pmap); \r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n if valid\r\n fprintf('sv Assert solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n end\r\n \r\n % Start recursive processing\r\n if ~valid\r\n [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n fprintf('sv recursion solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\nfunction [p]=assert(p,bsegs,s,c,emap,pmap)\r\n %Insert code here to assert a segment as Red/Black\r\n %Check if evolve of is valid\r\n %If not valid then Assert segment as Black/Red depending on case and then evolve\r\n %Keep asserting until no more p updates and/or s is solved\r\n %Asserting ends of red segments first may reduce total time\r\n pb=p*0;\r\n valid=0;\r\n while ~isequal(p,pb) \u0026\u0026 ~valid\r\n pb=p;\r\n [pr,pc]=find(p==1);\r\n % insert code here\r\n end % while\r\nend\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3) \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n pn=p;\r\n pn(ptr,i)=5;pn(i,ptr)=5; % make linkage\r\n \r\n %This modified pn may be invalid and create an invalid evolve result\r\n [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n if ~evalid,continue;end\r\n \r\n [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n if valid\r\n solved=1;\r\n p=pn;\r\n return;\r\n end\r\n \r\n %Invoke the next level of recursion build with the recursion assert and Evolve\r\n [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n if solved\r\n p=pn;\r\n return\r\n end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n % if invalid try next option\r\n % call next level recur\r\n % if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n end %i s3 3\r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n p(1,2)=5; p(2,1)=5;\r\n p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n end\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n end\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n end\r\n end % j L col\r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vud\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hLR\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n if p(cv(2),cv(2)+1)==5 % rr;xr\r\n if i\u003e1\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)+1)==5 % rr;rx\r\n if i\u003e1\r\n p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n if p(cv(2),cv(2)-1)==5 % xr;rr\r\n if i\u003cnrc\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)-1)==5 % rx;rr\r\n if i\u003cnrc\r\n p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n \r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5)\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %need an isequal(p,pb)\r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars\r\n ps=sum(p);\r\n pv= ps\u003e4 \u0026 ~(ps==10);\r\n pidx=find(pv);\r\n for i=pidx\r\n v=[i find(p(i,:)==5)];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n \r\n end\r\n if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n if Lv\u003cnnz(p==5)/2\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n else % Possible solve\r\n pchk=p;\r\n pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n if valid\r\n p=pchk;\r\n return\r\n else % invalid loop connect thus must be 0\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n end\r\n end % Lv\r\n end % p( v 1 end)\r\n end % pidx\r\n end % isequal p pb after cells, ends make no change\r\n \r\n %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n \r\n% isequal(p,pb)\r\n% show_pfig(s,p,c,emap,pmap,3)\r\n% show_pfig(s,p,c,emap,pmap,2)\r\n \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); % .48 17K\r\n %if nnz(sum(p)==5) % Node with no escape %.48\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created **********************************\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n %pidx=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; \r\n %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n 5 5 5 2 5 5 5 5 5 5;\r\n 1 5 5 5 5 1 1 5 5 2;\r\n 0 5 5 5 5 2 5 5 3 3;\r\n 0 5 5 5 1 3 5 5 5 5;\r\n 5 5 5 5 2 3 5 5 5 0;\r\n 3 2 5 5 1 5 5 5 5 2;\r\n 3 5 5 2 0 5 5 5 5 2;\r\n 5 5 5 5 5 5 2 5 5 5;\r\n 3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\ns=['053552235013';\r\n '505555535555';\r\n '355135525552';\r\n '521552155535';\r\n '555305555553';\r\n '535555335551';\r\n '525050255352';\r\n '325255555505';\r\n '525555552521';\r\n '152552253525';\r\n '255533555535';\r\n '255555522555';\r\n '535551355315';\r\n '355535512553';\r\n '555525555515';\r\n '132523255153']-'0'; % Solves with Assert\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=['225355223525';\r\n '555235535535';\r\n '555255555555';\r\n '232535355512';\r\n '355555535515';\r\n '255035555502';\r\n '555555522555';\r\n '055515555315';\r\n '513555535550';\r\n '555025555555';\r\n '015555522552';\r\n '505535555553';\r\n '315553525223';\r\n '555555553555';\r\n '525515531555';\r\n '535312551533']-'0'; % solves with Assert, Dies in Recursion\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n 5 0 5 5 5 3 3 5 5 5;\r\n 5 5 5 1 2 5 5 5 3 5;\r\n 2 5 5 5 5 5 2 0 5 2;\r\n 0 5 5 5 5 5 5 5 5 5;\r\n 5 5 5 5 5 5 5 5 5 3;\r\n 3 5 1 2 5 5 5 5 5 1;\r\n 5 3 5 5 5 3 0 5 5 5;\r\n 5 5 5 0 0 5 5 5 3 5;\r\n 2 1 5 5 5 1 5 5 3 5]; % solves with recursive/assert\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T21:28:57.000Z","updated_at":"2024-12-14T18:13:16.000Z","published_at":"2020-11-12T23:19:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink V: Assert/Evolve/Check(large size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method. The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board. The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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IV: Recursive (medium size)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.667px 7.91667px; transform-origin: 144.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink IV: Recursive (medium size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 239.35px 7.91667px; transform-origin: 239.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion. When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 324.633px 7.91667px; transform-origin: 324.633px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv init solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n% show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv evolve solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n \r\n % Start recursive processing\r\n if ~valid\r\n [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n fprintf('sv recursion solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3) \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n pn=p;\r\n %insertion of code required here\r\n \r\n %This modified pn may be invalid and create an invalid evolve result\r\n [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n if ~evalid,continue;end\r\n \r\n [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n if valid\r\n solved=1;\r\n p=pn;\r\n return;\r\n end\r\n \r\n %Invoke the next level of recursion build with the recursion assert and Evolve\r\n [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n if solved\r\n p=pn;\r\n return\r\n end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n % if invalid try next option\r\n % call next level recur\r\n % if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n end %i s3 3\r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n p(1,2)=5; p(2,1)=5;\r\n p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n end\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n end\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n end\r\n end % j L col\r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vud\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hLR\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n if p(cv(2),cv(2)+1)==5 % rr;xr\r\n if i\u003e1\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)+1)==5 % rr;rx\r\n if i\u003e1\r\n p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n if p(cv(2),cv(2)-1)==5 % xr;rr\r\n if i\u003cnrc\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)-1)==5 % rx;rr\r\n if i\u003cnrc\r\n p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n \r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5)\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %need an isequal(p,pb)\r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars\r\n ps=sum(p);\r\n pv= ps\u003e4 \u0026 ~(ps==10);\r\n pidx=find(pv);\r\n for i=pidx\r\n v=[i find(p(i,:)==5)];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n \r\n end\r\n if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n if Lv\u003cnnz(p==5)/2\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n else % Possible solve\r\n pchk=p;\r\n pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n if valid\r\n p=pchk;\r\n return\r\n else % invalid loop connect thus must be 0\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n end\r\n end % Lv\r\n end % p( v 1 end)\r\n end % pidx\r\n end % isequal p pb after cells, ends make no change\r\n \r\n %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n \r\n% isequal(p,pb)\r\n% show_pfig(s,p,c,emap,pmap,3)\r\n% show_pfig(s,p,c,emap,pmap,2)\r\n \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); % .48 17K\r\n %if nnz(sum(p)==5) % Node with no escape %.48\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created **********************************\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n %pidx=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; \r\n %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n 5 5 5 2 5 5 5 5 5 5;\r\n 1 5 5 5 5 1 1 5 5 2;\r\n 0 5 5 5 5 2 5 5 3 3;\r\n 0 5 5 5 1 3 5 5 5 5;\r\n 5 5 5 5 2 3 5 5 5 0;\r\n 3 2 5 5 1 5 5 5 5 2;\r\n 3 5 5 2 0 5 5 5 5 2;\r\n 5 5 5 5 5 5 2 5 5 5;\r\n 3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n '151';\r\n '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n '15551';\r\n '25055';\r\n '55253';\r\n '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n 5 0 5 5 5 3 3 5 5 5;\r\n 5 5 5 1 2 5 5 5 3 5;\r\n 2 5 5 5 5 5 2 0 5 2;\r\n 0 5 5 5 5 5 5 5 5 5;\r\n 5 5 5 5 5 5 5 5 5 3;\r\n 3 5 1 2 5 5 5 5 5 1;\r\n 5 3 5 5 5 3 0 5 5 5;\r\n 5 5 5 0 0 5 5 5 3 5;\r\n 2 1 5 5 5 1 5 5 3 5]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T20:32:38.000Z","updated_at":"2020-11-12T23:28:31.000Z","published_at":"2020-11-12T23:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink IV: Recursive (medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion. When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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the number","description":"Using the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\r\n\r\nExamples:\r\n\r\n '10007.76' should return 'Ten thousand and seven point seven six'\r\n '001102.34' should return 'One thousand one hundred and two point three four'\r\n '.4' should return 'Zero point four'\r\n '4.' should return 'Four'\r\n '0.' and '.' should return 'Zero'\r\n '340,139' should return 'Three hundred and forty thousand one hundred and thirty nine'\r\n\r\n\r\nIn the British short scale notation:\r\n\r\n 1 Hundred = 10^2\r\n 1 Thousand = 10^3\r\n 1 Million = 10^6\r\n 1 Billion = 10^9\r\n 1 Trillion = 10^12\r\n 1 Quadrillion = 10^15\r\n 1 Quintillion = 10^18\r\n 1 Sextillion = 10^21\r\n 1 Septillion = 10^24\r\n 1 Octillion = 10^27\r\n 1 Nonillion = 10^30\r\n 1 Decillion = 10^33\r\n 1 Vigintillion = 10^63\r\n 1 Centillion = 10^303\r\n","description_html":"\u003cp\u003eUsing the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e'10007.76' should return 'Ten thousand and seven point seven six'\r\n'001102.34' should return 'One thousand one hundred and two point three four'\r\n'.4' should return 'Zero point four'\r\n'4.' should return 'Four'\r\n'0.' and '.' should return 'Zero'\r\n'340,139' should return 'Three hundred and forty thousand one hundred and thirty nine'\r\n\u003c/pre\u003e\u003cp\u003eIn the British short scale notation:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 Hundred = 10^2\r\n1 Thousand = 10^3\r\n1 Million = 10^6\r\n1 Billion = 10^9\r\n1 Trillion = 10^12\r\n1 Quadrillion = 10^15\r\n1 Quintillion = 10^18\r\n1 Sextillion = 10^21\r\n1 Septillion = 10^24\r\n1 Octillion = 10^27\r\n1 Nonillion = 10^30\r\n1 Decillion = 10^33\r\n1 Vigintillion = 10^63\r\n1 Centillion = 10^303\r\n\u003c/pre\u003e","function_template":"function out_str = n2t(in_str)\r\n out_str = in_str;\r\nend","test_suite":"%%\r\nx = '0';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0.003';\r\ny_correct = 'Zero point zero zero three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0.';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '.343';\r\ny_correct = 'Zero point three four three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n%%\r\nx = '.';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx='23000000011.2330';\r\ny_correct = 'Twenty three billion and eleven point two three three zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '1,700,343.04014';\r\ny_correct = 'One million seven hundred thousand three hundred and forty three point zero four zero one four';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '4,003,900,030,001';\r\ny_correct = 'Four trillion three billion nine hundred million thirty thousand and one';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '50506';\r\ny_correct = 'Fifty thousand five hundred and six';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '5001000';\r\ny_correct = 'Five million one thousand';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '101000';\r\ny_correct = 'One hundred and one thousand';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '100003.0000341';\r\ny_correct = 'One hundred thousand and three point zero zero zero zero three four one';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '3.14159265358979';\r\ny_correct = 'Three point one four one five nine two six five three five eight nine seven nine';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0497, 367, 158, 401, 387, 490, 191, 320, 142.72931574943292322499550584316';\r\ny_correct = 'Four hundred and ninety seven septillion three hundred and sixty seven sextillion one hundred and fifty eight quintillion four hundred and one quadrillion three hundred and eighty seven trillion four hundred and ninety billion one hundred and ninety one million three hundred and twenty thousand one hundred and forty two point seven two nine three one five seven four nine four three two nine two three two two four nine nine five five zero five eight four three one six';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '10000000000000000000000000600000000000000000000000000000000000000';\r\ny_correct = 'Ten vigintillion six hundred thousand decillion';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n%%\r\nx = '2878366142600744685534686064407323355445697471111330511689595534';\r\ny_correct = 'Two vigintillion eight hundred and seventy eight octillion three hundred and sixty six septillion one hundred and forty two sextillion six hundred quintillion seven hundred and forty four quadrillion six hundred and eighty five trillion five hundred and thirty four billion six hundred and eighty six million sixty four thousand four hundred and seven decillion three hundred and twenty three nonillion three hundred and fifty five octillion four hundred and forty five septillion six hundred and ninety seven sextillion four hundred and seventy one quintillion one hundred and eleven quadrillion three hundred and thirty trillion five hundred and eleven billion six hundred and eighty nine million five hundred and ninety five thousand five hundred and thirty four';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '34000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001.580023';\r\ny_correct = 'Thirty four centillion and one point five eight zero zero two three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nfiletext = fileread('n2t.m');\r\nassert(isempty(strfind(filetext, 'regexp')));\r\nassert(isempty(strfind(filetext, 'assert')));\r\nassert(isempty(strfind(filetext, 'eval'))) \r\nassert(isempty(strfind(filetext, '!'))) \r\nassert(isempty(strfind(filetext, 'eighty seven trillion four'))) \r\nassert(isempty(strfind(filetext, 'three point zero four zero one four'))) \r\nassert(~isempty(strfind(filetext, 'three'))) \r\nassert(~isempty(strfind(filetext, 'n2t'))) \r\nassert(isempty(dir ('assert.m')))\r\nassert(isempty(dir ('fileread.m')))\r\nassert(isempty(dir ('isempty.m')))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":310490,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-20T13:01:17.000Z","updated_at":"2019-08-22T07:17:53.000Z","published_at":"2019-08-22T07:17:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['10007.76' should return 'Ten thousand and seven point seven six'\\n'001102.34' should return 'One thousand one hundred and two point three four'\\n'.4' should return 'Zero point four'\\n'4.' should return 'Four'\\n'0.' and '.' should return 'Zero'\\n'340,139' should return 'Three hundred and forty thousand one hundred and thirty nine']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the British short scale notation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 Hundred = 10^2\\n1 Thousand = 10^3\\n1 Million = 10^6\\n1 Billion = 10^9\\n1 Trillion = 10^12\\n1 Quadrillion = 10^15\\n1 Quintillion = 10^18\\n1 Sextillion = 10^21\\n1 Septillion = 10^24\\n1 Octillion = 10^27\\n1 Nonillion = 10^30\\n1 Decillion = 10^33\\n1 Vigintillion = 10^63\\n1 Centillion = 10^303]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47468,"title":"Slitherlink III: Evolve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.75px 7.91667px; transform-origin: 84.75px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink III: Evolve\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265.65px 7.91667px; transform-origin: 265.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges. Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.783px 7.91667px; transform-origin: 366.783px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv init solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n% show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv evolve solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb) %Keep evolving while there is any update to p\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n %insert code\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n %insert code\r\n end %i s3 3\r\n \r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n %insert code\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n %insert code\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n %insert code\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n %insert code\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n %insert code\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n %insert code\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n %insert code\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n %insert code\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n %insert code\r\n end % \r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n %insert code\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n %insert code\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n %insert code\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n %insert code\r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n %insert code\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n %insert code\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n %insert code\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n %insert code\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5) %Extend Red Bars where there is only 1 option\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars only if no prior evolves have updated p\r\n % insert code\r\n end % isequal p pb after cells, ends make no change\r\n \r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); %\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n '151';\r\n '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n '15551';\r\n '25055';\r\n '55253';\r\n '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5]; % Trivial 33\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T19:13:03.000Z","updated_at":"2020-11-12T23:28:07.000Z","published_at":"2020-11-12T23:28:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink III: Evolve\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges. Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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Circular Perfect Square Sequence ","description":"A sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u003cN\u003c52 as solutions beyond 51 may take significant processing time.","description_html":"\u003cp\u003eA sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u0026lt;N\u0026lt;52 as solutions beyond 51 may take significant processing time.\u003c/p\u003e","function_template":"function [v] = solve_PerfectSqr_Seq(n)\r\n v(1:n)=1:n;\r\nend","test_suite":"%%\r\n n=32;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));\r\n%%\r\n n=33;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));\r\n%%\r\n n=41;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));","published":true,"deleted":false,"likes_count":1,"comments_count":7,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-04-29T01:10:25.000Z","updated_at":"2026-01-29T06:26:26.000Z","published_at":"2012-04-29T01:10:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u0026lt;N\u0026lt;52 as solutions beyond 51 may take significant processing time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1173,"title":"Binpack Contest: Retro - - Best Packing","description":"The \u003chttp://www.mathworks.com/matlabcentral/contest/contests/3/rules Full Binpack Rules and examples\u003e.\r\n\r\nThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\r\n\r\nBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\r\n\r\n*Input:* [songList, mediaLength]\r\n\r\n*Output:* indexList\r\n\r\n*Example:*\r\n\r\nInput: [ 0.5 2 3 1.5 4], [5.6]\r\n\r\nOutput: [4 5] as 1.5+4 is very near and below 5.6.\r\n\r\nThe answer of [1 2 3] is also valid and also gives 5.5.\r\n\r\n*Scoring:* 1000*(Known_Best_Possibles - sum(songList(indexList))\r\n\r\n*Initial Leader:* Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\r\n\r\n\r\nFinal Score of 1 achieved in \u003c 6 Cody seconds for entry 10.\r\n\r\nFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\r\n\r\n","description_html":"\u003cp\u003eThe \u003ca href=\"http://www.mathworks.com/matlabcentral/contest/contests/3/rules\"\u003eFull Binpack Rules and examples\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\u003c/p\u003e\u003cp\u003eBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [songList, mediaLength]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e indexList\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInput: [ 0.5 2 3 1.5 4], [5.6]\u003c/p\u003e\u003cp\u003eOutput: [4 5] as 1.5+4 is very near and below 5.6.\u003c/p\u003e\u003cp\u003eThe answer of [1 2 3] is also valid and also gives 5.5.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 1000*(Known_Best_Possibles - sum(songList(indexList))\u003c/p\u003e\u003cp\u003e\u003cb\u003eInitial Leader:\u003c/b\u003e Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\u003c/p\u003e\u003cp\u003eFinal Score of 1 achieved in \u0026lt; 6 Cody seconds for entry 10.\u003c/p\u003e\u003cp\u003eFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\u003c/p\u003e","function_template":"function indexList = binpack_scr(songList,mediaLength)\r\n indexList=[1 2];\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',200);\r\n%%\r\nmediaLength=45;\r\nrng(0)\r\nsongList=floor(10000*rand(1,100))/10000;\r\nindexList = binpack_scr(songList,mediaLength) ;\r\nsum(songList(indexList))\r\n%[songList(indexList)' indexList']\r\nassert(sum(songList(indexList))\u003e44.8) % anti hardcode\r\n%%\r\na{1} = [4.3078 2.5481 1.4903 5.4302 3.4142 2.9736 3.3768 ...\r\n 2.1612 3.3024 0.3269 2.6761 4.2530 2.6648 1.9644 ...\r\n 3.3389 22.122 4.1015 3.2104 2.3945 4.7151];\r\na{2} = [1.2671 3.1377 4.0687 4.1459 3.6469 6.1881 8.2452 ...\r\n 7.3962 9.7071 10.4798 11.4082 12.2282 12.6320 13.9705 ...\r\n 13.8851 15.6195 17.0187 18.5778 18.4140 20.0473];\r\na{3} = [1.6283 6.0703 8.1323 2.6226 3.1230 3.0081 6.1405 ...\r\n 1.1896 4.2769 5.0951 6.4869 3.9215 2.5858 4.7130 ...\r\n 4.5529];\r\na{4} = [40:-1:1]+.1;\r\na{5} = [1.0979 3.5540 1.8627 0.0849 3.2110 3.6466 4.8065 ...\r\n 3.2717 0.1336 2.5008 0.4508 3.0700 3.1658 0.8683 ...\r\n 3.5533 3.7528 2.7802 4.2016 1.6372 9.6254 1.3264 ...\r\n 0.3160 4.3212 3.0192 0.7744 2.3970 1.7416 2.4751 ...\r\n 1.0470 1.9091];\r\na{6} = [1 1 2 3 5 8 13 21 34]+.1;\r\na{7} = [0.8651 3.3312 0.2507 0.5754 2.2929 2.3818 2.3783 ...\r\n 0.0753 0.6546 0.3493 0.3734 1.4516 1.1766 4.3664 ...\r\n 0.2728 20.279 2.1335 0.1186 0.1913 1.6647 0.5888 ...\r\n 2.6724 1.4286 3.2471 1.3836 1.7160 2.5080 3.1875 ...\r\n 2.8819 1.1423 0.7998 1.3800 1.6312 1.4238 2.5805 ...\r\n 1.3372 2.3817 2.4049 0.0396 0.3134];\r\na{8} = [pi*ones(1,10) exp(1)*ones(1,10)];\r\na{9} = [1.6041 0.2573 1.0565 1.4151 0.8051 0.5287 0.2193 ...\r\n 0.9219 2.1707 0.0592 1.0106 0.6145 0.5077 1.6924 ...\r\n 0.5913 0.6436 0.3803 1.0091 0.0195 0.0482 20.000 ...\r\n 0.3179 1.0950 1.8740 0.4282 0.8956 0.7310 0.5779 ...\r\n 0.0403 0.6771 0.5689 0.2556 0.3775 0.2959 1.4751 ...\r\n 0.2340 8.1184 0.3148 1.4435 0.3510 0.6232 0.7990 ...\r\n 0.9409 0.9921 0.2120 0.2379 1.0078 0.7420 1.0823 ...\r\n 0.1315];\r\na{10}= [1.6041 0.2573 1.0565 1.4151 0.8051 0.5287 0.2193 ...\r\n 0.9219 2.1707 0.0592 1.0106 0.6145 0.5077 1.6924 ...\r\n 0.5913 0.6436 0.3803 10.091 0.0195 0.0482 20.000 ...\r\n 0.3179 1.0950 1.8740 44.999 0.8956 0.7310 0.5779 ...\r\n 0.0403 0.6771 0.5689 0.2556 0.3775 0.2959 1.4751 ...\r\n 0.2340 0.1184 0.3148 1.4435 0.3510 0.6232 0.7990 ...\r\n 0.9409 0.9921 0.2120 0.2379 1.0078 0.7420 1.0823 ...\r\n 0.1315];\r\na{11}= [40*ones(1,50) ones(1,20)]+0.05;\r\na{12}= 4.3 + sin(1:100);\r\n\r\nmediaLength=45;\r\n\r\nnet_gap=0;\r\nt0=clock;\r\nfor j=1:1\r\nfor i=1:12\r\n songList=a{i};\r\n indexList = binpack_scr(songList,mediaLength) ;\r\n indexList=unique(indexList); % No dupes\r\n total(i)=sum(songList(indexList));\r\n if total(i)\u003e45+1.5*eps(mediaLength) % Rqmt \u003c= 45\r\n total(i)=-Inf;\r\n end\r\n net_gap=net_gap+45-total(i) ;\r\nend\r\nend\r\ntte=etime(clock,t0);\r\nfprintf('Total Time E %f\\n',tte)\r\nfprintf('Totals: ');fprintf('%.5f ',total);fprintf('\\n')\r\nfprintf('Net Gap: %.2f\\n',net_gap)\r\n%format long\r\nfprintf('Performance: %.4f\\n',net_gap/(12*45))\r\nfprintf('Score=1000*(12*45-1.76987514-sum(total): %.3f\\n',1000*(12*45-1.76987514-sum(total)))\r\nfprintf('Final Score %i\\n',round(1000*(12*45-1.76987514-sum(total))))\r\n\r\nScore=round(1000*(12*45-1.76987514-sum(total)));\r\n\r\nassert(Score\u003e=0)\r\n\r\n\r\nfeval( @assignin,'caller','score',floor(min( 200,Score )) );\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":"2013-01-06T00:41:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T16:28:33.000Z","updated_at":"2013-01-06T00:52:17.000Z","published_at":"2013-01-04T18:16:41.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/contest/contests/3/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFull Binpack Rules and examples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [songList, mediaLength]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e indexList\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: [ 0.5 2 3 1.5 4], [5.6]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: [4 5] as 1.5+4 is very near and below 5.6.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe answer of [1 2 3] is also valid and also gives 5.5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 1000*(Known_Best_Possibles - sum(songList(indexList))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInitial Leader:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFinal Score of 1 achieved in \u0026lt; 6 Cody seconds for entry 10.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":43086,"title":"Recursion at variable input","description":"input of any length\r\na =2\r\nb =2\r\nc =3\r\n\r\noutput = (a^b)^c = 64","description_html":"\u003cp\u003einput of any length\r\na =2\r\nb =2\r\nc =3\u003c/p\u003e\u003cp\u003eoutput = (a^b)^c = 64\u003c/p\u003e","function_template":"function y = pow2Pow(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 1;\r\nassert(isequal(pow2Pow(x),y_correct))\r\n%%\r\nx = 2;\r\ny =2;\r\ny_correct = 4;\r\nassert(isequal(pow2Pow(x,y),y_correct))\r\n%%\r\nx = 2;\r\ny= 2;\r\nz=3;\r\ny_correct = 64;\r\nassert(isequal(pow2Pow(x,y,z),y_correct))\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":0,"created_by":13865,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":67,"test_suite_updated_at":"2016-10-29T17:07:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-10-06T01:29:47.000Z","updated_at":"2026-03-11T08:26:55.000Z","published_at":"2016-10-06T01:31:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003einput of any length a =2 b =2 c =3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput = (a^b)^c = 64\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60618,"title":"ICFP2024 005: Lambdaman 1, 2, 3","description":"The ICFP2024 contest was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\r\nThe ICFP Language is based on Lambda Calculus.\r\nThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\r\nThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\r\nThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\r\n\r\nThe ICFP competition is more about manual solving optimizations for each unique problem.\r\nThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 315px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 157.5px; transform-origin: 407px 157.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/task.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2024 contest\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 300px 8px; transform-origin: 300px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/icfp.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP Language\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 40.5px 8px; transform-origin: 40.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is based on \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Lambda_calculus\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eLambda Calculus\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 356.5px 8px; transform-origin: 356.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.5px 8px; transform-origin: 357.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 285.5px 8px; transform-origin: 285.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP competition is more about manual solving optimizations for each unique problem.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [pathbest]=Lambdaman_123(m)\r\n Lmax=Inf;\r\n %m % Wall0 Lambda1 Cheese2 Eaten3\r\n [nr,nc]=size(m);\r\n adj=[-1 1 -nr nr]; % using index requires a wall ring around maze\r\n \r\n pathn=''; %UDLR\r\n ztic=tic;tmax=35; %Recursion time limit\r\n Lbest=inf;\r\n pathbest='';\r\n mstate=m(:)'; % recursion performs maze state checks to avoid dupolication\r\n mstaten=mstate;\r\n L=0;\r\n mn=m;\r\n Lidxn=find(m==1);\r\n [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L, ...\r\n pathn,mn,Lidxn,mstaten,Lmax); %use VARn for recursion updates\r\n\r\n toc(ztic)\r\nend %Lambda_123\r\n\r\nfunction [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L, ...\r\n path,m,Lidx,mstate,Lmax)\r\n\r\n%Conditional recursion aborts\r\n if toc(ztic)\u003etmax,return;end %Recursion time-out\r\n if L\u003eLmax,return;end % Limit recursion trials to known Lmax\r\n if L\u003e=Lbest,return;end % Bail on long solutions\r\n \r\n m(Lidx)=3;\r\n if nnz(m==2)==0 % Solution case. Better solution found\r\n Lbest=L;\r\n pathbest=path;\r\n toc(ztic)\r\n fprintf('Lbest=%i ',Lbest);fprintf('Path=%s',pathbest);fprintf('\\n\\n');\r\n return;\r\n end\r\n \r\n UDLR='UDLR';\r\n \r\n mn=m;\r\n Cadj=m(adj+Lidx);\r\n for i=1:4 % UDLR\r\n if Cadj(i)\u003e0 % Ignore into wall Cadj==0 movement\r\n Lidxn=Lidx+adj(i);\r\n mn(Lidxn)=1;\r\n mn_state=mn(:)';\r\n \r\n if nnz(sum(abs(mstate-mn_state),2)==0) % Pre-exist state check\r\n mn(Lidxn)=m(Lidxn); % Reset mn\r\n continue; %Abort when create an existing prior state\r\n end\r\n \r\n mstaten=[mstate;mn_state]; % When update walls re-init mstate\r\n pathn=[path UDLR(i)];\r\n \r\n [pathbest,Lbest]=maze_rec(ztic,tmax,adj,pathbest,Lbest,L+1, ...\r\n pathn,mn,Lidxn,mstaten,Lmax);\r\n \r\n mn(Lidxn)=m(Lidxn); % reset mn in fastest way\r\n end\r\n end % for UDLR\r\nend %maze_rec","test_suite":"%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 1 optimal solution L15 LLLDURRRUDRRURR\r\nms=['###.#...'\r\n '...L..##'\r\n '.#######'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 2 optimal solution L26 RDURRDDRRUUDDLLLDLLDDRRRUR\r\nms=['L...#.'\r\n '#.#.#.'\r\n '##....'\r\n '...###'\r\n '.##..#'\r\n '....##'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 3 optimal solution L40 DRDRLLLUDLLUURURLLURLUURRDRDRDRDUUUULDLU\r\nms=[ '......'\r\n '.#....'\r\n '..#...'\r\n '...#..'\r\n '..#L#.'\r\n '.#...#'\r\n '......'];\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nfor i=1:nr % Display maze numeric\r\n fprintf('%i',m(i,:));fprintf('\\n');\r\nend\r\n\r\nv = Lambdaman_123(m);\r\nfprintf('Answer Length: %i\\n',length(v));\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n valid=1;\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n for i=1:nr % Display maze numeric\r\n fprintf('%i',mc(i,:));fprintf('\\n');\r\n end\r\nend\r\n\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-07-13T05:35:58.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-07-13T05:05:28.000Z","updated_at":"2026-03-11T09:46:10.000Z","published_at":"2024-07-13T05:35:58.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/task.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2024 contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/icfp.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP Language\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is based on \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lambda_calculus\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLambda Calculus\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Lambdaman 1, 2, and 3 mazes are small matrices L at various indices, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest's best Lambdaman1, 2, and 3 solutions take 15, 26, and 40 U/R/D/L commands, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP competition is more about manual solving optimizations for each unique problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve Lamdaman mazes 1, 2 and 3 by eating all the cheese via a char path of UDLR, with a common program smaller than the template. The template implements a near brute force recursion with a time limit. Optimal solutions are not required.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1827,"title":"Negation the hard way","description":"Write a function that has the following property: f(f(x)) = -x for any numeric array x.\r\n\r\nNote that there is no restriction on the output for f(x). How bizarre can you make it?","description_html":"\u003cp\u003eWrite a function that has the following property: f(f(x)) = -x for any numeric array x.\u003c/p\u003e\u003cp\u003eNote that there is no restriction on the output for f(x). How bizarre can you make it?\u003c/p\u003e","function_template":"function y = neg2(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = rand(2,2,2);\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = [];\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = sqrt(-5);\r\ny = neg2(neg2(x));\r\ny_correct = -x;\r\nassert(isequal(y,y_correct))\r\n\r\n%% What does a single call give?\r\nx = rand;\r\ndisp(neg2(x))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":1011,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":71,"test_suite_updated_at":"2013-08-15T02:58:27.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-08-14T22:59:34.000Z","updated_at":"2026-02-26T12:24:27.000Z","published_at":"2013-08-14T22:59:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that has the following property: f(f(x)) = -x for any numeric array x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that there is no restriction on the output for f(x). How bizarre can you make it?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44543,"title":"Normie Function","description":"So, I built a function and gave it a name- _Normie_.\r\n*Find the nth term of Normie function:*\r\n_f(n)= 1*f(n-1)+ 2*f(n-3)+ 3_ , *when n\u003e3* and _0_ , *when n\u003c=3*.","description_html":"\u003cp\u003eSo, I built a function and gave it a name- \u003ci\u003eNormie\u003c/i\u003e. \u003cb\u003eFind the nth term of Normie function:\u003c/b\u003e \u003ci\u003ef(n)= 1*f(n-1)+ 2*f(n-3)+ 3\u003c/i\u003e , \u003cb\u003ewhen n\u0026gt;3\u003c/b\u003e and \u003ci\u003e0\u003c/i\u003e , \u003cb\u003ewhen n\u0026lt;=3\u003c/b\u003e.\u003c/p\u003e","function_template":"function y = nth_term(n)\r\n y = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 2;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 3;\r\ny_correct = 0;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 4;\r\ny_correct = 3;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 5;\r\ny_correct = 6;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 10;\r\ny_correct = 93;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 11;\r\ny_correct = 162;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 20;\r\ny_correct = 18753;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 35;\r\ny_correct = 51651090;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 50;\r\ny_correct = 142236278205;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 70;\r\ny_correct = 5490159117130629;\r\nassert(isequal(nth_term(n),y_correct))\r\n%%\r\nn = 75;\r\ny_correct = 76953534045721408;\r\nassert(isequal(nth_term(n),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":104442,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":"2018-03-28T11:14:13.000Z","rescore_all_solutions":false,"group_id":61,"created_at":"2018-03-21T19:10:33.000Z","updated_at":"2026-03-16T11:15:12.000Z","published_at":"2018-03-21T19:30:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo, I built a function and gave it a name-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNormie\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFind the nth term of Normie function:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ef(n)= 1*f(n-1)+ 2*f(n-3)+ 3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewhen n\u0026gt;3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewhen n\u0026lt;=3\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":58748,"title":"Fibonacci Sequence","description":"Write a MATLAB function called fibonacci_sequence(n) that takes an integer n as input and returns the first n terms of the Fibonacci sequence as an array.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 42px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407.5px 21px; transform-origin: 407.5px 21px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 21px; text-align: left; transform-origin: 384.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a MATLAB function called \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003efibonacci_sequence(n)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e that takes an integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as input and returns the first \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e terms of the Fibonacci sequence as an array.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = fibonacci_sequence(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = [0];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n%%\r\nx = 2;\r\ny_correct = [0, 1];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n%%\r\nx = 6;\r\ny_correct = [0, 1, 1, 2, 3, 5];\r\nassert(isequal(fibonacci_sequence(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3494988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":23,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-07-18T20:37:26.000Z","updated_at":"2026-03-11T08:32:57.000Z","published_at":"2023-07-18T20:37:26.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a MATLAB function called \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efibonacci_sequence(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that takes an integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as input and returns the first \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e terms of the Fibonacci sequence as an array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":48990,"title":"Solve the recursion","description":"Solve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\r\nf(1)=4;\r\nf(2)=8;","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 81px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 40.5px; transform-origin: 407px 40.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 141px 8px; transform-origin: 141px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSolve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef(1)=4;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ef(2)=8;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rec(n)\r\n y = n;\r\nend","test_suite":"%%\r\nfiletext = fileread('rec.m');\r\nillegal = contains(filetext, 'switch') || contains(filetext, 'else') \r\nassert(~illegal)\r\n%%\r\nn = 1;\r\ny_correct = 4;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 5;\r\ny_correct = 44;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 13;\r\ny_correct = 2204;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 20;\r\ny_correct = 64076;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 34;\r\ny_correct = 54018518;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 37;\r\ny_correct = 228826124;\r\nassert(isequal(rec(n),y_correct))\r\n%%\r\nn = 40;\r\ny_correct = 969323026;\r\nassert(isequal(rec(n),y_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":698530,"edited_by":223089,"edited_at":"2022-12-12T06:11:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":38,"test_suite_updated_at":"2022-12-12T06:11:46.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-12-22T17:50:06.000Z","updated_at":"2026-03-23T17:38:43.000Z","published_at":"2020-12-31T01:21:21.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve the recursion: f(n)=f(n-1)+1 + f(n-2)+2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef(1)=4;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ef(2)=8;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1836,"title":"Negation and new variables","description":"Inspired by Problem 1827 by Andrew Newell.\r\n\r\nWrite a function that has the following property: \r\n\r\n(x~=y)\r\n\r\nneg3(x)=x;\r\n\r\nneg3(y)=y;\r\n\r\nneg3(x)=-x;\r\n\r\nneg3(y)=-y;\r\n\r\n...\r\n\r\nThe test suite is simple. The goal is just to obtain a general method.","description_html":"\u003cp\u003eInspired by Problem 1827 by Andrew Newell.\u003c/p\u003e\u003cp\u003eWrite a function that has the following property:\u003c/p\u003e\u003cp\u003e(x~=y)\u003c/p\u003e\u003cp\u003eneg3(x)=x;\u003c/p\u003e\u003cp\u003eneg3(y)=y;\u003c/p\u003e\u003cp\u003eneg3(x)=-x;\u003c/p\u003e\u003cp\u003eneg3(y)=-y;\u003c/p\u003e\u003cp\u003e...\u003c/p\u003e\u003cp\u003eThe test suite is simple. The goal is just to obtain a general method.\u003c/p\u003e","function_template":"function y = neg3(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny1=-1;\r\nneg3(neg3(x));\r\nassert(isequal(ans,y1))\r\n\r\n%%\r\nx=2;\r\ny=3;\r\nz=4;\r\nneg3(x);\r\nassert(isequal(ans,2))\r\nneg3(y);\r\nassert(isequal(ans,3))\r\nneg3(x);\r\nassert(isequal(ans,-2))\r\nneg3(z);\r\nassert(isequal(ans,4))\r\nneg3(x);\r\nassert(isequal(ans,2))\r\nneg3(z);\r\nassert(isequal(ans,-4))\r\n\r\n%%\r\nx=0;\r\ny=10;\r\nneg3(x);\r\nassert(isequal(ans,0))\r\nneg3(x);\r\nassert(isequal(ans,0))\r\nneg3(y);\r\nassert(isequal(ans,10))\r\nneg3(y);\r\nassert(isequal(ans,-10))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-08-19T17:05:10.000Z","updated_at":"2013-08-19T17:18:06.000Z","published_at":"2013-08-19T17:09:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by Problem 1827 by Andrew Newell.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that has the following property:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(x~=y)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(x)=x;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(y)=y;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(x)=-x;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eneg3(y)=-y;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe test suite is simple. The goal is just to obtain a general method.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":292,"title":"Infernal Recursion","description":"Consider the recursion relation:\r\n\r\nx_n = (x_(n-1)*x_(n-2))^k\r\n\r\nGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\r\n\r\nFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\r\n\r\n R = get_recurse(exp(1),pi,5,4/9)\r\n\r\n R =\r\n 2.31187497992966\r\n","description_html":"\u003cp\u003eConsider the recursion relation:\u003c/p\u003e\u003cp\u003ex_n = (x_(n-1)*x_(n-2))^k\u003c/p\u003e\u003cp\u003eGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\u003c/p\u003e\u003cp\u003eFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eR = get_recurse(exp(1),pi,5,4/9)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eR =\r\n 2.31187497992966\r\n\u003c/pre\u003e","function_template":"function xn = get_recurse(x1,x2,n,k)\r\n xn = x1+x2+n+k;\r\nend","test_suite":"%%\r\nassert(abs(get_recurse(exp(1),pi,5,7/9)-20.066097534719034)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(1.01,1.02,5,pi)-4.1026063901404743)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(3.3,1,5,1/2)-1.5647419554132411)\u003c100*eps)\r\n%%\r\nassert(abs(get_recurse(8,9,35,5/11)-1.3002291773509134)\u003c1000*eps)\r\n%%\r\nassert(abs(get_recurse(2,5,50,10/21)-1.3133198875358512)\u003c1000*eps)\r\n%%\r\nassert(abs(get_recurse(1.5,2,600,1/2)-1.8171205928321394)\u003c1000*eps)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":459,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":84,"test_suite_updated_at":"2012-02-09T03:52:52.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-02-09T01:44:32.000Z","updated_at":"2025-11-20T19:38:34.000Z","published_at":"2012-02-09T04:26:02.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider the recursion relation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_n = (x_(n-1)*x_(n-2))^k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven x_1, x_2, and k, x_n can be found by this definition. Write a function which takes as input arguments x_1, x_2, n and k. The output should be x_n.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if x_1=exp(1), x_2=pi, n=5, and k=5/9 then:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[R = get_recurse(exp(1),pi,5,4/9)\\n\\nR =\\n 2.31187497992966]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46591,"title":"Ackerman Function","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 111px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 55.5px; transform-origin: 407px 55.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 193.5px 8px; transform-origin: 193.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite the ackerman function, which conforms to 3 constraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48px 8px; transform-origin: 48px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eack(0, n) = n+1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eack(m, 0) = ack(m-1, 1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 105.5px 8px; transform-origin: 105.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"perspective-origin: 48.5px 8px; transform-origin: 48.5px 8px; \"\u003eack(m, n) = ack\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2.5px 8.5px; transform-origin: 2.5px 8.5px; \"\u003e(\u003c/span\u003e\u003cspan style=\"perspective-origin: 52px 8px; transform-origin: 52px 8px; \"\u003em-1, ack(m, n-1)\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2.5px 8.5px; transform-origin: 2.5px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function a = ack(m, n)\r\n a = 1;\r\nend","test_suite":"%%\r\nm = 1;\r\nn = 1;\r\na_correct = 3;\r\nassert(isequal(ack(m,n),a_correct))\r\n\r\n%%\r\nm = 3;\r\nn = 4;\r\na_correct = 125;\r\nassert(isequal(ack(m,n),a_correct))\r\n\r\n%%\r\nm = 3;\r\nn = 2;\r\na_correct = 29;\r\nassert(isequal(ack(m,n),a_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":515893,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-09-17T20:53:54.000Z","updated_at":"2020-09-17T20:53:54.000Z","published_at":"2020-09-17T20:53:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite the ackerman function, which conforms to 3 constraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(0, n) = n+1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(m, 0) = ack(m-1, 1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eack(m, n) = ack(m-1, ack(m, n-1))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60642,"title":"ICFP2024 010: Lambdaman Optimal-Crawler-Backfill","description":"The ICFP2024 contest was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\r\nThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\r\nThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\r\nShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\r\n\r\nThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\r\nMaze#/Crawler/OptimalCrawler \r\n1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\r\nThese are believed to be optimal solutions. Post in comments if any are beat.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 786px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 393px; transform-origin: 407px 393px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2024.github.io/task.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP2024 contest\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 300px 8px; transform-origin: 300px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 317.5px 8px; transform-origin: 317.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 357.5px 8px; transform-origin: 357.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371px 8px; transform-origin: 371px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 420px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 210px; text-align: left; transform-origin: 384px 210px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: middle;width: 560px;height: 420px\" 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\" data-image-state=\"image-loaded\" width=\"560\" height=\"420\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 98.5px 8px; transform-origin: 98.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMaze#/Crawler/OptimalCrawler \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 313.5px 8px; transform-origin: 313.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 242.5px 8px; transform-origin: 242.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThese are believed to be optimal solutions. Post in comments if any are beat.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [pathbest]=crawler_fill(m)\r\n% This is Optimal crawler_fill\r\n% Optimal Crawler with backfill will solve non-loop mazes with path width of 1\r\n% At intersections the path, UDLR, that is least deep to fill is selected\r\n% Recursive fast move if only one cheese adjacent or one open path\r\n% Backfill L spot if 3 adj are Wall 0\r\n\r\n%crawler 1/15 2/33 4/394/.09s\r\n%11[103x103]/9988/.33s 12[101x101]/9992 13/9976 14/9994 15/9986/.33s\r\n%Optimal crawler 1/15 2/26 4/348 11/9622/.9s 12/9626 13/9562 14/9478 15/9584\r\n\r\n pathv=zeros(1000,1); pathvptr=0;\r\n %zmap=[0 0 0;1 0 0;0 1 0;0 0 1]; \r\n \r\n [nr,nc]=size(m);\r\n adj=[-1 1 -nr nr]; % UDLR 1234\r\n \r\n %figure(1);image(reshape(m+1,nr,nc));colormap(zmap);axis equal;axis tight\r\n %pause(0.05)\r\n \r\n Lidx=find(m==1);\r\n ztic=tic;\r\n while nnz(m==2)\u003e0\r\n %if toc(ztic)\u003e120\r\n % fprintf('ztic Timeout\\n');\r\n % break;\r\n %end\r\n \r\n vadj=m(adj+Lidx);\r\n m(Lidx)=3;\r\n \r\n [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr);\r\n \r\n if nnz(m==2)==0 %All cheezy bits eaten check post evolve\r\n break;\r\n end\r\n \r\n %Create path lengths in parallel to UDLR pu,pd,pl,pr\r\n %evolve all four until one active path has no growth. This is branch to take\r\n %dir will be called dptr 1 2 3 4\r\n mUDLR=m;\r\n mUDLR(m\u003e0)=inf;\r\n mUDLR(Lidx)=1;\r\n mU=mUDLR;\r\n \r\n % Use cell arrays mUDLRc{1} mU {2} mD {3}mL {4}mR \r\n mUDLRc{4}=[];\r\n Mdepth=zeros(1,4);\r\n depth=2;\r\n for i=1:4 % Initialize mUDLRc{i}\r\n mUDLRc{i}=mU; % all same start UDLR\r\n mUDLRc{i}(Lidx+adj(i))=min(mUDLRc{i}(Lidx+adj(i)),depth);\r\n if nnz(mUDLRc{i}==depth)\r\n Mdepth(i)=depth;\r\n end\r\n end\r\n \r\n % depth=2 at entry with at least 2 at depth 2\r\n nnzMdepth=nnz(Mdepth); % Base active paths\r\n while nnz(Mdepth==depth)==nnzMdepth % 0012 stop 1223 stop 0022 0022 stop\r\n pdepth=depth;\r\n depth=depth+1;\r\n for i=1:4\r\n if Mdepth(i)==0,continue;end % matrix i never grew\r\n gptr=find(mUDLRc{i}==pdepth)';\r\n for j=gptr\r\n mUDLRc{i}(j+adj)=min(mUDLRc{i}(j+adj),depth); % grow UDLR from each new point\r\n end % j gptr\r\n if nnz(mUDLRc{i}==depth) % Search for any new placements, cant use max as use Inf for path\r\n Mdepth(i)=depth;\r\n end\r\n end % i mUDLRc\r\n \r\n if nnz(Mdepth==depth)\u003cnnzMdepth % Some path group ended\r\n dptr=find(Mdepth==depth-1,1,'first'); %New direction\r\n end\r\n \r\n end % while nnz Mdepth depth\r\n \r\n Lidx=Lidx+adj(dptr);\r\n m(Lidx)=1;\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=dptr;\r\n \r\n end % while m==2\r\n \r\n UDLR='UDLR';\r\n if nnz(m==2)\u003e0\r\n pathbest=UDLR(pathv(1:pathvptr));\r\n fprintf('BestPath:');fprintf('%s',pathbest);fprintf(' Uneaten:%i\\n',nnz(m==2));fprintf('\\n')\r\n else\r\n pathbest=UDLR(pathv(1:pathvptr));\r\n fprintf('Solved Path:');fprintf('%s',pathbest);fprintf('\\nLength:%i\\n',length(pathbest));\r\n end\r\n \r\n %figure(4);image(reshape(m+1,nr,nc));colormap(zmap);axis equal;axis tight\r\nend % crawler_fill\r\n \r\nfunction [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr)\r\n vadj=m(adj+Lidx);\r\n update=0;\r\n while nnz(vadj==0)==3 % serial cul-de-sac exit; speed\r\n m(Lidx)=0; % cul-de-sac Backfill\r\n ptr=find(vadj\u003e0,1,'first');\r\n Lidx=Lidx+adj(ptr);\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=ptr;\r\n vadj=m(adj+Lidx);\r\n update=1;\r\n end % while cul-de-sac\r\n \r\n while nnz(vadj==2)==1 % serial tunnel cul-de-sac; speed\r\n m(Lidx)=3; % movement update\r\n ptr=find(vadj==2,1,'first');\r\n Lidx=Lidx+adj(ptr);\r\n pathvptr=pathvptr+1;\r\n pathv(pathvptr)=ptr;\r\n vadj=m(adj+Lidx);\r\n update=1;\r\n end % while cul-de-sac\r\n m(Lidx)=3;\r\n \r\n if update\r\n [Lidx,m,pathv,pathvptr]=evolve(Lidx,m,adj,pathv,pathvptr); \r\n end\r\n \r\nend % evolve\r\n ","test_suite":"%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 1 optimal solution L15\r\n ms=['###.#...'\r\n '...L..##'\r\n '.#######'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=15 % Lambda1 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=15 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 2 optimal solution L26\r\n ms=[ ...\r\n 'L...#.'\r\n '#.#.#.'\r\n '##....'\r\n '...###'\r\n '.##..#'\r\n '....##'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=26 % Lambda2 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=26 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n\r\n%Lambdaman 4 optimal solution L348 DDLLRRUULLUUUUDDDDLLUULLUURRLLDDRRDDRRRRRRLLUUUURRRRRRDDRRRRUURRLLDDRRLLLLLLUURRLLLLLLLLDDRRRRDDDDLLRRDDLLDDLLUUDDRRDDRRLLDDLLDDDDUUUUUULLDDDDDDLLRRUULLLLUURRUUDDLLDDDDUURRRRUUUUUULLUUUUDDRRLLDDLLUUUUUUDDDDDDDDUURRRRDDRRDDRRDDDDUURRDDUURRDDUULLLLUUUUUUUURRUURRRRLLUURRRRRRLLDDRRDDDDDDDDLLRRUULLRRUUUULLLLRRDDLLLLUUDDLLRRDDRRDDLLLLRRRRDDDDRRRRLLUURR\r\n ms=[ ...\r\n'...#.#.........#...'\r\n'.###.#.#####.###.##'\r\n'...#.#.....#.......'\r\n'##.#.#.###.########'\r\n'.#....L..#.#.......'\r\n'.#####.###.#.###.##'\r\n'.#.#...#.......#...'\r\n'.#.#######.#######.'\r\n'.#...#.#...#.#.....'\r\n'.#.###.#.###.###.#.'\r\n'.....#...#.......#.'\r\n'.###.###.###.#####.'\r\n'.#.#...#...#...#...'\r\n'##.#.#.#.#####.###.'\r\n'...#.#...#.....#...'\r\n'.###.#.#.#####.####'\r\n'.....#.#.....#.#...'\r\n'.###.#.#.#.#.#.#.##'\r\n'.#...#.#.#.#.#.....'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=348 % Lambda4\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=348 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 11[103x103]/9988/.33s crawler\r\n%Lambdaman 11 optimal solution 9622\r\n ms=[ 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.#.......#.#...#...#.#.#.#...#...........#.#.#.#...#.......#'\r\n'#.#.###.#####.#.#######.###.#######.#.#.#.#####.###.#######.#.###.#####.#.###.#####.#######.#####.#.#'\r\n'#.#.....#.#.......#.........#.......#.#.#...#.........#.....#...#...#...#.#.#...#.........#...#...#.#'\r\n'###.###.#.#######.#.###.#######.#.#.#.###.#.#######.###.#.#.###.#.#########.#.#.###.#####.#####.###.#'\r\n'#...#...#...#...#...#...#.#.....#.#.#...#.#.........#...#.#...#.#...#.........#...#.#.#.#.#.....#.#.#'\r\n'###.#.###.###.#.###.#####.#######.#.#.###.###.#######.###########.#.#.###.#######.#.#.#.#.###.#.#.###'\r\n'#...#.........#.#...........#.....#.#.#.....#...#...............#.#...#.....#.....#...#.......#.....#'\r\n'#####################################################################################################'];\r\n\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9622 % Lambda11 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9622 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 12[101x101]/9994\r\n%Lambdaman 12 optimal solution 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##.#.###.####.........#...#.......#.......#.....#.....#.#.....#.....#.#.....#...#.....#.....#.....#...#...#.....######################################################################################################';\r\nms=reshape(s12,101,101)';\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9626 % Lambda12 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9626 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 13[101x101]/9994\r\n%Lambdaman 13 optimal solution 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%Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9562 % Lambda13 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9562 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 14[101x101]/9994\r\n%Lambdaman 14 optimal solution 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##.#.#.###.##.......#.#.........#.#...#.....#.........#...#.#...#...........#...#.....#...#...#...#.....#.....#.######################################################################################################';\r\nms=reshape(s14,101,101)';\r\n[nr,nc]=size(ms);\r\nmb=ones(nr,nc)*2; %Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9478 % Lambda14 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9478 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n\r\nvalid=0;\r\n% L lambdaman 1, . Cheese 2, # Wall 0\r\n% 15[101x101]/9986/.33s\r\n%Lambdaman 15 optimal solution 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%Cheese bits are 2.\r\nmb(ms=='#')=0; % Wall\r\nmb(ms=='L')=1; % Landaman, start point\r\nm=zeros(nr+2,nc+2);\r\nm(2:end-1,2:end-1)=mb; %Wall surrounded maze\r\n[nr,nc]=size(m);\r\n\r\nzmap=[0 0 0;1 0 0;0 1 0;0 0 1]; % maps to 1:4\r\nfigure;image(m+1);colormap(zmap);axis equal;axis tight\r\n\r\n%for i=1:nr % Display maze numeric\r\n% fprintf('%i',m(i,:));fprintf('\\n');\r\n%end\r\n\r\nztic=tic;\r\nv = crawler_fill(m);\r\n\r\nfprintf('Answer Length: %i Time:%.2f\\n',length(v),toc(ztic));\r\n%fprintf('Path:');fprintf('%s',v);fprintf('\\n')\r\n\r\nmc=m==2; %Create cheese binary matrix for processing path coverage\r\n\r\n[r,c]=find(m==1); % Lambdaman\r\nfor i=1:length(v)\r\n if v(i)=='R' % R\r\n if c+1\u003c=nc\r\n if m(r,c+1)\u003e0\r\n c=c+1;\r\n end\r\n end\r\n elseif v(i)=='L' % L\r\n if c-1\u003e=1\r\n if m(r,c-1)\u003e0\r\n c=c-1;\r\n end\r\n end\r\n elseif v(i)=='U' % U\r\n if r-1\u003e=1\r\n if m(r-1,c)\u003e0\r\n r=r-1;\r\n end\r\n end\r\n elseif v(i)=='D' % D\r\n if r+1\u003c=nr\r\n if m(r+1,c)\u003e0\r\n r=r+1;\r\n end\r\n end\r\n end\r\n mc(r,c)=0; \r\n if nnz(mc)==0,break;end\r\nend\r\n\r\nif nnz(mc)==0\r\n if length(v)\u003c=9584 % Lambda15 crawler\r\n valid=1;\r\n else\r\n fprintf('Length \u003c=9584 required. Given length:%i\\n',length(v));\r\n end\r\nelse\r\n fprintf('Failed to Clear - remaining cheesy bits\\n');\r\n %for i=1:nr % Display maze numeric\r\n % fprintf('%i',mc(i,:));fprintf('\\n');\r\n %end\r\nend\r\n\r\nassert(valid)\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-07-17T17:24:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-07-17T16:18:16.000Z","updated_at":"2025-12-12T15:14:10.000Z","published_at":"2024-07-17T17:24:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2024.github.io/task.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP2024 contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e was held June29 thru July 1. The contest consisted of five parts: ICFP Language, Lambdaman maze, Starship flying, 3D - graph programming, and Efficiency - processing complex ICFP message to a numerical value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Lambdaman 4 maze is medium size,21x21, L near top left, '.' a cheese bit, # is Wall. Matrix uses Wall=0,L=1,Cheese=2. Encircling Walls are added to all mazes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest goal was to write a minimal size, bytes, expression that moves L, Lambdaman, to eat each cheese bit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShown is Lambdaman4 with a best known solution is 348 U/R/D/L commands by completing the lower left before lower right. This challenge requires an Optimal Crawler-Backfill method for paths width==1 and there are no loops.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"420\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"560\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"middle\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve multiple Lamdaman mazes by eating all the cheese via a char path of UDLR, with a program smaller than the template. The template implements an Optimal Crawler-Backfill with recursions for speed where only one choice possible. Optimal checks all viable move directions from an intersection and selects shortest to fill. Fill smallest branch first to minimize total length. The challenge is to make a smaller optimal crawler.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMaze#/Crawler/OptimalCrawler \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1/15/15 2/33/26 4/394/348 11/9988/9622 12/9992/9626 13/9976/9562 14/9994/9478 15/9986/9584\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThese are believed to be optimal solutions. Post in comments if any are beat.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 81.7333px; transform-origin: 404px 81.7333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 284px 8.5px; tab-size: 4; transform-origin: 284px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e Generate \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 244px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 244px 8.5px; \"\u003ethe first k terms in the sequence a(n) define recursively by \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 188px 8.5px; tab-size: 4; transform-origin: 188px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 116px 8.5px; transform-origin: 116px 8.5px; \"\u003e a(n+1)=p*a(n)+(1+a(n)) with \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 72px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 72px 8.5px; \"\u003ep=0.9 and a(1)=0.5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003eTest \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003eCase\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e n = 2;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 160px 8.5px; tab-size: 4; transform-origin: 160px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e a = [a(1) a(2)] = [0.5000 1.9500]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function out = rec_fun(x)\r\n p = 0.9;\r\n out(1) = 0.5;\r\nend","test_suite":"%%)\r\nx = 1;\r\ny_correct = 0.5;\r\nassert(abs(rec_fun(x) - y_correct) \u003c 1e-4)\r\n\r\n%%\r\nx = 5;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851];\r\nassert(sum(abs(rec_fun(x) - y_correct)) \u003c 1e-4)\r\n\r\n%%\r\nx = 7;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851 38.7816 74.6850];\r\nassert(sum(abs(rec_fun(x) - y_correct)) \u003c 1e-4)\r\n\r\n%%\r\nx = 9;\r\ny_correct = [0.5000 1.9500 4.7050 9.9395 19.8851 38.7816 74.6850 142.9016 272.5130];\r\nassert(all(abs(rec_fun(x) - y_correct) \u003c 1e-4))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":7,"created_by":44015,"edited_by":223089,"edited_at":"2023-03-07T10:35:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":45,"test_suite_updated_at":"2023-03-07T10:33:48.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-10-03T06:35:03.000Z","updated_at":"2025-12-12T06:32:21.000Z","published_at":"2015-10-03T06:36:26.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Generate the first k terms in the sequence a(n) define recursively by \\n\\n a(n+1)=p*a(n)+(1+a(n)) with p=0.9 and a(1)=0.5\\n\\nTest Case\\n\\n n = 2;\\n a = [a(1) a(2)] = [0.5000 1.9500]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2040,"title":"Additive persistence","description":"Inspired by Problem 2008 created by Ziko.\r\n\r\nIn mathematics, the persistence of a number is the *number of times* one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\r\n\r\nProblem 2008 is an example of multiplicative persistence.\r\nCan you code an additive persistence ?\r\n\r\n2718-\u003e2+7+1+8=18-\u003e1+8=9. So the persistence of 2718 is 2.\r\n\r\nYou can use the tips : num2str(666)-'0'=[6 6 6].\r\n\r\n","description_html":"\u003cp\u003eInspired by Problem 2008 created by Ziko.\u003c/p\u003e\u003cp\u003eIn mathematics, the persistence of a number is the \u003cb\u003enumber of times\u003c/b\u003e one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\u003c/p\u003e\u003cp\u003eProblem 2008 is an example of multiplicative persistence.\r\nCan you code an additive persistence ?\u003c/p\u003e\u003cp\u003e2718-\u0026gt;2+7+1+8=18-\u0026gt;1+8=9. So the persistence of 2718 is 2.\u003c/p\u003e\u003cp\u003eYou can use the tips : num2str(666)-'0'=[6 6 6].\u003c/p\u003e","function_template":"function y = add_persistence(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 0;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=18;\r\ny_correct = 1;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=2718;\r\ny_correct = 2;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=199;\r\ny_correct = 3;\r\nassert(isequal(add_persistence(x),y_correct))\r\n%%\r\nx=100;\r\ny_correct = 1;\r\nassert(isequal(add_persistence(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":1,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":184,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":38,"created_at":"2013-12-11T08:33:55.000Z","updated_at":"2026-03-31T17:48:57.000Z","published_at":"2013-12-11T08:33:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by Problem 2008 created by Ziko.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn mathematics, the persistence of a number is the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enumber of times\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e one must apply a given operation to an integer before reaching a fixed point; where further application does not change the number any more (Wikipedia).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2008 is an example of multiplicative persistence. Can you code an additive persistence ?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2718-\u0026gt;2+7+1+8=18-\u0026gt;1+8=9. So the persistence of 2718 is 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can use the tips : num2str(666)-'0'=[6 6 6].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44073,"title":"Fractal: area and perimeter of Koch snowflake","description":"Starting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration? \r\n\r\nFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\r\n\r\nRef: \u003chttps://en.wikipedia.org/wiki/Koch_snowflake\u003e","description_html":"\u003cp\u003eStarting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration?\u003c/p\u003e\u003cp\u003eFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\u003c/p\u003e\u003cp\u003eRef: \u003ca href = \"https://en.wikipedia.org/wiki/Koch_snowflake\"\u003ehttps://en.wikipedia.org/wiki/Koch_snowflake\u003c/a\u003e\u003c/p\u003e","function_template":"function [Area,Perimeter] = KochSnowFlake(side,nth)\r\n Area = 1;\r\n Perimeter = 1;\r\nend","test_suite":"%%\r\nside = 10; nth = 0;\r\nArea_correct = 43;\r\nPerimeter_correct = 30;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n\r\n%%\r\nside = 10; nth = 1;\r\nArea_correct = 58;\r\nPerimeter_correct = 40;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n\r\n%%\r\nside = 10; nth = 20;\r\nArea_correct = 69;\r\nPerimeter_correct = 9460;\r\n[myArea,myPerimeter] = KochSnowFlake(side,nth)\r\nassert(isequal(myArea, Area_correct))\r\nassert(isequal(myPerimeter, Perimeter_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":115733,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":49,"test_suite_updated_at":"2017-02-16T21:55:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2017-02-14T01:44:26.000Z","updated_at":"2026-01-07T20:59:16.000Z","published_at":"2017-02-14T01:44:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStarting from an equilateral triangle with side 's', what is the area and perimeter of Koch snowflake at n'th recursive iteration?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor given 's' and 'n' as function input, return 'Area' and 'Perimeter' of snowflake. (round to nearest integer for simplicity)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRef:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Koch_snowflake\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Koch_snowflake\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47385,"title":"Find Logic 28","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 251.571px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 174px 125.786px; transform-origin: 174px 125.786px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGuess the logic!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(1) = 21\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(2) = 25\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(3) = 22\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(4) = 38\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(5) = 33\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.9524px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 10.4762px; text-align: left; transform-origin: 151px 10.4762px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elogic(6) = 69\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 41.9048px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 151px 20.9524px; text-align: left; transform-origin: 151px 20.9524px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eMake a function logic(x) which will written 'x' th term of sequence.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = logic(x)\r\n y = 21;\r\nend","test_suite":"%%\r\nx = 1;\r\ny_correct = 21;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 3;\r\ny_correct = 22;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 4;\r\ny_correct = 38;\r\nassert(isequal(logic(x),y_correct))\r\n\r\n%%\r\nx = 5;\r\ny_correct = 33;\r\nassert(isequal(logic(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":293792,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":200,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-06T17:21:56.000Z","updated_at":"2026-02-19T09:52:52.000Z","published_at":"2020-11-06T17:21:56.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGuess the logic!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(1) = 21\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(2) = 25\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(3) = 22\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(4) = 38\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(5) = 33\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elogic(6) = 69\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMake a function logic(x) which will written 'x' th term of sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":54390,"title":"That's not my hat! ","description":"There exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees n\u003e0, the function c computes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 126px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 63px; transform-origin: 407px 63px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThere exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003en\u0026gt;0\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ec \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ecomputes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = c(n)\r\n y = 0;\r\nend","test_suite":"%%\r\nn = 7;\r\ny_correct = 1854;\r\nassert(isequal(c(n),y_correct))\r\n\r\n%%\r\nn = 10;\r\ny_correct = 1334961;\r\nassert(isequal(c(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct = 9;\r\nassert(isequal(c(n),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2242415,"edited_by":2242415,"edited_at":"2022-04-29T13:46:27.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2022-04-29T13:46:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-04-29T13:43:22.000Z","updated_at":"2025-12-02T19:48:02.000Z","published_at":"2022-04-29T13:46:28.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere exists a highly secretive hat consortium. Members possess identical hats. The members are invited to a meeting. All of them bring their hats, but are required to leave them in the shared cloakroom. During the meeting an earthquake strikes the region, leaving the cloakroom a mess. When the members return to the cloakroom, they find all the hats lying on the floor, rendering it impossible to determine the owners. Unwilling to dwell on this, the members each pick up a random hat and leave. Given the number of attendees \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ec \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ecomputes the number of distinct events where none of the members pick up their original hat. Your task is to define this function.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58946,"title":"Count block fountains","description":"A block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \r\nWrite a function to compute the number of block fountains with circles on the first row. For example, there are five block fountains with three circles on the first row. \r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 429.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 214.85px; transform-origin: 407px 214.85px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 364.85px 8px; transform-origin: 364.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 195.125px 8px; transform-origin: 195.125px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the number of block fountains with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.958px 8px; transform-origin: 176.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e circles on the first row. For example, there are five block fountains with three circles on the first row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 327.7px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 163.85px; text-align: left; transform-origin: 384px 163.85px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"543\" height=\"322\" style=\"vertical-align: baseline;width: 543px;height: 322px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = blockFountain(n)\r\n y = factorial(n);\r\nend","test_suite":"%%\r\nassert(isequal(blockFountain(3),5))\r\n\r\n%%\r\nassert(isequal(blockFountain(5),34))\r\n\r\n%%\r\nassert(isequal(blockFountain(8),610))\r\n\r\n%%\r\nassert(isequal(blockFountain(14),196418))\r\n\r\n%%\r\nassert(isequal(blockFountain(14),196418))\r\n\r\n%%\r\nassert(isequal(blockFountain(23),1134903170))\r\n\r\n%%\r\nassert(isequal(blockFountain(28),139583862445))\r\n\r\n%%\r\nassert(isequal(blockFountain(33),17167680177565))\r\n\r\n%%\r\nassert(isequal(blockFountain(35),117669030460994))\r\n\r\n%%\r\nfiletext = fileread('blockFountain.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'switch'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":46909,"edited_by":46909,"edited_at":"2023-09-03T17:54:36.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2023-09-03T17:54:19.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-09-02T14:47:44.000Z","updated_at":"2026-01-26T19:21:38.000Z","published_at":"2023-09-02T14:47:49.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA block fountain consists of rows of circles in which each row is a continuous block of circles (i.e., adjacent circles are tangent) and each circle in a row above the first touches exactly two circles on the previous row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the number of block fountains with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e circles on the first row. For example, there are five block fountains with three circles on the first row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"322\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"543\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56030,"title":"Pay up","description":"You live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by s. However, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list L contains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function pay takes as arguments L and s and returns boolean true if the exchange is possible and false otherwise. Complete the definition of pay.\r\nConstraints:\r\n(1) s ∈ Z\r\n(2) L ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ Z, i ∈ N } ∩ { ∅ }","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 216px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 108px; transform-origin: 407px 108px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eYou live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003es. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHowever, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003econtains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e takes as arguments \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eand \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003es\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and returns boolean \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003etrue \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eif the exchange is possible and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efalse \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eotherwise. Complete the definition of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eConstraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(1)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e s\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eZ\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(2) \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eZ,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e i ∈ \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eN \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e} ∩ { ∅ }\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = pay(L,s)\r\n y = 0;\r\nend","test_suite":"%%\r\nL = [967, 605, -249, -255, 894, -199];\r\ns = -504;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-22];\r\ns = -467;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-637, -402];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [473, -498, -16, -288];\r\ns = 169;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-384, -715, 651, 874, -13];\r\ns = 759;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [186, -745];\r\ns = -166;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-186, -266, 914, 122, -289, 912];\r\ns = 359;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [437, -832, 572, -361, 877, 548];\r\ns = 548;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-803, 95, 503];\r\ns = 503;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [62, -867, -222];\r\ns = -805;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [394];\r\ns = -333;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-836, -353];\r\ns = 519;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [740, 952, -150];\r\ns = -765;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [137, -973, 620];\r\ns = 757;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [483, 772, 458];\r\ns = 109;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [212, 869, 10, -468, 523];\r\ns = -905;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-851];\r\ns = 822;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-667, -308, 549, 792, -128];\r\ns = 674;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -8;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [206, -246, -545, -402, 18, 150];\r\ns = -929;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-104, -710, -565, 448, -717];\r\ns = -669;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [672, -991, -467, 527, -111, -14];\r\ns = -65;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [384];\r\ns = -371;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-436, -662, -530, 279, -469, -415];\r\ns = 257;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [844, 288];\r\ns = 956;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-709, 570, 896, 293, -925];\r\ns = 403;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [11, -626, -258, -537, -961, -341];\r\ns = -864;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [710, -256, 63];\r\ns = -136;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-336, -238, 127, 193, 437, -220];\r\ns = 56;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-446, 274];\r\ns = -172;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [648, 931, 234, -218];\r\ns = 737;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [447, 109, 157, -252, -512, 134];\r\ns = -208;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-581, -916];\r\ns = -133;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [801, 621, -437, 580];\r\ns = -437;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [617, -424, -690, 9, -783];\r\ns = -64;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [864, -98, 179, -443, 505, 515];\r\ns = 922;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-48];\r\ns = -469;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [581, 372, -712, -154, 126, 80];\r\ns = 787;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [655, -167, 357, 289, -891];\r\ns = -701;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [941, -67, -764, -202, -919, -210];\r\ns = -809;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-361, -905, 437, 955, -40, 907];\r\ns = 915;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-506, -724];\r\ns = 157;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-432, -652, 860, -115, 579];\r\ns = -497;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 735;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-141, 334, -315, -866];\r\ns = -141;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [874, -980, -112];\r\ns = 246;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [768, 962, 245];\r\ns = 245;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [207, -281, 543, 462, -440, 995];\r\ns = 462;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 397;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [438, -548, -295, -270, 721, 750];\r\ns = -405;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [583, -617, -567];\r\ns = 307;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-590];\r\ns = -343;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-582];\r\ns = -526;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-634];\r\ns = -470;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-341, -761, -641];\r\ns = -341;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [929, -448, -53, 951, -932];\r\ns = -53;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [23, -338, -653, 107, -533, 802];\r\ns = 61;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-297, -331, -150, 481, -112, 895];\r\ns = -262;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-992, 401, -467];\r\ns = 339;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [592, 611, 660];\r\ns = 900;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-135, -544, -857];\r\ns = -523;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-259, -466, 976, 635, -607];\r\ns = 369;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-876, -852, -430, 370, -489, 468];\r\ns = -971;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-804, 96, -655, -253, 462];\r\ns = 209;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-418, 193, 353, 810, -780];\r\ns = 546;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-383, 116, 814, -359, 106];\r\ns = -359;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -327;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [317, -825, 299, 674];\r\ns = 991;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 977;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [873, -44, 505, 352, -794, -609];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 300;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-60, -817, 431, 411, -229, 625];\r\ns = -386;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-830];\r\ns = 786;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-341, 711, -155, 531, -317];\r\ns = -155;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -429;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 596;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [121, 915, 610, -114, -957, -594];\r\ns = 938;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [633, -465, -282, 413, -58];\r\ns = -110;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-946, -349, 290, 228, 565, 399];\r\ns = -331;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-104, 91, 452];\r\ns = 167;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [374, -547, -796, 603, 18];\r\ns = 448;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-503, 440, 604, 897, -390, 597];\r\ns = -33;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-762, -748, -55, -542, -927, -339];\r\ns = -799;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-129, -575, 221, 926, 927, -44];\r\ns = 798;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-669, 999, 316, 594, -40, 625];\r\ns = -75;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-857];\r\ns = 824;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [810, 680, 180];\r\ns = 680;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-554, 478];\r\ns = -926;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-562, 736, 114, 855];\r\ns = -562;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [478, -853, -694, -868, 666];\r\ns = -881;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-790, -131, -419, -669, 956];\r\ns = -673;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-19, 702, -57, -771];\r\ns = 728;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-188];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = -469;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-491, 617, 718, 503, 452];\r\ns = -341;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [290, 697, -644, 345, 230, -494];\r\ns = -494;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [];\r\ns = 0;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [289];\r\ns = 650;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [-426, -610, -85, 191, -544, -385];\r\ns = 618;\r\ny_correct = 0;\r\nassert(isequal(pay(L,s),y_correct))\r\n%%\r\nL = [833, 403, -426];\r\ns = -426;\r\ny_correct = 1;\r\nassert(isequal(pay(L,s),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":2242415,"edited_by":2242415,"edited_at":"2022-09-29T09:49:41.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":"2022-09-29T08:37:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-09-23T11:47:19.000Z","updated_at":"2022-09-29T09:49:41.000Z","published_at":"2022-09-23T11:50:25.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou live in a world where arbitrary denominations of currency exist. You owe your friend a sum of money denoted by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eHowever, it is possible that neither of you carry exact change for this deal. You and your friend happen to carry currency in only certain specific denominations. You both take all the money you have and lay them out on a table and identify every bill of currency you have collectively. The list \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econtains the values of each of the bills such that the values corresponding to your bills come with a positive sign and that of your friend negative. The function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e takes as arguments \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and returns boolean \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eif the exchange is possible and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eotherwise. Complete the definition of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eConstraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e s\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eZ\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(2) \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ∈ T ∣ T = { { x₁,x₂,...,xₙ } ∣ xᵢ ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eZ,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e i ∈ \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e} ∩ { ∅ }\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56215,"title":"Calculate pi using the Mandelbrot Set.","description":"The Mandelbrot Set is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point remains bounded. The iterative equation is .\r\nFor any complex , we can continue this iteration until either diverges, meaning , or it converges such that . To visualize this set, all those values of in which converge are plotted in the complex plane. Thus having the following image:\r\n\r\nWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and . In 1991, Dave Boll was trying to convince himself that the single point (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form went through before scaping the set (meaning ), with being a small number. This same procedure works when approaching a small real number to the point (also called the Mandelbrot Set's Cusp). You will see that as decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that for or approaches the digits of .\r\nTo find out more information about this discovery, check out the following article: π IN THE MANDELBROT SET.\r\nIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input corresponding to the number of times we will decrement by 100, beginning with (no decrement). It outputs the number of times it will take for to be greater than two when iterating the recursive equation with with . For example:\r\nif x == 1\r\n epsilon = 1e-3;\r\nelseif x == 2\r\n epsilon = 1e-5;\r\n...\r\nend\r\nThen we will iterate the following:\r\nz = 0;\r\nz = z + 1/2 + epsilon;\r\nUntil it diverges or becomes greater than 2. Finally, we will output the number of times it took it.\r\nProblem based upon: Problem 81. Mandelbrot Numbers and Problem 785. Mandelbrot Number Test [Real+Imaginary].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1245.23px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 622.614px; transform-origin: 407px 622.614px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 44.0455px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 22.0227px; text-align: left; transform-origin: 384px 22.0227px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Mandelbrot_set\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot Set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"35\" height=\"18\" style=\"width: 35px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e remains bounded. The iterative equation is \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-8px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"80\" height=\"22\" style=\"width: 80px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.0909px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5455px; text-align: left; transform-origin: 384px 31.5455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor any complex \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, we can continue this iteration until either \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"27\" height=\"19.5\" style=\"width: 27px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e diverges, meaning \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, or it converges such that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. To visualize this set, all those values of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e in which \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADYAAAAnCAYAAABEz7vjAAAAAXNSR0IArs4c6QAAAr1JREFUaEPt10mojWEYB/DflShDMmWjTAusiLJA2YkFNhZEKFFSMhTJnMjCEGVayTzskGSjJFOGQrGRuaQoC0Om6Kn33I7bvfd85957zndx3uX3Pd/7Pv/n/3//z/PV+UdX3T+KSw3Y38ZsjbEaY+2kAjUpthMiMqdRYyxzqdpJYI2xdkJE5jT+O8aGYyu6NFOiYziauYRVDmyKsVmIxJta+7ASn6qcb+bjGgPWKbH1ECfxrWi3YHIF1uBt5lNyCGwMWF/MQbDypSinftiCHXicQ65lHZnVPLpiM87iSlkn5BScBVhHLMfrJM1fOeVaODaKPA9D0pUoVlV9aqWAxfuZ6I+d+JEjqM6YgvmYhINY1uC6ZAY2AdOwrkIO2AE98B0fSxQtlBOFHozTuNlSYNVwwN44jmvpDmcRxNDWAKuWA1YVWDkO2CdJdRRGYjVeYj2mYy824msTVFQNWOg4GvA23MJhHClxv6LvncBn7MLkJK/od7GiJ77JE1jBAQ+ge1EixeNTsBnWH5YfiYfVFjR/F++wPc2YAfY2VuXNWE+Mx2XEHVuIBeiFpdiTesepBHoGnmBqatz3U3yAGYeLWJTYi08KBRiRxSGasfNWmUfh7IHYhLD9GHovJbnFnBh3J/paTCRxt5Zgf3oW/SWKEuAfpM1CBQEq5F5Y8SzmzogJhovXB8S8+rPB8zYBFnuGRKNBT0Swegc3krzCPOLXJSQad+l5kuHuJOfFeN8MQ1Uzj4wqqQ8bjfM4hA2JrUFp9Iq5MnpUNzzK0zzKBRXx8d8W8puNc2mDwp2biwHJVV+0IbBhOIPrLZ08SgGN/7ZoC2PTPPksfRDGEUzdw9pm2IrwcqQYs+KYdFaYUhQrVHIVTxsmW2oILgWute9DpsH2K1xo7WbF3+cNrC2x/LFXDVjFSluhjWuMVaiwFdv2NxLXqih5/DKIAAAAAElFTkSuQmCC\" width=\"27\" height=\"19.5\" style=\"width: 27px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e converge are plotted in the complex plane. Thus having the following image:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 542.455px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 271.227px; text-align: left; transform-origin: 384px 271.227px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"731\" height=\"536\" style=\"vertical-align: baseline;width: 731px;height: 536px\" 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alt=\"Mandelbrot Set\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168.091px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 84.0455px; text-align: left; transform-origin: 384px 84.0455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eπ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. In 1991, Dave Boll was trying to convince himself that the single point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"89.5\" height=\"18\" style=\"width: 89.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"89\" height=\"18\" style=\"width: 89px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e went through before scaping the set (meaning \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e), with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e being a small number. This same procedure works when approaching a small real number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to the point \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"79.5\" height=\"18\" style=\"width: 79.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e (also called the Mandelbrot Set's Cusp). You will see that as \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"63\" height=\"19.5\" style=\"width: 63px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"89\" height=\"18\" style=\"width: 89px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"74.5\" height=\"18\" style=\"width: 74.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e approaches the digits of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eπ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTo find out more information about this discovery, check out the following article: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://doi.org/10.1142/S0218348X01000828\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eπ IN THE MANDELBROT SET\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 86.0909px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 43.0455px; text-align: left; transform-origin: 384px 43.0455px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e corresponding to the number of times we will decrement \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eϵ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e by 100, beginning with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"47\" height=\"18\" style=\"width: 47px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; \"\u003e(no decrement)\u003c/span\u003e\u003cspan style=\"\"\u003e. It outputs the number of times \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e it will take for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37\" height=\"19.5\" style=\"width: 37px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e to be greater than two when iterating the recursive equation \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-8px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"80\" height=\"22\" style=\"width: 80px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"40\" height=\"19.5\" style=\"width: 40px; height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"74.5\" height=\"18\" style=\"width: 74.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.591px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.2955px; transform-origin: 404px 61.2955px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eif \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex == 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e epsilon = 1e-3;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eelseif \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ex == 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e epsilon = 1e-5;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003e...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration: none; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eend\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThen we will iterate the following:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8636px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4318px; transform-origin: 404px 20.4318px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ez = 0;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4318px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2159px; transform-origin: 404px 10.2159px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ez = z + 1/2 + epsilon;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUntil it diverges or becomes greater than 2. Finally, we will output the number of times \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e it took it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eProblem based upon: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://la.mathworks.com/matlabcentral/cody/problems/81-mandelbrot-numbers\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 81. Mandelbrot Numbers\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://la.mathworks.com/matlabcentral/cody/problems/785\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblem 785. Mandelbrot Number Test [Real+Imaginary]\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = piInMandelbrot(x)\r\n n = pi;\r\n % Is it really equal to pi?\r\nend","test_suite":"%%\r\nx = 0;\r\nassert(isequal(piInMandelbrot(x),2))\r\n\r\n%%\r\nx = 1;\r\nassert(isequal(piInMandelbrot(x),30))\r\n\r\n%%\r\nx = 2;\r\nassert(isequal(piInMandelbrot(x),312))\r\n\r\n%%\r\nx = 4;\r\nassert(isequal(piInMandelbrot(x),31414))\r\n\r\n%%\r\nx = 6;\r\nassert(isequal(piInMandelbrot(x),3141625))","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":2620230,"edited_by":2620230,"edited_at":"2022-10-10T05:30:25.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-10-04T21:53:11.000Z","updated_at":"2022-10-10T05:30:25.000Z","published_at":"2022-10-10T05:30:25.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mandelbrot_set\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot Set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a set of complex numbers built around a simple iterative equation for which the orbit of the critical point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e remains bounded. The iterative equation is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1} = z_n^2 +c\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor any complex \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, we can continue this iteration until either \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e diverges, meaning \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \u0026gt; 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, or it converges such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \\\\leq 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. To visualize this set, all those values of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e in which \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e converge are plotted in the complex plane. Thus having the following image:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"536\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"731\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Mandelbrot Set\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is amazing about this set is that it has several properties related to famous mathematical concepts such as the bifurcation diagram of the logistic map, the Fibonacci sequence and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. In 1991, Dave Boll was trying to convince himself that the single point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = -3/4 + 0i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (also called the Mandelbrot Set's Neck), shown in the diagram above, connected the cardioid and the disk to its left, and had zero thickness. In order to do this, he was seeing how many iteration points of the form \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = -3/4 + \\\\epsilon{i}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e went through before scaping the set (meaning \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}| \u0026gt; 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e), with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e being a small number. This same procedure works when approaching a small real number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to the point \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + 0i\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (also called the Mandelbrot Set's Cusp). You will see that as \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e decreases, the number of times that the Mandelbrot Set's equation has to be iterated in order that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}|\u0026gt;2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec=-3/4 + \\\\epsilon{i}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + \\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e approaches the digits of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\pi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo find out more information about this discovery, check out the following article: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://doi.org/10.1142/S0218348X01000828\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eπ IN THE MANDELBROT SET\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this task we will compute what Dave Boll did using the Mandelbrot Set's Cusp (to work with real numbers). To do so, we will create a function that takes an input \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e corresponding to the number of times we will decrement \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e by 100, beginning with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\epsilon = 0.1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e(no decrement). It outputs the number of times \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e it will take for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e|z_{n+1}|\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e to be greater than two when iterating the recursive equation \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_{n+1} = z_n^2 +c\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_0 = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec = 1/2 + \\\\epsilon\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[if x == 1\\n epsilon = 1e-3;\\nelseif x == 2\\n epsilon = 1e-5;\\n...\\nend]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThen we will iterate the following:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[z = 0;\\nz = z + 1/2 + epsilon;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUntil it diverges or becomes greater than 2. Finally, we will output the number of times \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e it took it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem based upon: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://la.mathworks.com/matlabcentral/cody/problems/81-mandelbrot-numbers\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 81. Mandelbrot Numbers\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://la.mathworks.com/matlabcentral/cody/problems/785\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 785. Mandelbrot Number Test 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Sequences 93: Recursive Polynomial Function","description":"For a natural number and a polynomial function , we define a recursive function , as follows:\r\n , and\r\n , for .\r\nFor example, if (or in Matlab array form, P = [1 2 1]):\r\n \r\n \r\n \r\n \r\n ...\r\n \r\n and so on...\r\nWe can see that can grow very quickly. Therefore, we will instead calculate: , that is, is equal to the logarithm (base-) of the absolute value of .\r\nGiven a polynomial array , and an integer , find the value of , rounded-off to 4 decimal places.\r\n----------------------\r\nNOTE: To encourage vectorization , FOR and WHILE loops are disabled. If you know the math, this problem can be solved in less than 15 lines of code. However, solutions up to 50 lines of code will still be accepted. The semicolon (;), shall be considered as an end-of-line character. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 505px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 252.5px; transform-origin: 407px 252.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69px 8px; transform-origin: 69px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a natural number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.5px 8px; transform-origin: 84.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and a polynomial function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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style=\"width: 31.5px; height: 18.5px;\" width=\"31.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100px 8px; transform-origin: 100px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, we define a recursive function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36.5px 8px; transform-origin: 36.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 79px; height: 18.5px;\" width=\"79\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 167.5px; height: 18.5px;\" width=\"167.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16.5px 8px; transform-origin: 16.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36.5px; height: 18px;\" width=\"36.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.75px; text-align: left; transform-origin: 384px 10.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 48.5px 8px; transform-origin: 48.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAO0AAAAnCAYAAADjPs9dAAAInklEQVR4Xu2c2ct3UxTH3/cPMLtS9OTlQhRlKuHiLUOUkgtjeosylRvTiwvJLFLKmAvJGEoyX1BIxiLKhSFJroz5A1ifOqtW29nn7PH8fvt59qlVz/P77bP32muv79pr2Pu3fVt/ugS6BJqSwPamuO3Mdgl0CWzroO1K0CXQmAQ6aBtbsM5ul0AHbdeBLoHGJNBB29iCedjdTz6/TehUoR1CfwrdLXTv5phen4WVwCpBe5kw8pLQ7wWW5ETp4x+hrwr01VoXAPYtoXeE3hQ6XOguoX2E3hY6vbUJdX6nJbAq0D4sbL0v9HyhBUJxHxJ6pWCfhVir3g3G7yChm81IR8rfXw7/H7VFjVl1wa9qgDnQHiyMHRDA3IcBbbQJgGVHfCzinZCmuuPcv8WAi+G7SehHR0ifyf/HCJ0p9EaIAFfYxurZt8JHCe9rhdNJHho5/D03/xDQniKdqLul3BAzfSoEUFAMnheEnppRkOvl+51CtVw2+PlE6ByhregqW23BZT5NaJ132jOEvweFiMPtg1t/pZBriJLRsOYvAtZrha4IWa850OpcAds9wz8A9lAhtYbEk68KEUPxXC40tovS7oNhgWouBu7ipULHrvlC1Wbv+2GA481a1R4zpv87pDEegu9Bz9gQaupKDL812moCEbDqM2tkQ0FrBcyOep4zA/5/bvjMJ2wsPwuABa39oLCPC23V7CmW+weh84VK5Q1Krpka8Eek0/sGvUCB8epuF9Kd93P5e7MaX7yMI4S+EbpVSD3WYqDV+IiFG9tJEfhvZlXdOGqpXVZZwDPYLbRvSU1rqC/yBsz9KqES8aF6WmMGO0Us6BM0ZsDRpe+E1HObVeIUBiLf0VDD50VGdve/5taTnZ1vyE7rAtLX6b+GFXdyWHus51JWc913mtxFnnofA/mA0LlCpVzLkqAls/2y0JTbbj27WkCJWYPmQMs2/vowQ1xf3+5lQWt3WgX9nfKuLUvECC2lLS4y7pXryqf01co7GCvqtRyyKAVY5l4StOjTgUJT1QP1zBi7g9bRvpCdFldLA2VikDGXxtYFGYJdVZVGQZ9SetBSwFgZYO5ABbs7ytuyi4xc9xByS2oYwsOEfjVy5rNnhIgJY0pwIQapJGhDxrOgPSlgPj5dmNKfED60TXM7LTuWJgZ8iQ0AgjvG4wJbFzxE+LyPopIB1iN5fGZ3aXWv9LieL8Oo41oDErpQCpbQ9r52KeDByF08zF/jOmvw8BwwpHri6cJhcBTrFiG3JoscKKHklMCWBq0mNkmm+dxodORsIcpaPG4C1LrYuV5eU6Cd2kFVURHeo8M/Y0JWQIfs6nRjC8xqMLTfveR7XF4MA0aCXdS3k8YaCws8XaQc0E6FElP9InMApnE5bTUBBKCfHuaP94MrjIKT1AHEZMztszH0A7BzElJLg1a9uxuEb18FQOVkKxfqSgNYjD5yQU65LnZToLWARAnPMhrBGVdr6VCssWylTjgUtFbpbFaNHZMxQk88qYuVUvZg0Y+eQlbAd38NgApo6m2issNQUXt+T8j1LGz4MtZRrsLS55Kg1ezxHzJuaI1Z8ykY89eEOLBxSI7gnXebAq11e1Ec11p/IZ99LcTpKF/iIwe0NrZhfHaW0GSWvjtlrQuua5WurNFi/mPub4mBNUb29XWBfMGOhZtNzOx7bIydyleKh6Q6hkeG52UP/4TwMXdcl2w8xhI3m0sZvif1CGbRko8vIxwiCG2TA1r6UB5i3c3NAFprtErVSMfWzo4Ts7Zu29xdXcOxWO/IKn0KD/b9nPnP1lg9nRcDrbuQKe4tPOaC1rqIMXXenKx1zsKVfleNVm4yZYovwKLHVMfa4Wpq4g+vyvfglqZeTtBz4xinUG9K+bC6mlKlIC7eNTGv4+Q7cgbs5Ho8dKx56nnpYqC12bece5kac6WCXl1098zzHDhS3Cztc5XZYzsvVWQAk7MGc7Ka+752TGvvBMcCFt5twrRGONRMTDt3dHFuofX7HPDYRBj9xVhRHXd/eS82c7rK7LGVq80p8HnKXELXaapdTdDmAtYaNuZQ47xyE6C15QYEkeqr825qFletJ7sMbglPjIuIoHHrUrKI65A9xmBdJ7RLiNtRsUarBFhdw1sjrmZzmEswEur43G4MG5nmn4TUxS9t3JoArXtrJ/dUEUIlHR/q+qj1vHpYrLG4Frdbb4iMKShjsqBL3CoqCRD6UoOlyufGtfpLHUsd0ay107KGbBBT96uZ48medbTXMMfiWj47QSj3tlcToLW1P9/RxRhFpT+K3VO7HgvA87HQE0IvGmG79VouDBPj+oyALmDoKayYudRoCwgvEfpZ6Bch7idTE9cTVdZocaiEHa9W+WdsfjVAqzqG98SvNYw9G/IhpSZdR4wZJ6C4zranEH3Y8o6t13K2mbp2bPlnjI+1B617Csp3aCJGeedA5GaqXTdsjKepXSbESMTwX7utW3JwyxZubJ9S1siZQ2nQzh0IsbzaGNXNNbhhm5sDyAnrLA81QauXPPSo8Gwizc3osjj8HIz7cHDiWWP5UxSAifsuwesNfibwrtCYO6O8+b5XnvREzUXyQWr5IWV+Oe9glG4U2ltorGxi5ZNTVknlsSRoiU8Je0KfJ6WhXuTXdzltxk8gueepVY70PfZ96JhuuxqgRdfxrsZO3oET7++opZZhUiYPk1hN6/al9DP3jiaRav0O1dz4m/F7jQ1x39fxlzBqyxyvjl+8/Ego5RJIUf6WBC2MM/lrhABUbBkmZOL6e1Ul4piQ8XqbLoHFJbA0aJkgrtaGUOmsbv8lxsXVpw+4CgmsArS64/rS+Cly0AvgBPE590ZTxu7vdAksKoFVgZZJ4sqm3opwhVSyr0UXoA/WJRArgVWCNpbX3r5LoEtAJNBB29WgS6AxCXTQNrZgnd0ugQ7argNdAo1JoIO2sQXr7HYJ/AeTKShG4xC/bAAAAABJRU5ErkJggg==\" style=\"width: 118.5px; height: 19.5px;\" width=\"118.5\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (or in Matlab array form, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 44px 8px; transform-origin: 44px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; \"\u003eP = [1 2 1]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e):\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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style=\"width: 326px; height: 19.5px;\" width=\"326\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38px 8px; transform-origin: 38px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 32px 8px; transform-origin: 32px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAOwAAAAlCAYAAABBNAVoAAAJsUlEQVR4Xu1cR8stRRB97weYXelGDAtBUIxgAgUziKCgoosHinFreAYQMT0FFQQjuhFFRQURzKBgQjGgiLgw8BBxZcQfoHVkjhRtdVXN3Lkzc6EHiu9+985Md5+uU1Vd1TNbt7SjIdAQ2BgEtm5MT1tHGwINgS2NsE0JGgIbhEAj7AZNVutqQ2Buwh4qU7CvyKsLmIoLpA9vify6gL60LjQETATmJCzIerfIRQshCftzvfTny6YvDYElIpAh7P7S8X0Snf+mB/FAjsdFTu9xDbqA6/qS6fiu7z/L3x+CceD+L4qcO6CdBESznwIsMjgQ6126Hr/fs+fUmb8G4Ig5GNruXnLtwV1f++ijNbypsGLb6PtukY5mCXuK3OgukT2Kkb0h/6OhI7vvn5O/T4p4IS7O/1bkbJGsIiBcvV3kUxF8jg4ozDXduTu6ky+Tv9+JRB4UE3W/SF9jEvVp7t/vkA7cKHKFyKNOZy6X364VwVz+KXKyyNEiD4vcJ+ItGa6T34HzAer+v8vnG4I2cTqu3S5S6tidiXYxZzeLQLeeF4HiX9n1+Qn5GxnqEo4psGKb6PMl3dhDnDKE5Y0BKEJYHnuryQNgLyuwPaV4vQPwqhIl4/8z5bsHRKgAUKKIsLDQ74j8JqI9JYD5uLvXWfLXMyrPyu/fi9yU6OMmnIL5ea/rqDc3GPf5IoeJ6EgGJH5EBOSDcbYI8JB8T5LslM/7dXNFAsJQ3lMB65PuvjUs4RhqSyfowzMi5TnQgy+CPlvtTYEV2y2NVGRMe5V1NGEBDjyQPggcvqtNLMEAASOrByv3Y9cAlAVHRFhNSmvwejK0wSknDh4ahD1BJBsF1JRt7u8Z0ZA4NaWgV7HmFmOAoT2tI0Y599ANeOIyekHbuA4kh04cJFJ6aOoV+vVC9zvwP09Ee1x42tKAkpToX2lk8B3vjcjsqMRETIEVuoHxwat+JXKiCAwdjlEJq62gZS0x2F8UKJYX6+NdNb5/d/9EhOUEeZ4AYTEMRnQv9PUPkcijJ/Rg1lM4DnjOmlLQQHlKow2ynluSsraE0NeVpKLOXCgNw7uXhzawmNM9ixNoRGqEzIxL33LdWFmKkPXo/16bDYlLMlrWDPcjsfC5nARawygctQaVJSzCYHgSz6IydEM7npelomWigVkZ6TTO9eipcg4ihhohGfLi99rcauXXXphJolokQoW0jCjaPUnEM4raUei+ae9qeV/CQgMdedkpsJqMsFhLvtK1Zlk6/KQBtCaeIZdHkpruZQir+4gESW2NrJWzZtnRDypoGKYslK2cD4b1xNAaD9euHmHxG+9R0wELCho+K6JBRPShiLfs0EsxTVjqE9r05lGPrWZ858RqLR5Wg1MLJTUw1joIlhJHZi1RTnyGsHpiPZJpgDxiow/w2G8GHmCJfGWYiowpEz0eYRlaRoSlt4rO05jg3geKDC2V6WWOXgNn+6z1wiL23FithbB6oqxB6zATodcxImVyAQpTS2hESp8hrDYYWcJGYRKVDQrX92D9t+91+vwhdUxcDwN7hIjOrGYJ6y1ZsiThGKAXCMchUZKxhhN1q3QUXP5ExkMT1gqd58ZqdMLqtQvAAWF/EtlV5DgRJDN02eVqg6wMObzUvqfYGcJqZcoSNgrteM/sWl+PQa/nh5I2SoxZ98XS4CkRrA11acYjbKTUbCdLWPThVhHW52HEt4kMybjTWZQZe41vbd2NfuuxlXguAavRCauzfAAAXhIehySFl7pXxNuHy05NRVhvAjGG7FqMkz0k8QTlXvV4W25Qq11a92ZZAuv3MuvqEVbnH2oREtqLQmJuWMFfbLYoN0H0TThSbyzDlZ1DTQh9n7mx4vyNTlgdapJwmsQgbLQraGrCevVTnfGOPCwJuyn1WK8U5REWyqPn2QodM3X20ohw15j2tNZyqWbYuCSxQmpNWKu+y3vqZKQm7FKwGp2weq2gFbf2vQX+1IQdKyTeJMKyLFEjRERYGLKnRbA5Agc87WPdZ2w3hGFmLRe/9VnX69JM1vhhPNgOW4b21K9VQuIlYTUqYctSjV7L6URTlG2dgrBDssTRGnGVUtSq4XCf6zlPMFRfVy7k1kR4z9e6c6wHAeCRkJtA0goHwnKUXpCzYGnPq3tazWsvlymTZWr22fV0qRcfSQexZXEpWI1KWF2zLDO8msy1bWecPCau1rmG1SFb1sNGirdK0mnKLHG5zztL9gx5eK8sQWptRx6e13Ev+MXyhbffWzuMbNIJa+hDRPSe+LmxGpWwepIsskXlHg3Guss6OpvteU6t3FESBONHqLhK7TirENZ5UQTAa2CstgUN6VAX84YDD1Z4pOAt+9SuI8J65MqSFW1kDTR1mDutkAxbElajEjZaJ2gPHCnXKjXNTFkHk8jJ8dZYTK542VAqHdqNwv2ags6RJfY4m/Vw5T30AxUZzKw+0Jh6dW+cg989z8rnXVEe0hv1vTliriXSz9K54P8+EQjOH4LVaITNbEcsa7Re+YPkXtfWRACmB28lN3SGOJoMhvzetrfAqS3q56GE1QZu1d1KtYjG2m1kgYcw+F0Rlqxqu6B4rdbhqNQ3BmGHYDUaYfV2RM8yZncYkdxDCJD1sACd/a7tXcX6JbPjCsqwXcQrGSyKkUFn+hJWZ40x/5eKWG/7IClqD6ozdK3lC0hWGPsdzhj4IH05H8xAWwaYEVff3Mm6sLKGNwphuZZg4durtfapzzFMRN02e+g1J0KyzDY3JiT02wro4b2HoXWfsM7DPuLMg/bZscx5Xh8l1C8OiN74UG6s0eWgw2XAIOItItZaWT8vm8HGIr2+B50BvrtNBM+ZRolFq911YVW2hX4+KMJyGZyMtVPwv+usLXcgCKxZeWAvqA5H9O96vYZnSF8S8Z5vzOwcgiKcI7K70ReUGkA87/1OsFxniLA88Zl8/qCiOGUTuBZv0Ki9XSGjXEs7h3NUSzRhzMeKoOaKAzVYPFCe2QNczhWu2ZmYo5qu1bDzXu+DPuBhcERyODI6UmtnnVixzdrYwZ/PRfBqm/+9jmfIHtlVFZGbrft42VXb7Hs9wiz9pEvf6zfxfCgQ6q3Zl7Rt4hjH6vNsWM1BWIYw2H9seeGxQB16H1q+JRuUoWNr1204AnMQFpAx/T0067gu2Ln3Ndobva72230bAi4CcxGWpMXe1ei1o1NNIRJtS3qx+VTjbu1sEAJzEpakxYufhzwnOTbMyIxmdv2M3W67X0MgjcDchE13tJ3YEGgI5N+a2LBqCDQEFoBA87ALmITWhYZAFoFG2CxS7byGwAIQaIRdwCS0LjQEsgj8AzV9Y1OLICiDAAAAAElFTkSuQmCC\" style=\"width: 118px; height: 18.5px;\" width=\"118\" height=\"18.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69.5px 8px; transform-origin: 69.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and so on...\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 53.5px 8px; transform-origin: 53.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWe can see that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 185px 8px; transform-origin: 185px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e can grow very quickly. Therefore, we will instead calculate:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANkAAAAlCAYAAADP7kwUAAAJ+0lEQVR4Xu2cScttxRWGvT9AYjSjmMHFZiBEFFsQzUBQg4EQEJtgkAsKNgMHavTaISLGBiUQsIlkGDUBQRGiUUEhCYINoiMHarhocBSj0R+g68H9wqKoqlW1m3PuPXdvWHznO7vat961atWqqrPniPVZEVgRWBSBPYuWvha+IrAicMSqZCsJehE41zL83uTM3ow7mj7EY1WyHR35Bbt1q5X9kMnKne9BDvHYNlCnWCOPNXlpJlJca+X8caay1mLyCISk2iBwjPdzJl/MUCcz0jcmH3SWFeKxTSVDwbCIV84EEthcbHLjzGV2Yr7zyUNSbQiBx62ef5j8Zab6jrFyHjN5obPMEI+akqHZU57PLfO/CwWgYH8y+fmMCqaqrrAPNy9U9hQ8evKCz2Um/zd5uCfjBtKGpHJtgLgnNbSpxpVcdhSMGWdur4X2/t3k0Q5FC/EoKRmD/L7r3Zf2+e3h/xPs7/Hu3SuF70mTUzI68pHJL03+1TAAY5LQ8b0mN4zJvKU8x1m9vzO50OSHQxv+an8xGgfTE5LKNZaxvsAEo3dG0ol37X/cvIuG7/kfcr82fF/qM/Wfb4KBXuKhzW+ZXGLS4jqGeJSUDF/3SRMG+Q4TryxouoCBFHe6nkKIZ00ArBR9Ij/lLakAAgrXca713hIDmisTD+Kfw4tDXcnUP98nvvuFGxeMC/2UEj5hn+82ya2zVE7JgM81RvD/mgqHfT2jlQw/9yiT1FpA3v+6GjxY+vod+/Bqonwp2EuDRH0oPG7FiYUBm2tAlijn2x1XstS4o2ifBLzi9SYMtJrxsX14yiRy10crGYP8oww5CSz8zYGRmwkB4g8muRlkkyDJLb3d2jK3776EYvkyd03J7rfO4RHxsLzIuXoYZ81mqYdEvk3NYhoHlGe/ydHBYI9SMqzKWSa5qE0LWKTxLqTaqHVebvZbirTMZKxxWEceSs+uKZlXoOsKRs8vQ3AZ0+UEfMQD2tQmuGbXXxd0wStjdd+wN4TvwbrNaommUk9sKWhuhlxKAbRGPNUqaFnELtWO3nJ7lAzjdeRQwYf2t2fPyOdtDUKFljvpbLrEKI3F/yyfAj6pIqqM3AzXi21PelxG4gu14FOIR4+StYJV6gQKytNriRQGzoV5RZISQTR7lqxnD+CbTBspGZhcbXKpCetfQv1E3AhI4Y5hAGtGBbcfl568B0xwiyA4hLrH5OuhsznMQ1IlQPklBlHqnPulQBtZSZOuo1XGGC+oxJEar9QFZk88oZrLGOLRo2QtYNWICHFK/niaDwBuGjooP5282rjm/dMDqchbA596c+5Hra28a93jicrp3QOivJqSKRqHUqRhZs3clFFyc5TGY6L1q2YS9SlnnEJSJYDgsl8/fJcbB99mkuVmOtV5nr1vmXHh6lUDf9QnzxEFxXjneZWOpeqtBepCPHqUzIPVG1rWjNLqYjLoPzAh1O/r1QAwKxKNIgKK9a65gz3K7UFOw86RMpXej5lFa0oml71UrscrJaVXphQzP5vUjFZIqgQIXC7tq+LuvTy8P8f+MhPLiDKLEjbPzcDMKJebtPIVvlGOj1qKsyjgn01QeJSf2bzkDrYod4hHa6PBxYPVSxwRtlXJSmTHOp9sggVq3WdjQU3gozf4oWNfY5VL+UqR1lq5JSXzVr9kXf1BgnS/0udPlcwvB2rjFJLKdSwNzdMeHikWriHRXzagS6eDSK+gSA9f1QzllRK/MdRfq095xdta8CPEo7XRKVi9+1xTlIwOi3QapJ513ZQBmqpgY/OXlEwWvbS2UX0+QOXHSoQgXW72lyGteSohqVyn/eyopcIYLk0ZQ99nrTlbDyi08DbEo1XJvAXETeudFVoaWyOkD+/2RgqnDNBYJZmar6RkUoJIybzL6F0/T7jc+kZY1dawIalc52UU+MrPjqXvS7hNGcMpJ2haeBvi0apk0eI1IlVLY2tleHL0bgGMjWpGfVryfUnJ9H2kZB4v79p7Y5kLh4vMk9wjB4wPy3ul9u1gdokOik9RMu8J9W4BtEQ1Z1MyD9aYMKpchDFrMkDybkdrhEljPTbwcTBGF/041IyNV7JUYUqupNZkeCpnm5T220JSDcCnh8xTg15SwJzxkpFvnRR8GTrHitvcGt1W/o0FPlKwemeSqWRP6++1RihZbx7afDBGF73bXDM2tbWX7k0RrWNG/M0wQPcOihXtsbUqmW9Djtw93lEL2UuehXdNSdPDX9XbYtCKBqDFMuQWr2NcpTFRPlmhu6zCfSaE62sn/NN2tUSHSn05GKOLfixqhkPEKq2fcdd+ZUKkja0SHhSh5VRMq5J5g5CLRnvjGc2eY8cRvH47cEc3G3o8sRbOhni0KFnvIrVEWhGkx5JQNwPAWcjcuowyPzMpRYtUZ280dIwRmTNPaU3m97lqxJQrliM3CjblUmtIKiu/9XSQ3xaKzgjSJwIyuXOxOeylxOKbMJVx0oxeOzJFnXCwtl0U4hEpWcvVllZytRy4ZKH508GqoiDk0Ylt774xIDyQpRbOxxLxLHXBr7Xvvelqm9H+5E1ujSvDknPRhCFkfcSkZa8obXtIKsuQ3tYoGVY/M0cHHHAva4e9ddTsU0v3H5MXTfzFYL9fhqtMfRwhKxloYRXFAEI8IiXzfjNgR9YmIlNEepGLcnKW2i+WIxejRamj9m7jvSdeqY/+WBCzlX5MhrwPmGB9cxcffdlp33RTme9rG+gRqSA74+xvQpc8idZ7ZLQpIr33dEifzuJp36MDFZFSC78Ij+IxFTKeZsKxpfR53b7AWuSuwkSkjO4EUS8HXbGwOZLoh3JK7339lMWxnZ6N66j9S76HcLeY8Dd9nrcv0jtxkJmr/aytGKevTN4zqa2tdCZUd7tq/SlFgmuk0lov5Y3alru1we2M011D4FfpdgfKW7pVj3vI6RHqzhkJ+n7fgG90CkduOUGh0kynJo9WsiXJJFCXdOEgKpZ5yd8RWRKjJcvGQh8wYfb7sauIs4R6dKI/5+aFpFqw8Zsa1x6OhnhE7uISeMmd6PlFoN52YPFqFrG3vF1JDy7g3zK7s5+WO7AbkmphsKYGbqLm4W2xnmv92YoQj20oGZ1UaD69qhEB0PKeTu81aT1A3FLmLqTRmiRai6ivKFnuJEZIqg2AtdQYj+FliMe2lEyKxp2waPOzZ8ywcj9bFSwLmaJrtftTyiiXMrc2CknVM2AT0s491rqj2MvHEI9tKpkUjR+/bLmI1zIeBEaihWpLObuYxkffiFoSwtbdLvr7ExOuEXHH6kGTUvABcu8bZrlt44Rr1/uTC6U2jy0rxGPbSrbtQTrc6kfR9pukN6CFA/tnz8xo9A43fLP9XZXs8KQBVttHE9+ccUY4PBGt9HpVspUSKwILI7Aq2cIAr8WvCKxKtnJgRWBhBL4DDZe9RE9ZN3MAAAAASUVORK5CYII=\" style=\"width: 108.5px; height: 18.5px;\" width=\"108.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, that is, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26.5px 8px; transform-origin: 26.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis equal to the logarithm (base-\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ee\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.5px 8px; transform-origin: 80.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) of the absolute value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAAAlCAYAAADyUO83AAAECUlEQVRoQ+1Zy8uOQRT/vn9AuazYyGVhRSHltlAuUUoSslVuZaMIa0TZWPApKyXsJESKwkYsKGXhkoWlW/kD+P1ec95O48zMmed5ePr63qnTN+88M2fO+Z0z55yZb3xsirfxKa7/2AiAkQf0i8BibD8HdK8jMfaBz+UaXn0eASp/FrQH9LVG6Mzczfh2uIanB4B5YDjbIeDbCkWo/BXQpoo1DhEGU3aBjnh5ewFYD4ZnQNMjKR7g90zQsjB+E3+vFlya89+BtoKeebWqnHcU8+eCDpbWeQAQHmRKl5U2S1lvNfq3FUD70U+dxfv49tEjXEn4zHeC/BzE45CNL00BoOXpvrrR9a6Hge/4S6+goroRqKeg+ca3FvqaSynPRdBCZai/JtYA8CIoRSbHQOcibkT9ixrbYqD/P6wvIshRO46BZGbwAhArtwRMXxu4/1Jju9G/oX4z8L0CWcB0bX3hRw/YAFqQ2sALANPL3cCE7j3DYCgKyqcYpFP4cAKkY8e/Ulz4yrFMGcxdCovwZMxIT8Zxo7V3hkErRvAIsS1voDVjx09Q7HWSolMpWIySDMpeD3iPzRm42GLX5hhd7UD4/gF/V4Di4obHwwImhQerum2gjWFCHFi1UU5jzskEI+57CWSmRA8ARJlKSSMAn0HTQKtAtLqAQ+84ZCgvlrCCZwoArqHFdXYRS1J5nm16FYHPpd0s8B4AtAAUllZkUBGlX6J/HvTQUFyUk/RXA4AGRoIrLXkHdCHIkAJPjzPzUF4zEHoA0GdbFNCgEIBSSdsWACrBo0BPZADO5vYIFVlr6uoB4BsYSgm8Bn0pX1PjllXaAqCr0Jy7W3u3AiBObRowHfiSQSZI1BYAWU92tXVENvuUPICReCIoEUdwDQ4jdM4tJZA2jQF6r1oerYKguA8xsDYupcc4kNWkQVkrFxsddGtqCQKQTJMlD9ClrVVNaQ9JFUiiSDYaZ8I5gzDjzSeQ3Ea91aQcHat2GWyZA8BT/sY1Qu6WJ2B5had8XLMXRItbcYBjK0HxxUzwlD2TcuUA0JUWU13K7XSazEVoAStpDezBs8509wbEQiu+zup6gDe8x4XYQ69ji6/uQ4dLAUBByFzSXy7Xe94BZMOSQDrmcE187DTY1vehYuh4ADePAHPuOs0p9Pm48QSkr7ixYvz9A3QrMa/0ICKPmuTBJ7j48kPD8H7PZn3XYlOPHaBswCwFQQOH1kM8WktBSbdsvcMf69Nri++OfQDAtEZX5/3B8qYO9B/wfwRKBcfhHn0AwM0lt2833LwtAO4XYW7UFwACwjV0WGBZz2tNgGBAXgsqPocL8z4BEBAWodPV/wcYRKv+zdY3AE2s3OmaEQCdwjkJmY08YBIarVORfwNL/u4m7cV/sAAAAABJRU5ErkJggg==\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 88px 8px; transform-origin: 88px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGiven a polynomial array \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eP\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57px 8px; transform-origin: 57px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, and an integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63.5px 8px; transform-origin: 63.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, find the value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAAAlCAYAAADyUO83AAAD1ElEQVRoQ+2ZO69OQRSGv/MDxLXTiEshIcS1QaFASDQiiEbnUihIiFtJQqJRuCRawQ8gKDQ0LgW1S0GicvkJvM/JXslkzJo98+3Nzsn5Jln59pnLWjPvvGutmTlTo1lepmb5+kcTACYMGBaBNTK/WPK4p2kclZ47NbqGdAEWf1VyWPKjZtKZvrvVdrJGZw6ALR0n9U3jPzs6WPxdya4eF2+mDurjdKluDwAm+C6Y/C99v27+Xq7fZUHbU6eePikAFqr+g2Sv5GVHkL3hZ9SwRHKiTb8HAL50W/JQcj5ayBP9vbNRfEW/FwIjoH9f8lay0THOeIBpnVzb5DPtgPxKgjtk44sHwAMNnCeBomFB8fegYk/CwBvVPYuAsSG41QuJx44Oa/5rKJtxU7JC4sYYD4DfGrQoMZAg8ygwlRrPDt9wkP8fu2/TM1c7pwo3M6QWsFQDNklgQVwuqwKXoOD7MUOop0/oFqbD4kqKNX3ufKgLBuyQELeSpTYNQu8Njaaz+r1WMXMDL8WsCjVVXS0mrdWo96mRNQDE/u8qdaYIeBQvOHorw+5KSSqtwqo5Ei+bGOuOqU/SDWoACP2ftLigai9GI+KK5zaxKhZ9qqGvMY6xdmii/Z7EslHOrbB7S5LMOjUA4E/Hm5mSHqFXabGdKHUbFjhXQroM7RrrYNMnCZkKEHJszAJfA8BHGbIDkEspBxFLf6UAhGpsLHWHJKsl870dTdgn8xAEk4GwFAAyA4hbqc3jXQDAJrtI4YBFqYkjdnBLrrUUAIumGAcIN638AwagMjx91gbfXgAI/dANKM7iqe7KAM723BwptWk0m31KGfBThvE7yjgHGXOhcWIANu1uwvdWSc0lqnMQjG+GtTtgxKhJgyGZYvvxBSxDvOkm7LpjShgQol+ax1OTykZjZxV2q7uo9iMSUl7uphmrMdcje6SO9kWPogw80Ggel8IhjWsYhG2CLneLVBxgc75KvCuvbZ6btdoYUHL9baOgtVsccHdDHTltrpLANCbPGLtwxecB9PLyk0uJsI6SurRNN7QBEEZ/+ucmXwJE24Qs36OLnd8sCe/yYTBOtYdzKAHcBQC6rZNw1IzLc1V8kSR9qgWFtgcR7G6XcAS+FC0e1fbo6bWH5tG1X5I9NLUxoGRXa/twLV4vcWlZqzDRn90nWLa+Ow4BAHEFV7g+JotK8EE/TG19rxgCABZg6W2fvpMPFSWrdPoUvwgzfigADATu9KTWvkDgzrJNUvziPCQABgKvPTVH2xw5CJJV/2YbGoAOTO9n6ASAfnCcuVomDJi5e9fPzP8AmsXMJndcxcwAAAAASUVORK5CYII=\" style=\"width: 32px; height: 18.5px;\" width=\"32\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117px 8px; transform-origin: 117px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, rounded-off to 4 decimal places.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55px 8px; transform-origin: 55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e----------------------\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 23px 8px; transform-origin: 23px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNOTE: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 91px 8px; transform-origin: 91px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo encourage vectorization , \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 121px 8px; transform-origin: 121px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFOR and WHILE loops are disabled\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 53px 8px; transform-origin: 53px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. If you know the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.cs.mtsu.edu/~xyang/3080/recurrenceRelations.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"font-weight: 700; text-decoration-line: underline; \"\u003emath\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 68px 8px; transform-origin: 68px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, this problem can be solved in less than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e15\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 76.5px 8px; transform-origin: 76.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e lines of code. However, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54.5px 8px; transform-origin: 54.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esolutions up to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8px 8px; transform-origin: 8px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; text-decoration-line: underline; \"\u003e50\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 121px 8px; transform-origin: 121px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e lines of code will still be accepted\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52px 8px; transform-origin: 52px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The semicolon (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; font-weight: 700; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 159px 8px; transform-origin: 159px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), shall be considered as an end-of-line character. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function t = T(P,x)\r\n t = round(log(abs(R(x))),4);\r\n function r = R(x)\r\n if x == 0\r\n r = polyval(P,x);\r\n else\r\n r = x * R(x-1) + polyval(P,x);\r\n end\r\n end\r\nend","test_suite":"%%\r\nP = [1 2 1]; x = 0:10;\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [0.0000 1.6094 2.9444 4.2905 5.7589 7.3908 9.1876 11.1344 13.2140 15.4113 17.7139];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [1 0 1]; x = [10 100 1000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [17.2030 365.8380 5914.2268];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [-1 1 -1 1]; x = [10 100 1000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [17.2030 365.8380 5914.2268];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [1 2 3 4 5]; x = [10 100 1000 10000];\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [19.7933 368.4283 5916.8171 82113.6167];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = [11 -22 33 -44 55 -66]; x = 12345;\r\nt = T(P,x);\r\nt_correct = 103969.6836;\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = 2:2:10; x = P.*200;\r\nt = arrayfun(@(i) T(P,i),x);\r\nt_correct = [2005.8827 4557.3328 7317.9383 10214.4041 13211.9064];\r\nassert(isequal(t,t_correct))\r\n%%\r\nP = 2:2:100; x = P./2;\r\nt = arrayfun(@(i) T(P,i),x);\r\ns = round([mean(t) median(t) mode(t) std(t)],4);\r\ns_correct = [163.8877 167.4357 7.8823 59.1051];\r\nassert(isequal(s,s_correct))\r\n%%\r\nfiletext = fileread('T.m');\r\nnot_allowed = contains(filetext, 'for') || contains(filetext, 'while') || contains(filetext, 'java') || contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'regexp') || contains(filetext, 'eval') || contains(filetext, 'assignin');\r\nassert(~not_allowed)\r\nassert(count(filetext, 'function')==1)\r\n\r\nc = 0;\r\nfor s = deblank(strtrim(splitlines(filetext)))'\r\n if ~isempty(s{1}) \u0026\u0026 ~isequal(s{1}(1),'%')\r\n c = c + numel(find(s{1}==';'));\r\n if ~isequal(s{1}(end),';')\r\n c = c + 1;\r\n end\r\n end\r\nend\r\nassert(c\u003c=50)","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":255988,"edited_by":255988,"edited_at":"2023-02-14T06:56:22.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":"2023-02-07T04:59:54.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-02-03T04:56:19.000Z","updated_at":"2025-11-26T02:41:15.000Z","published_at":"2023-02-06T08:01:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a natural number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and a polynomial function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, we define a recursive function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(0)=P(0)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x) = x\\\\cdot R(x-1) + P(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP(x) = x^2+2x+1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (or in Matlab array form, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP = [1 2 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(0) = P(0)=0^2+2\\\\cdot 0 + 1=1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(1) = 1\\\\cdot R(0)+P(1)=1\\\\cdot 1+1^2+2\\\\cdot 1 + 1=5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(2) = 2\\\\cdot R(1)+P(2)=2\\\\cdot 5+2^2+2\\\\cdot 2 + 1=19\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(3) = 3\\\\cdot R(2)+P(3)=3\\\\cdot 19+3^2+2\\\\cdot 3 + 1=73\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e ...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(10) = 49320491\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e and so on...\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe can see that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e can grow very quickly. Therefore, we will instead calculate:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x) = \\\\text{log}|R(x)|\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, that is, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis equal to the logarithm (base-\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e) of the absolute value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eR(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGiven a polynomial array \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and an integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, find the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eT(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, rounded-off to 4 decimal places.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e----------------------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eTo encourage vectorization , \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFOR and WHILE loops are disabled\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. If you know the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.cs.mtsu.edu/~xyang/3080/recurrenceRelations.html\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emath\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, this problem can be solved in less than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e15\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e lines of code. However, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esolutions up to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e50\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e lines of code will still be accepted\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The semicolon (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), shall be considered as an end-of-line character. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":734,"title":"Ackermann's Function","description":"Ackermann's Function is a recursive function that is not 'primitive recursive.'\r\n\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Ackermann_function Ackermann Function\u003e\r\n\r\nThe first argument drives the value extremely fast.\r\n\r\nA(m, n) =\r\n\r\n* n + 1 if m = 0\r\n* A(m − 1, 1) if m \u003e 0 and n = 0\r\n* A(m − 1,A(m, n − 1)) if m \u003e 0 and n \u003e 0\r\n\r\n \r\nA(2,4)=A(1,A(2,3)) = ... = 11.\r\n\r\n % Range of cases\r\n % m=0 n=0:1024\r\n % m=1 n=0:1024\r\n % m=2 n=0:128\r\n % m=3 n=0:6\r\n % m=4 n=0:1\r\n\r\nThere is some deep recusion.\r\n\r\n*\r\nInput:* m,n\r\n\r\n*Out:* Ackerman value\r\n\r\n\r\nAckermann(2,4) = 11\r\n\r\nPractical application of Ackermann's function is determining compiler recursion performance.","description_html":"\u003cp\u003eAckermann's Function is a recursive function that is not 'primitive recursive.'\u003c/p\u003e\u003cp\u003e\u003ca href=\"http://en.wikipedia.org/wiki/Ackermann_function\"\u003eAckermann Function\u003c/a\u003e\u003c/p\u003e\u003cp\u003eThe first argument drives the value extremely fast.\u003c/p\u003e\u003cp\u003eA(m, n) =\u003c/p\u003e\u003cul\u003e\u003cli\u003en + 1 if m = 0\u003c/li\u003e\u003cli\u003eA(m − 1, 1) if m \u003e 0 and n = 0\u003c/li\u003e\u003cli\u003eA(m − 1,A(m, n − 1)) if m \u003e 0 and n \u003e 0\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eA(2,4)=A(1,A(2,3)) = ... = 11.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e% Range of cases\r\n% m=0 n=0:1024\r\n% m=1 n=0:1024\r\n% m=2 n=0:128\r\n% m=3 n=0:6\r\n% m=4 n=0:1\r\n\u003c/pre\u003e\u003cp\u003eThere is some deep recusion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,n\u003c/p\u003e\u003cp\u003e\u003cb\u003eOut:\u003c/b\u003e Ackerman value\u003c/p\u003e\u003cp\u003eAckermann(2,4) = 11\u003c/p\u003e\u003cp\u003ePractical application of Ackermann's function is determining compiler recursion performance.\u003c/p\u003e","function_template":"function vAck = ackermann(m,n)\r\n set(0,'RecursionLimit',512);\r\n\r\n vAck=0;\r\n\r\nend","test_suite":"%%\r\nm=0;\r\nn=1;\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=0;\r\nn=1024;\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=0;\r\nn=randi(1024)\r\nAck = n+1;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=1;\r\nn=1024\r\nAck = n+2;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=1;\r\nn=randi(1024)\r\nAck = n+2;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=2;\r\nn=randi(128)\r\nAck = 2*n+3;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=3;\r\nn=6;\r\nAck = 509;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=3;\r\nn=randi(6)\r\nAck = 2^(n+3)-3;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=4;\r\nn=0;\r\nAck = 13;\r\nassert(isequal(ackermann(m,n),Ack))\r\n%%\r\nm=4;\r\nn=1; % Fails at RecursionLimit 1030; Create Special\r\nAck = 65533;\r\nassert(isequal(ackermann(m,n),Ack))","published":true,"deleted":false,"likes_count":4,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":25,"created_at":"2012-06-02T02:10:19.000Z","updated_at":"2026-02-15T03:26:49.000Z","published_at":"2012-06-02T02:36:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann's Function is a recursive function that is not 'primitive recursive.'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Ackermann_function\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann Function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first argument drives the value extremely fast.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m, n) =\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en + 1 if m = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m − 1, 1) if m \u0026gt; 0 and n = 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(m − 1,A(m, n − 1)) if m \u0026gt; 0 and n \u0026gt; 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(2,4)=A(1,A(2,3)) = ... = 11.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[% Range of cases\\n% m=0 n=0:1024\\n% m=1 n=0:1024\\n% m=2 n=0:128\\n% m=3 n=0:6\\n% m=4 n=0:1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere is some deep recusion.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOut:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Ackerman value\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAckermann(2,4) = 11\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePractical application of Ackermann's function is determining compiler recursion performance.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":43963,"title":"Finding operators in a MATLAB function in a string.","description":"The aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\r\n\r\nFor example:\r\n\r\n 'min(var1+var2,2)' =\u003e true\r\n\r\n 'min(1,2)+min(3,4)' =\u003e false\r\n\r\n 'min(min(1,2),3))' =\u003e false\r\n\r\n 'min(min(1,var1+2),3))' =\u003e true\r\n\r\n '4*var1' =\u003e false, there is no MATLAB function\r\n\r\nYou can assume that all opening brackets are closed.","description_html":"\u003cp\u003eThe aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\u003c/p\u003e\u003cp\u003eFor example:\u003c/p\u003e\u003cpre\u003e 'min(var1+var2,2)' =\u0026gt; true\u003c/pre\u003e\u003cpre\u003e 'min(1,2)+min(3,4)' =\u0026gt; false\u003c/pre\u003e\u003cpre\u003e 'min(min(1,2),3))' =\u0026gt; false\u003c/pre\u003e\u003cpre\u003e 'min(min(1,var1+2),3))' =\u0026gt; true\u003c/pre\u003e\u003cpre\u003e '4*var1' =\u0026gt; false, there is no MATLAB function\u003c/pre\u003e\u003cp\u003eYou can assume that all opening brackets are closed.\u003c/p\u003e","function_template":"function y = FindingOperators( x )\r\n y = x;\r\nend","test_suite":"%%\r\nx = 'min(var1+var2,2)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(1,2)+min(3,4)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(1,2),3))';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(1,var1+2),3))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n\r\n%%\r\n\r\nx = '4*var1';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4*(var1+2)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n\r\n%%\r\n\r\nx = 'max(min(1,var1-2),3))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2/var1)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+abs(2+min(1+5,1))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+max(2,min(1+5,1))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = '4+max((2,min(1+5,1)))';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(2-1,1)+abs(-5)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(var1,1)+abs(-5)';\r\ny_correct = false;\r\nassert(isequal(FindingOperators(x),y_correct))\r\n\r\n%%\r\n\r\nx = 'min(min(var1,var2),1+3)';\r\ny_correct = true;\r\nassert(isequal(FindingOperators(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":55046,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-12-22T09:44:46.000Z","updated_at":"2016-12-22T15:57:10.000Z","published_at":"2016-12-22T09:44:46.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe aim is to find if there is an operator inside a MATLAB function call in a formula. The input is a string and the output is a boolean which is true if the string contains at least one operator in a function call, and false otherwise.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 'min(var1+var2,2)' =\u003e true\\n\\n 'min(1,2)+min(3,4)' =\u003e false\\n\\n 'min(min(1,2),3))' =\u003e false\\n\\n 'min(min(1,var1+2),3))' =\u003e true\\n\\n '4*var1' =\u003e false, there is no MATLAB function]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume that all opening brackets are closed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); \r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); \r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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Hole-In-Wall: Solve Problem 47, Score=0, Figure Vertices 11, Hole Vertices 10","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \r\nThis Challenge is to solve ICFP problems 47 according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u003c= epsilon/1000000. Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon) \r\nThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\r\nThe function template includes routines to read ICFP problem files and to write ICFP solution files.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 775px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 387.5px; transform-origin: 407px 387.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8.05px; transform-origin: 14px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 146.65px 8.05px; transform-origin: 146.65px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.95px 8.05px; transform-origin: 29.95px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 8.05px; transform-origin: 1.95px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 283px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 141.5px; text-align: left; transform-origin: 384px 141.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.9px 8.05px; transform-origin: 379.9px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 541px;height: 262px\" 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\" data-image-state=\"image-loaded\" width=\"541\" height=\"262\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 191.767px 8.05px; transform-origin: 191.767px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 47 according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 138.367px 8.05px; transform-origin: 138.367px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8.05px; transform-origin: 384px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u0026lt;= epsilon/1000000. Lsqr is length squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250.1px 8.05px; transform-origin: 250.1px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 152.783px 8.05px; transform-origin: 152.783px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 8.05px; transform-origin: 3.88333px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8.05px; transform-origin: 384px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 303.4px 8.05px; transform-origin: 303.4px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files and to write ICFP solution files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.883px 8.05px; transform-origin: 375.883px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 42.7833px 8.05px; transform-origin: 42.7833px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems Page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256.35px 8.05px; transform-origin: 256.35px 8.05px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP047(hxy,pxy,mseg,epsilon)\r\n%Problem 47 shows potential for recursion but a brute force with reduction can quickly solve\r\n% nH equals nP-1 and Score=0 optimally, nH is before repeating row 1\r\n% Since Score=0 then all hole vertices are covered. \r\n% Know that only 1 figure vertex not on a hole vertex\r\n% Assume that the longest segment spans two hole vertices not necessarily sequential hole nodes\r\n% Identify longest segment and associated hole vertices\r\n% Try all permutations of nchoosek(1:nP,nP-1) after reduced by Long segment nodes and hole nodes \r\n% Verify segments where both nodes are in nck set are correct length\r\n% For unselected vertex find all segments containing and create valid pt sets\r\n% for each segment constraint. Find point common to all constraint sets\r\n\r\n npxy=pxy;\r\n nseg=size(mseg,1);\r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %hplot4(hxy,npxy,mseg,nseg,4,msegMM);\r\n \r\n hxy1=hxy(1:end-1,:);\r\n np=size(npxy,1); %\r\n vpn=zeros(np,1);\r\n pnchk=nchoosek(1:np,np-1);\r\n \r\n % Note: *** Indicates line was changed from working program\r\n ptrLseg=find(msegMM(:,2)==0,1,'first'); % *** Find max L segment\r\n nodesL=mseg(ptrLseg,:); % figure nodes of longest figure segment\r\n nodesLMM=msegMM(ptrLseg,:); % Min and Max of selected long figure segment\r\n found=0;\r\n nh=size(hxy,1);\r\n for hi=1:nh-2 % search all hole vertices hi to hj that matches long figure segment\r\n for hj=hi+1:nh-1\r\n if prod([0 0])\u003c=0 % *** Find pair of valid hole vertices\r\n found=1;\r\n break;\r\n end\r\n end %hj nh\r\n if found,break;end\r\n end %hi nh\r\n % hi,hj Hole indices that are nodes that fit longest segment\r\n % that will need to be either of nodesL\r\n \r\n % remove nchoosek vectors that omit the long segment of nodesL\r\n pnchkval=sum([0 0],2)\u003e1; % ***\r\n pnchk=pnchk(pnchkval,:);\r\n Lpnchk=size(pnchk,1); % Length of final nchoosek matrix\r\n \r\n mperms=perms(1:np-1); % fast repetitve perms method, create a mapping array\r\n for ipnchk=1:Lpnchk %subset of figure vertices to place onto hole vertices\r\n vpnchk=pnchk(ipnchk,:);\r\n phset=vpnchk(mperms); \r\n % remove matrix rows that lack nodesL in hi,hj columns\r\n % Massive reduction in phset matrix \r\n permvalid=phset(:,hi)==0 | phset(:,hi)==0; % *** match nodesL\r\n phset=phset(permvalid,:);\r\n permvalid=phset(:,hj)==0 | phset(:,hj)==0; % *** match nodesL\r\n phset=phset(permvalid,:); % Final reduced permutation set that must have nodesL in cols hi,hj\r\n \r\n nphset=size(phset,1); % greatly reduced from 10! for each nchoosek vector\r\n \r\n for i=1:nphset\r\n npxy=npxy*0;\r\n vphset=phset(i,:); \r\n npxy(vphset,:)=hxy1; % load hole vertices into figure vertex matrix, one row is [0 0] unset\r\n vpn=0*vpn;\r\n vpn(vphset)=1; % vpn is vector that indicates used figure vertices\r\n fail=0;\r\n for segptr=1:nseg\r\n if prod(vpn(mseg(segptr,:)))\r\n L2seg=sum((npxy(mseg(segptr,1),:)-npxy(mseg(segptr,2),:)).^2);\r\n if prod([0 0])\u003e0 % *** Verify L2seg is valid length squared\r\n fail=1;\r\n break;\r\n end\r\n end\r\n end\r\n if fail,continue;end %length of subset placed vertices placed on hole ver failed\r\n %Hole Points covered. Have 1 free point to place constrained by its segments\r\n node=find(vpn==0); % Free node to place\r\n \r\n cptr=1;\r\n for fseg=1:nseg\r\n if prod(vpn(mseg(fseg,:))),continue;end % Both seg vertices placed\r\n MM=msegMM(fseg,:); % Create [Min Max] vector\r\n Node2=mseg(fseg,:);\r\n Node2(Node2==node)=[]; % Reduce Node2 to a single value of the set vertex\r\n \r\n if cptr==1 % create an initial list of all in range and then inpolygon\r\n Lmm=ceil(MM(2)^.5);\r\n dmap=(0:Lmm).^2;\r\n dmap=repmat(dmap,Lmm+1,1);\r\n dmap=dmap+dmap'; % Create a 2D map of distance squared from [0,0]. dmap(1,1) is [0,0]\r\n % This 2D map is of the Positive XY quadrant. The goal will be to find all valid [dx dy]\r\n dmap(dmap\u003cMM(1))=0; % Remove Points less than Min Seg length\r\n dmap(1,:)=0; % *** Remove Points greater than Max Seg length\r\n [dx,dy]=find(dmap);\r\n dx=dx-1; dy=dy-1; % remove 1,1 offset from grid\r\n dxy=[dx dy;dx -dy;-dx dy;-dx -dy];% Create all valid deltas by symmetry about [0,0]\r\n mxy=dxy+npxy(Node2,:);% Create matrix of all points in the valid region\r\n % remove negatives from hole comparison as hole is all positive\r\n mxy=mxy(mxy(:,1)\u003e=0,:); % Speed option remove all points with neg x values\r\n mxy=mxy(1,:); % *** Speed option remove all points with neg y values\r\n in=inpolygon(mxy(:,1),mxy(:,2),hxy(:,1),hxy(:,2));\r\n mxy=mxy(in,:); % reduce to in-hole points\r\n cptr=2;\r\n else % test points from m for additional new fseg constraint and prune\r\n Lmxy=size(mxy,1);\r\n vmxy=ones(Lmxy,1); % Valid mxy vector\r\n for ptrmxy=1:Lmxy\r\n d2=sum((mxy(ptrmxy,:)-npxy(Node2,:)).^2); % Calc dist squared from mxy to Node2\r\n if d2\u003cMM(1),vmxy(ptrmxy)=0;end % clear vmxy for too short\r\n if d2\u003eMM(2),vmxy(ptrmxy)=0;end % clear vmxy for too long\r\n end\r\n mxy=mxy(vmxy\u003e0,:);\r\n if isempty(mxy) %If no points left in mxy then vertex could not reach from set nodes\r\n fail=1;\r\n break;\r\n end\r\n end % cptr==1\r\n end % fseg 1:nseg\r\n if fail,continue;end\r\n \r\n npxy(node,:)=mxy(1,:); % solution found are all valid??? Possible seg fail\r\n \r\n fprintf('Solution found\\n');\r\n %hplot4(hxy,npxy,mseg,nseg,4,msegMM);\r\n return;\r\n \r\n end % nphset\r\n end % ipnchk\r\n \r\n fprintf('No solution found\\n');\r\nend %Solve_ICFP047\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2]. The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n delta=floor(epsilon*Lseg/1000000);\r\n msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n\r\n%These routines can be used to read ICFP problems, write ICFP text file, and visualize the data\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n% fid=fopen([num2str(pid) '.problem'],'r');\r\n% pstr=fgetl(fid);\r\n% fclose(fid)\r\n% \r\n% Lpstr=length(pstr);\r\n% holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n% epsidx=findstr('\"epsilon\":',pstr);\r\n% figidx=findstr(',\"figure\"',pstr);\r\n% edgidx=findstr('\"edges\":[[',pstr);\r\n% veridx=findstr('\"vertices\":[[',pstr);\r\n% epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n% \r\n% hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n% hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n% \r\n% pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n% \r\n% mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n% function write_submission(npxy,pid)\r\n% fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n% fid=fopen(fname,'wt'); % t for notepad editing\r\n% \r\n% %Create ICFP submission\r\n% fprintf('{\"vertices\": [');\r\n% fprintf(fid,'{\"vertices\": [');\r\n% for i=1:size(npxy,1)-1 \r\n% fprintf('[%i,%i],',npxy(i,:));\r\n% fprintf(fid,'[%i,%i],',npxy(i,:));\r\n% end \r\n% fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n% fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n% fclose(fid);\r\n% end\r\n\r\n\r\n% function hplot(vxy,qxy,mseg,Lmseg,id)\r\n% %Need check of segment crossing a hole segment but ignore endpoint\r\n% [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n% figure(id)\r\n% plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n% hold on\r\n% plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n% plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n% for i=1:size(qxy,1)%length(xq)\r\n% text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i),'FontSize',12);\r\n% end\r\n% \r\n% for i=1:Lmseg\r\n% if in(mseg(i,1))+in(mseg(i,2))\u003c2\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment to OOB pt\r\n% else\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-')\r\n% end\r\n% text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n% end\r\n% \r\n% %o+*.x_|sd^v\u003e\u003cph\r\n% %colors ymcrgbwk\r\n% \r\n% axis tight\r\n% axis ij\r\n% hold off \r\n% end % hplot\r\n\r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n% segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n% [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n% figure(id)\r\n% plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n% hold on\r\n% plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n% plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n% for i=1:size(qxy,1) %length(xq)\r\n% text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n% end\r\n% \r\n% for i=1:Lmseg\r\n% d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n% if d2seg\u003csegMNM(i,1)\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n% elseif d2seg\u003esegMNM(i,3)\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n% else\r\n% plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n% end\r\n% text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n% end\r\n% \r\n% %o+*.x_|sd^v\u003e\u003cph\r\n% %colors ymcrgbwk\r\n% \r\n% axis tight\r\n% axis ij\r\n% hold off\r\n% end % hplot3\r\n\r\n\r\n","test_suite":"%%\r\n% ICFP Problem 47 \r\n% 75% of hole edges not covered in solution. All hole vertices covered.\r\n% possible method force longest fig segment onto pair hole vertices then perms\r\n% brute force processing will take 180 seconds so not part of this cody challenge\r\ntic\r\n% ICFP Problem Id 47\r\n% nh 10 np 11\r\nepsilon=41323;\r\nhxy=[6 14;36 19;40 17;69 0;79 21;41 36;36 33;16 44;7 34;0 28;6 14];\r\npxy=[0 11;1 85;8 56;11 0;14 45;14 59;14 88;30 37;30 56;56 85;67 64];\r\nmseg=[1 4;4 8;8 5;5 1;8 11;11 10;10 9;9 8;5 6;6 9;9 7;7 2;2 6;6 3;3 5];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP047(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\nfor i=1:nseg\r\n L2pxyseg = sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n L2npxyseg = sum((npxy(mseg(i,1),:)-npxy(mseg(i,2),:)).^2);\r\n if abs(L2npxyseg/L2pxyseg-1)*1000000 \u003e epsilon\r\n valid = 0;\r\n break;\r\n end\r\nend\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-07-21T17:55:33.000Z","updated_at":"2021-07-22T01:54:06.000Z","published_at":"2021-07-22T01:54:06.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. A final solution is shown to aid in programming. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"262\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"541\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to solve ICFP problems 47 according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole. The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. Brute force of problem 47 may take 180 seconds due to the 10 hole vertices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) all lengths squared of npxy segments must match the pxy segments within an allowed epsilon, abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1)\u0026lt;= epsilon/1000000. Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enpxy=Solve_ICFP047(hxy, pxy, mseg, epsilon)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge requires a Score of zero. The npxy vertices set must contain an nchoosek(1:nP,nP-1) permutation of the hole vertices as number of figure vertices,nP, equals hole vertices, nH, plus one. One method would be to reduce the nchoosek to force the longest figure segment to fit across a pair of hole vertices. This problem with its solution shown shows that a recursive point to available hole vertices could be a more general solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes routines to read ICFP problem files and to write ICFP solution files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2026,"title":"Skyscrapers - Puzzle","description":"The Skyscraper puzzle challenge comes from \u003chttp://logicmastersindia.com/home/ Logic Masters India\u003e and \u003chttp://www.conceptispuzzles.com/ Games' Concept is Puzzles\u003e. \r\n\r\nCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\r\n\r\n*Input:* [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\r\n\r\n*Output:* M an NxN matrix\r\n\r\n*Example:*\r\n\r\n vr=[0 0 3 0 0]';\r\n vL=[3 0 0 1 0]';\r\n vd=[0 0 0 0 0];\r\n vu=[5 2 0 0 0];\r\n\r\n M\r\n 5 4 2 1 3\r\n 4 5 1 3 2\r\n 3 2 4 5 1\r\n 2 1 3 4 5\r\n 1 3 5 2 4\r\n\r\n*Algorithm Discussion:*\r\n\r\n 1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n 2) Calc Skyscraper count from Left and Right\r\n 3) Determine subset of SkyVectors possible for each Row and Column\r\n 4) Sort the Qty of 2*N possible solutions\r\n 5) Recursion from least to most valid SkyVectors\r\n 6) In recursion verify valid overlay or return\r\n","description_html":"\u003cp\u003eThe Skyscraper puzzle challenge comes from \u003ca href = \"http://logicmastersindia.com/home/\"\u003eLogic Masters India\u003c/a\u003e and \u003ca href = \"http://www.conceptispuzzles.com/\"\u003eGames' Concept is Puzzles\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M an NxN matrix\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003evr=[0 0 3 0 0]';\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eM\r\n 5 4 2 1 3\r\n 4 5 1 3 2\r\n 3 2 4 5 1\r\n 2 1 3 4 5\r\n 1 3 5 2 4\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm Discussion:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n2) Calc Skyscraper count from Left and Right\r\n3) Determine subset of SkyVectors possible for each Row and Column\r\n4) Sort the Qty of 2*N possible solutions\r\n5) Recursion from least to most valid SkyVectors\r\n6) In recursion verify valid overlay or return\r\n\u003c/pre\u003e","function_template":"function m=solve_skyscrapers(vr,vL,vd,vu)\r\n m=[];\r\nend","test_suite":"%%\r\n%Games Feb 2014 #1\r\nvr=[0 0 1 0 5]'; %1\r\nvL=[0 4 4 0 0]';\r\nvd=[2 2 0 1 3];\r\nvu=[3 0 0 2 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd; % view down check\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view Left check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m); % view Up check\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #2\r\nvr=[0 4 0 2 0]'; %2\r\nvL=[5 1 0 0 0]';\r\nvd=[0 0 3 0 0];\r\nvu=[4 1 2 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #3\r\nvr=[5 2 2 0 0]'; %3\r\nvL=[0 3 0 3 4]';\r\nvd=[5 0 0 0 0];\r\nvu=[0 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #4\r\nvr=[0 0 4 5 0]'; %4\r\nvL=[0 0 0 0 0]';\r\nvd=[2 0 2 3 0];\r\nvu=[0 0 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #5\r\nvr=[3 5 0 0 0]'; %5\r\nvL=[0 0 4 0 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[2 0 1 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\n\r\n%%\r\nvr=[0 0 3 0 0]'; %Games Feb 2014 #6\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n if mt(k,z)\u003eshi\r\n shi=mt(k,z);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n if mt(z,k)\u003eshi\r\n shi=mt(z,k);\r\n c=c+1;\r\n end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-29T19:42:36.000Z","updated_at":"2026-01-08T14:21:06.000Z","published_at":"2013-11-29T22:09:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Skyscraper puzzle challenge comes from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://logicmastersindia.com/home/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames' Concept is Puzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M an NxN matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[vr=[0 0 3 0 0]';\\nvL=[3 0 0 1 0]';\\nvd=[0 0 0 0 0];\\nvu=[5 2 0 0 0];\\n\\nM\\n 5 4 2 1 3\\n 4 5 1 3 2\\n 3 2 4 5 1\\n 2 1 3 4 5\\n 1 3 5 2 4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Discussion:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\\n2) Calc Skyscraper count from Left and Right\\n3) Determine subset of SkyVectors possible for each Row and Column\\n4) Sort the Qty of 2*N possible solutions\\n5) Recursion from least to most valid SkyVectors\\n6) In recursion verify valid overlay or return]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1978,"title":"Sokoban: Puzzle 10.45","description":"The \u003chttp://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 Sokoban Site\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55. \u003chttp://en.wikipedia.org/wiki/Sokoban wiki Sokoban reference\u003e. \r\n\r\nThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\r\n\r\nSokoban can not jump blocks or move diagonally.\r\n\r\nThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7). \r\n\r\nSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\r\n\r\n*Input:* Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\r\n\r\n*Output:* Moves, Vector of [-1 +1 -nr +nr] values\r\n\r\n*Scoring:* Sum of Moves and Pushes\r\n\r\n*Examples:* \r\n\r\nMap\r\n\r\n 11111111\r\n 11111111 Moves=[5] push right for a 5 row array\r\n 11042311\r\n 11111111\r\n 11111111\r\n\r\n*Test Suite Visualization:* A visualization option is provided.\r\n\r\n*Algorithms:* Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid. \r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138\"\u003eSokoban Site\u003c/a\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55. \u003ca href = \"http://en.wikipedia.org/wiki/Sokoban\"\u003ewiki Sokoban reference\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/p\u003e\u003cp\u003eSokoban can not jump blocks or move diagonally.\u003c/p\u003e\u003cp\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/p\u003e\u003cp\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Sum of Moves and Pushes\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMap\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e11111111\r\n11111111 Moves=[5] push right for a 5 row array\r\n11042311\r\n11111111\r\n11111111\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eTest Suite Visualization:\u003c/b\u003e A visualization option is provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithms:\u003c/b\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/p\u003e","function_template":"function moves=solve_Sokoban(m)\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n\r\n moves=[];\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\nvisualize=0;\r\nif visualize\r\n figure(1); % Start\r\n map=[.5 .5 .5;0 0 0;.5 .5 .5;0 1 0;0 0 1;\r\n 1 0 0;1 1 0;0 0 0;1 0 1;.5 .5 .5];\r\n colormap(map);\r\n figure(2); % Move map\r\n% -1 0 1 2 3 4 5 6 7 8\r\n% -1 color limit, 8 color limit\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n colormap(map)\r\nend\r\n\r\n%Sokoban map http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 \r\n%Puzzle 45 \r\nsmap=[0 0 0 0 0 0;0 3 2 2 4 0;3 3 2 0 2 0;3 5 0 0 1 1];\r\n[nr,nc]=size(smap);\r\nm=ones(nr+4,nc+4);\r\nm(3:end-2,3:end-2)=smap;\r\n\r\nif visualize\r\n im=m;\r\n mend=size(map,1)-2;\r\n im(1)=-1;im(end)=mend;\r\n figure(1);imagesc(im)\r\n m\r\nend\r\n\r\ntic\r\nmoves=solve_Sokoban(m);\r\ntoc\r\n\r\n% Check Solution\r\n valid=1;\r\n ptr=find(m==4);\r\n pushes=0;\r\n if isempty(ptr),ptr=find(m==7);end\r\n for i=1:length(moves)\r\n mv=moves(i);\r\n mvptr=m(ptr+mv);\r\n mvptr2=m(ptr+2*mv);\r\n if mvptr==1 % Illegal run into wall\r\n valid=0;\r\n break;\r\n end\r\n if (mvptr2==5 || mvptr2==2 || mvptr2==1) \u0026\u0026 (mvptr==5 || mvptr==2) % Illegal double block push\r\n valid=0;\r\n break;\r\n end\r\n if mvptr==0 || mvptr==3\r\n m(ptr)=m(ptr)-4;\r\n m(ptr+mv)=m(ptr+mv)+4;\r\n ptr=ptr+mv;\r\n elseif mvptr==2 || mvptr==5\r\n m(ptr)=m(ptr)-4;\r\n m(ptr+2*mv)=m(ptr+2*mv)+2;\r\n m(ptr+mv)=m(ptr+mv)-2+4;\r\n ptr=ptr+mv;\r\n pushes=pushes+1;\r\n end\r\n end\r\n \r\n fprintf('Moves %i Pushes %i\\n',length(moves),pushes)\r\n valid=valid \u0026\u0026 nnz(m==3)==0 \u0026\u0026 nnz(m==7)==0;\r\n assert(valid)\r\n\r\nif visualize \u0026\u0026 valid\r\n % display moves\r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n ptr=find(im==4);\r\n if isempty(ptr),ptr=find(im==7);end\r\n for i=1:length(moves)\r\n mv=moves(i);\r\n mvptr=im(ptr+mv);\r\n if mvptr==0 || mvptr==3\r\n im(ptr)=im(ptr)-4;\r\n im(ptr+mv)=im(ptr+mv)+4;\r\n ptr=ptr+mv;\r\n elseif mvptr==2 || mvptr==5\r\n im(ptr)=im(ptr)-4;\r\n im(ptr+2*mv)=im(ptr+2*mv)+2;\r\n im(ptr+mv)=im(ptr+mv)-2+4;\r\n ptr=ptr+mv;\r\n end\r\n \r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n end\r\n \r\nend % vis and valid\r\n\r\n\r\nmovs=length(moves);\r\nassignin('caller','score',min(200,max(0,movs+pushes)));","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2013-11-11T01:51:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-10T23:21:50.000Z","updated_at":"2025-12-03T12:16:08.000Z","published_at":"2013-11-11T01:51:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.game-sokoban.com/index.php?mode=level\u0026amp;lid=16138\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sokoban\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ewiki Sokoban reference\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban can not jump blocks or move diagonally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sum of Moves and Pushes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[11111111\\n11111111 Moves=[5] push right for a 5 row array\\n11042311\\n11111111\\n11111111]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTest Suite Visualization:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A visualization option is provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithms:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2005,"title":"BattleShip - Seaman (1) thru Admiral(6) : CPU Time Scoring(msec)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThis Challenge is to complete three full sets of Battleship in minimal time.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Scoring:* Total Time (msec)\r\n\r\n*Example:*\r\n\r\n r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n \r\n m b\r\n 000000000000 000000000000\r\n 077757777770 000011000000\r\n 077777777770 000000000000\r\n 077777777770 000100010000\r\n 077777777770 000100010000\r\n 077777777770 010000010000\r\n 077777777770 010000010010\r\n 027777777760 010000000010\r\n 077777777770 000101000010\r\n 077777777770 000000000000\r\n 077777477770 010001100100\r\n 000000000000 000000000000\r\n\r\n*Algorithm:* \r\n\r\n 1) Initialize processing array based upon input matrix.\r\n 2) Implement a cycling check of driven array changes\r\n 3) Quick Test of Change every single Unknown serially\r\n 4) Evolve and check if complete solution created\r\n 5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003eGames Magazine Battleships\u003c/a\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/p\u003e\u003cp\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/p\u003e\u003cp\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/p\u003e\u003cp\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/p\u003e\u003cp\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e b; A binary 12x12 array\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total Time (msec)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003em b\r\n000000000000 000000000000\r\n077757777770 000011000000\r\n077777777770 000000000000\r\n077777777770 000100010000\r\n077777777770 000100010000\r\n077777777770 010000010000\r\n077777777770 010000010010\r\n027777777760 010000000010\r\n077777777770 000101000010\r\n077777777770 000000000000\r\n077777477770 010001100100\r\n000000000000 000000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Initialize processing array based upon input matrix.\r\n2) Implement a cycling check of driven array changes\r\n3) Quick Test of Change every single Unknown serially\r\n4) Evolve and check if complete solution created\r\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs\r\n\u003c/pre\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"assignin('caller','score',2000);\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 1-Seaman\r\nr=[0 2 2 3 1 1 1 1 2 2 5 0]';\r\nc=[0 1 0 1 1 2 6 0 5 0 4 0];\r\nm(2,2)=1;\r\nm(2,6)=1;\r\nm(4,9)=3;\r\n\r\n%tz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\n%tt=tz+cputime-time0\r\ntt=cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 2-Petty Officer\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 3-Ensign\r\nr=[0 3 0 4 1 0 0 1 2 1 8 0]';\r\nc=[0 5 1 1 3 1 1 1 1 3 3 0];\r\nm(4,7)=1;\r\nm(4,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 4-Captain\r\nr=[0 1 2 2 2 2 5 0 5 0 1 0]';\r\nc=[0 5 0 0 0 2 1 4 2 1 5 0];\r\nm(4,8)=0;\r\nm(7,10)=4;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 5-Commodore\r\nr=[0 1 1 5 0 3 1 3 2 1 3 0]';\r\nc=[0 2 2 1 0 2 1 6 0 5 1 0];\r\nm(6,4)=1;\r\nm(6,8)=0;\r\nm(7,10)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 6-Admiral\r\nr=[0 5 1 4 2 3 1 1 0 3 0 0]';\r\nc=[0 4 0 1 2 4 2 1 1 5 0 0];\r\nm(5,2)=1;\r\nm(10,7)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 1-Seaman\r\nr=[0 1 1 1 1 2 3 3 3 1 4 0]';\r\nc=[0 3 2 0 1 6 0 3 1 4 0 0];\r\nm(2,3)=1;\r\nm(8,5)=1;\r\nm(7,8)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 3-Ensign\r\nr=[0 3 0 0 2 4 3 2 1 4 1 0]';\r\nc=[0 2 2 5 2 3 0 3 0 2 1 0];\r\nm(7,2)=1;\r\nm(7,4)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 4-Captain\r\nr=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\nm(8,2)=2;\r\nm(2,5)=5;\r\nm(11,7)=4;\r\nm(8,11)=6;\r\n\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 5-Commodore\r\nr=[0 3 2 3 1 1 1 3 3 2 1 0]';\r\nc=[0 1 2 4 1 4 1 1 0 5 1 0];\r\nm(2,10)=5;\r\nm(8,4)=6;\r\nm(8,6)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 6-Admiral\r\nr=[0 5 1 0 3 0 1 5 2 3 0 0]';\r\nc=[0 0 4 2 5 2 1 2 1 1 2 0];\r\nm(2,10)=0;\r\nm(8,7)=0;\r\nm(10,5)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 1-Seaman\r\nr=[0 1 1 2 4 1 0 2 2 5 2 0]';\r\nc=[0 1 1 1 1 4 0 7 0 2 3 0];\r\nm(2,8)=0;\r\nm(8,3)=1;\r\nm(9,6)=0;\r\nm(5,11)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 3-Ensign\r\nr=[0 3 0 2 3 1 1 2 2 2 4 0]';\r\nc=[0 1 1 0 6 1 4 0 3 1 3 0];\r\nm(4,3)=0;\r\nm(5,6)=4;\r\nm(7,9)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 4-Captain\r\nr=[0 0 6 0 2 2 4 1 3 2 0 0]';\r\nc=[0 3 1 3 1 2 2 2 2 0 4 0];\r\nm(5,2)=0;\r\nm(9,4)=0;\r\nm(3,5)=4;\r\nm(6,11)=2;\r\nm(8,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 5-Commodore %\r\nr=[0 5 2 1 1 7 1 2 0 0 1 0]';\r\nc=[0 2 3 1 2 1 3 1 2 0 5 0];\r\nm(8,2)=1;\r\nm(5,11)=2;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 6-Admiral % Solved with with Bship HV .10 \r\n% solved recur .023\r\nr=[0 0 2 4 1 4 1 0 2 0 6 0]';\r\nc=[0 3 1 3 1 3 2 1 2 1 3 0];\r\nm(3,2)=0;\r\nm(4,5)=4;\r\nm(9,9)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\ntt\r\nassignin('caller','score',min(2000,floor(1000*tt)));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-17T23:26:01.000Z","updated_at":"2013-11-18T00:27:11.000Z","published_at":"2013-11-18T00:27:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total Time (msec)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm b\\n000000000000 000000000000\\n077757777770 000011000000\\n077777777770 000000000000\\n077777777770 000100010000\\n077777777770 000100010000\\n077777777770 010000010000\\n077777777770 010000010010\\n027777777760 010000000010\\n077777777770 000101000010\\n077777777770 000000000000\\n077777477770 010001100100\\n000000000000 000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created\\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":57555,"title":"Easy Sequences 91: Generalized McCarthy-91 Recursive Function","description":"The McCarthy 91 function is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\r\nThe McCarthy 91 function is defined for integer as: \r\n \r\nRemarkably, the function yields for all (hence, the function name).\r\nA more generalized form of the McCarthy's recursive function is defined below: \r\n \r\nFor positive integers , , , , and . \r\nThe expression , means applying the function , number of times:\r\n \r\nThis means that the 'original' McCarthy-91 function is actually , with , , and .\r\nGiven integers, , , , , and , evaluate the value of .\r\n--------------------\r\nNOTE: The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 454px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 227px; transform-origin: 407px 227px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/McCarthy_91_function\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMcCarthy 91 function\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 286.5px 8px; transform-origin: 286.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 167.5px 8px; transform-origin: 167.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThe McCarthy 91 function is defined for integer \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e as:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 366.5px; height: 18.5px;\" width=\"366.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 100px 8px; transform-origin: 100px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRemarkably, the function yields \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 70.5px; height: 18.5px;\" width=\"70.5\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.5px 8px; transform-origin: 21.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for all \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 51.5px; height: 18px;\" width=\"51.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 89px 8px; transform-origin: 89px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (hence, the function name).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 280px 8px; transform-origin: 280px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eA more generalized form of the McCarthy's recursive function is defined below: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 291px; height: 19.5px;\" width=\"291\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.5px 8px; transform-origin: 66.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor positive integers \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ea\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eb\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50.5px 8px; transform-origin: 50.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe expression \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 61px; height: 19.5px;\" width=\"61\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 94.5px 8px; transform-origin: 94.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, means applying the function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eG\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 54.5px 8px; transform-origin: 54.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e number of times:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 52px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 26px; text-align: left; transform-origin: 384px 26px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80px 8px; transform-origin: 80px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-20px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 165px; height: 52px;\" width=\"165\" height=\"52\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 162px 8px; transform-origin: 162px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis means that the 'original' McCarthy-91 function \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 37px; height: 18.5px;\" width=\"37\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35px 8px; transform-origin: 35px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is actually \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 33px; height: 18.5px;\" width=\"33\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 18px 8px; transform-origin: 18px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 51.5px; height: 18px;\" width=\"51.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 44px; height: 18px;\" width=\"44\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFcAAAAkCAYAAAD8fqYDAAACGklEQVRoQ+2Yu0oEQRBFZz9A8BWaGQmCgoiJpipGCoqGgqDoBxgJBmpgaiB+gKBGpj7AREMTYx+IuQ/8Ab0F3TA0M7NdPVPbu0wNXNx1u6q7ztb03p5GopcYgYZYZk2cKFzBJlC4CleQgGBq7VyFK0JgBFmfAjN7xdaxc1cAdB96hOg152LF1gnuHCgeQYOG5gUDblBsXeAeAOSHgXrChBscWxe46Vv/jwk3OFbhcnbcJGF9MQq3jeD2YS1DZj0PvHW1zWhW9zmrZsX6dC4BXYPWoRvoHVqCeqBVyBcyecOuChD7zpc3FQuQJFyyIKfQFzQNvZnJjvF3EyKvOAt9ekC7wpgZj3FFQ77xYW/JHG0BlwzzGfQKTTgAX/Ce/CKnWLI0YyXB/CCea/zdKaPDncSK7s2qpjJuferoYegaCj1CluQcHB4dru1MzikmuNoWB0aFa7cDqjmra1vMovLposI9RznLpqR+Z68tU6m6BdAjZ0A2i5zAeBmaTqy6BQCxt03VcNUtAK79MSvysBsYdwtZ31thg4unirrn2gMCVTnqWC06re2Z8rfEMchMEBUuAXxO7bu7eP0LDUD0BH8Hoh+9Try2sehDs3A6HKVPnc3qYcfmPVsgwIvQQmrGyw7eCshezkPdGQTv8L+iw1BwrM+Dm2bfqH6eQ0DhCraGwlW4ggQEU2vnKlxBAoKptXMVriABwdTauYJw/wHrU5AlYbeBLQAAAABJRU5ErkJggg==\" style=\"width: 43.5px; height: 18px;\" width=\"43.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16px 8px; transform-origin: 16px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 36px; height: 18px;\" width=\"36\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 56.5px 8px; transform-origin: 56.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eGiven integers, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ea\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eb\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ec\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ek\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19px 8px; transform-origin: 19px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.5px 8px; transform-origin: 80.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e, evaluate the value of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 33px; height: 18.5px;\" width=\"33\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 50px 8px; transform-origin: 50px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e--------------------\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21px 8px; transform-origin: 21px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eNOTE:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 355px 8px; transform-origin: 355px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function m = M(a,b,c,k,n)\r\n m = G(n);\r\n function g = G(x)\r\n if x \u003e a\r\n g = x - b;\r\n else\r\n g = G(G(G(G(x+c)))); % k-times\r\n end\r\n end\r\nend","test_suite":"%%\r\na = 100; b = 10; c = 11; k = 2; n = [1:20 201:220];\r\nm_correct = [repelem(91,20) 191:210];\r\nassert(isequal(arrayfun(@(i) M(a,b,c,k,i),n),m_correct))\r\n%%\r\na = 333; b = 3; c = 33; k = 3; n = 33;\r\nm_correct = 354;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 4444; b = 444; c = 444; k = 4; n = 444444;\r\nm_correct = 444000;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 55555; b = 555; c = 5555; k = 5; n = 5;\r\nm_correct = 56145;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 7777777; b = 77777; c = 777777; k = 7; n = 777;\r\nm_correct = 7700875;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 999999999; b = 9999; c = 999999; k = 99; n = 999;\r\nm_correct = 999997623;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 9999999999; b = 9; c = 99999999; k = 99999; n = 9999;\r\nm_correct = 10009111707;\r\nassert(isequal(M(a,b,c,k,n),m_correct))\r\n%%\r\na = 7654321; b = 891011; c = 171615141312; k = 181920; n = 1:100;\r\ns_correct = 952242024250;\r\nassert(isequal(sum(arrayfun(@(i) M(a,b,c,k,i),n)),s_correct))\r\n%%\r\na = 10000000000; b = 1000000; c = 1000000000000; k = 1000000; n = 1:1000;\r\nm = arrayfun(@(i) M(a,b,c,k,i),n);\r\ns_correct = [9999000500 9999000500 9999000001 288];\r\nassert(isequal(floor([mean(m) median(m) mode(m) std(m)]),s_correct))\r\n%%\r\nfiletext = fileread('M.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'regexp') || contains(filetext, 'eval') || contains(filetext, 'assignin');\r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":255988,"edited_by":255988,"edited_at":"2023-02-19T04:00:26.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-01-17T23:39:34.000Z","updated_at":"2025-08-23T09:20:38.000Z","published_at":"2023-01-18T08:07:36.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/McCarthy_91_function\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMcCarthy 91 function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe McCarthy 91 function is defined for integer \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e as:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n) = n-10 \\\\ \\\\text{if} \\\\ n \u0026gt; 100 \\\\ \\\\text{and} \\\\ \\nM(M(n+11)) \\\\ \\\\text{if} \\\\ n \\\\le 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRemarkably, the function yields \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n) = 91\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for all \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en \\\\le 101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e (hence, the function name).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA more generalized form of the McCarthy's recursive function is defined below: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n) = n-b \\\\ \\\\text{if} \\\\ n \u0026gt; a \\\\ \\\\text{and} \\\\ \\nG^k(n+c) \\\\ \\\\text{if} \\\\ n \\\\le a\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor positive integers \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe expression \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG^k(n+c) \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, means applying the function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e number of times:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG^k(x) = \\\\underbrace{G(G(G(...G(x))))}\\\\\\\\_{\\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\ \\\\text{k times}}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis means that the 'original' McCarthy-91 function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eM(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is actually \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea=100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb=10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec=11\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek=2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGiven integers, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e, evaluate the value of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eG(n)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e--------------------\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNOTE:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The McCarthy-91 function was created to test recursive function implementations. In this problem, while iterative loops are allowed, use of recursion is encouraged.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2838,"title":"Optimum Egyptian Fractions","description":"Following problem was inspired by \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm this problem\u003e and \u003chttp://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542 that comment\u003e.\r\n\r\nGiven fraction numerator _A_ and denominator _B_, find denominators _C_ for \u003chttps://en.wikipedia.org/wiki/Egyptian_fraction Egyptian fraction\u003e. The goal of this problem is to minimize the sum of the list.\r\n\r\nExample:\r\n \r\n A = 16;\r\n B = 63;\r\n % 16/63 == 1/7 + 1/9\r\n C = [7, 9];\r\n\r\n_C_ may be _[4, 252]_ or _[5, 19, 749, 640395]_ or _[5, 27, 63, 945]_ or _[6, 12, 252]_ or _[7, 9]_ or almost any else of infinite more other options. The best one is _[7, 9]_ with sum 16.\r\n\r\n* You may assume _A\u003cB_,\r\n* Your score will be based on sum of answers,\r\n* No cheating, please,\r\n* While greedy algorithm usually solves this problem, score may not be satisfying,\r\n* Class of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\r\n* I'm open for proposals to improve test, i.e. verification of output which is far from perfect.","description_html":"\u003cp\u003eFollowing problem was inspired by \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm\"\u003ethis problem\u003c/a\u003e and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542\"\u003ethat comment\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eGiven fraction numerator \u003ci\u003eA\u003c/i\u003e and denominator \u003ci\u003eB\u003c/i\u003e, find denominators \u003ci\u003eC\u003c/i\u003e for \u003ca href = \"https://en.wikipedia.org/wiki/Egyptian_fraction\"\u003eEgyptian fraction\u003c/a\u003e. The goal of this problem is to minimize the sum of the list.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e A = 16;\r\n B = 63;\r\n % 16/63 == 1/7 + 1/9\r\n C = [7, 9];\u003c/pre\u003e\u003cp\u003e\u003ci\u003eC\u003c/i\u003e may be \u003ci\u003e[4, 252]\u003c/i\u003e or \u003ci\u003e[5, 19, 749, 640395]\u003c/i\u003e or \u003ci\u003e[5, 27, 63, 945]\u003c/i\u003e or \u003ci\u003e[6, 12, 252]\u003c/i\u003e or \u003ci\u003e[7, 9]\u003c/i\u003e or almost any else of infinite more other options. The best one is \u003ci\u003e[7, 9]\u003c/i\u003e with sum 16.\u003c/p\u003e\u003cul\u003e\u003cli\u003eYou may assume \u003ci\u003eA\u0026lt;B\u003c/i\u003e,\u003c/li\u003e\u003cli\u003eYour score will be based on sum of answers,\u003c/li\u003e\u003cli\u003eNo cheating, please,\u003c/li\u003e\u003cli\u003eWhile greedy algorithm usually solves this problem, score may not be satisfying,\u003c/li\u003e\u003cli\u003eClass of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\u003c/li\u003e\u003cli\u003eI'm open for proposals to improve test, i.e. verification of output which is far from perfect.\u003c/li\u003e\u003c/ul\u003e","function_template":"function C = egyptian(A,B)\r\n A = uint64(A);\r\n B = uint64(B);\r\n C = idivide(A,B,'ceil'); % not likely\r\nend","test_suite":"%%\r\nA = 1;\r\nB = 4;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),4);\r\n%%\r\nA = 2;\r\nB = 6;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),3);\r\n%%\r\nA = 3;\r\nB = 7;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),59);\r\n%%\r\nA = 11;\r\nB = 30;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),11);\r\n%%\r\n% random\r\nfor k = 3:7;\r\n C_min = unique(randi([2 30],1,k));\r\n A = 0; B = 1;\r\n for l = C_min\r\n A = round((A*l + B)/gcd(l,B));\r\n B = lcm(B,l);\r\n end\r\n C = egyptian(A,B);\r\n fra = sum(1./double(C));\r\n fra_correct = A/B;\r\n assert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\n fprintf('Choosen A: %d, B: %d\\nbased on random C: [%s\\b]\\n Sum of C: %d, best is %d or less\\n',A,B,sprintf(' %d,',C_min),sum(C),sum(C_min));\r\nend\r\n%%\r\nA = 2;\r\nB = 101;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),1212);\r\n%%\r\nA = 11;\r\nB = 28;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),11);\r\n%%\r\nA = 17;\r\nB = 24;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),15);\r\n%%\r\nA = 25;\r\nB = 36;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),16);\r\n%%\r\nA = 1805;\r\nB = 1806;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),55);\r\n%%\r\n\r\nA = 287;\r\nB = 396;\r\nC = egyptian(A,B);\r\nfra = sum(1./double(C));\r\nfra_correct = A/B;\r\nassert(~any(mod(C,1)) \u0026\u0026 all(C\u003e1) \u0026\u0026 isequal(sort(C),unique(C)) \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\nfprintf('Sum of C: %d, best %d',sum(C),49);\r\n%%\r\n% Scoring.\r\n% by courtesy of LY Cao\r\nfid = fopen('score.p','wb');\r\nfwrite(fid,sscanf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x'));\r\nfclose(fid);\r\n% Those lists may be extended and scoring mechanism may be changed a bit\r\nlistA = [2 2 2 2 3 3 3 3 4 5 5 13 31 1805];\r\nlistB = [5 7 21 25 5 7 8 71 71 121 17 42 311 1806];\r\nS = 0;\r\ntry\r\n for k = 1:numel(listA),\r\n A = listA(k);\r\n B = listB(k);\r\n C = egyptian(A,B);\r\n fra = sum(1./double(C));\r\n fra_correct = A/B;\r\n assert(~any(mod(C,1)) ...\r\n \u0026\u0026 all(C\u003e1) ...\r\n \u0026\u0026 isequal(sort(C),unique(C)) ...\r\n \u0026\u0026 abs(fra-fra_correct)\u003c10*eps(fra));\r\n S = S + sum(C);\r\n end\r\n score(round(20*log10(double(S))));\r\ncatch\r\n score(1e4);\r\n error+1;\r\nend\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":12,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2016-04-12T22:03:10.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-18T23:21:47.000Z","updated_at":"2025-11-29T19:57:57.000Z","published_at":"2016-04-11T13:38:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing problem was inspired by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2126-split-bread-like-the-pharaohs-egyptian-fractions-and-greedy-algorithm\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethis problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/solutions/868356#comment_6542\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethat comment\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven fraction numerator\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and denominator\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eB\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, find denominators\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Egyptian_fraction\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eEgyptian fraction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The goal of this problem is to minimize the sum of the list.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ A = 16;\\n B = 63;\\n % 16/63 == 1/7 + 1/9\\n C = [7, 9];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eC\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e may be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[4, 252]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[5, 19, 749, 640395]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[5, 27, 63, 945]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[6, 12, 252]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[7, 9]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e or almost any else of infinite more other options. The best one is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[7, 9]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with sum 16.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou may assume\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eA\u0026lt;B\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour score will be based on sum of answers,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNo cheating, please,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhile greedy algorithm usually solves this problem, score may not be satisfying,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eClass of inputs is double, but keep in mind it may change in the future - most likely to uint64. Preferred output class is uint64.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI'm open for proposals to improve test, i.e. verification of output which is far from perfect.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":579,"title":"Spiral In","description":"Create an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\r\nFor example:\r\n\u003e\u003e spiralIn(4,5)\r\nans =\r\n 1 14 13 12 11\r\n 2 15 20 19 10\r\n 3 16 17 18 9\r\n 4 5 6 7 8","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"baseline-shift: 0px; block-size: 190px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 95px; transform-origin: 468.5px 95px; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 434.5px 8px; transform-origin: 434.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCreate an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 40.8417px 8px; transform-origin: 40.8417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 108px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.5px 54px; transform-origin: 464.5px 54px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 61.6px 8.5px; tab-size: 4; transform-origin: 61.6px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u0026gt;\u0026gt; spiralIn(4,5)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 19.25px 8.5px; tab-size: 4; transform-origin: 19.25px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 14 13 12 11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 2 15 20 19 10\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3 16 17 18 9\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.5px 9px; text-wrap-mode: nowrap; transform-origin: 464.5px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 8.5px; tab-size: 4; transform-origin: 107.8px 8.5px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4 5 6 7 8\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = spiralIn(m,n)\r\n s = zeros(m,n);\r\nend","test_suite":"%%\r\nm = 3;\r\nn = 5;\r\ns_correct = [1 12 11 10 9; 2 13 14 15 8; 3 4 5 6 7];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 5;\r\nn = 3;\r\ns_correct = [1 12 11; 2 13 10; 3 14 9; 4 15 8; 5 6 7];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n\r\n%%\r\nm = 1;\r\nn = 1;\r\ns_correct = 1;\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 5;\r\nn = 0;\r\ns_correct = zeros(5,0);\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n%%\r\nm = 2;\r\nn = 2;\r\ns_correct = [1 4; 2 3];\r\nassert(isequal(spiralIn(m,n),s_correct))\r\n\r\n\r\n%%\r\n%Test case added on 4/4/26\r\nm = 2*randi(10)+1;\r\ns_correct = m^2+1-rot90(spiral(m));\r\nassert(isequal(spiralIn(m,m),s_correct))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":4,"created_by":3117,"edited_by":223089,"edited_at":"2026-04-04T09:55:47.000Z","deleted_by":null,"deleted_at":null,"solvers_count":120,"test_suite_updated_at":"2026-04-04T09:55:47.000Z","rescore_all_solutions":false,"group_id":18,"created_at":"2012-04-13T13:50:35.000Z","updated_at":"2026-04-04T09:56:24.000Z","published_at":"2012-04-13T13:50:35.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an m by n matrix filled with sequential integers starting from 1 and arranged in a counterclockwise spiral that hugs the outside border and begins in the upper left corner.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e spiralIn(4,5)\\nans =\\n 1 14 13 12 11\\n 2 15 20 19 10\\n 3 16 17 18 9\\n 4 5 6 7 8]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n %show_pfig(s,p,c,emap,pmap,1)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2020-11-12T23:27:09.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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Solver - Standard 9x9","description":"Solve a Standard 9x9 \u003chttp://en.wikipedia.org/wiki/Sudoku Sudoku\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner. \u003chttp://www.free-sudoku.com/sudoku.php?dchoix=evil Sudoku practice site\u003e.\r\n\r\n*Input:* m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\r\n\r\n*Output:* mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\r\n\r\n*Scoring:* Time (msec) to solve the Hard Sudoku\r\n\r\n*Example:*\r\n\r\n m mout\r\n 390701506 398721546\r\n 042890701 542896731\r\n 106540890 176543892\r\n 820600150 829674153\r\n 400138009 457138269\r\n 031002087 631952487\r\n 065087304 965287314\r\n 703065920 713465928\r\n 204309075 284319675\r\n\r\n*Sudoku Variations:*\r\n\r\nFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\r\n\r\n*Algorithm Spoiler:*\r\nSudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.","description_html":"\u003cp\u003eSolve a Standard 9x9 \u003ca href = \"http://en.wikipedia.org/wiki/Sudoku\"\u003eSudoku\u003c/a\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner. \u003ca href = \"http://www.free-sudoku.com/sudoku.php?dchoix=evil\"\u003eSudoku practice site\u003c/a\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Time (msec) to solve the Hard Sudoku\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003em mout\r\n390701506 398721546\r\n042890701 542896731\r\n106540890 176543892\r\n820600150 829674153\r\n400138009 457138269\r\n031002087 631952487\r\n065087304 965287314\r\n703065920 713465928\r\n204309075 284319675\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eSudoku Variations:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithm Spoiler:\u003c/b\u003e\r\nSudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.\u003c/p\u003e","function_template":"function mout=sudoku_solver(m)\r\n% m is a 9x9 Sudoku array with 0 for unknown values\r\n% create mout a consistent sudoku array\r\n mout=m;\r\nend","test_suite":"assignin('caller','score',500);\r\n%%\r\n% Test 1\r\nmstr=['012300007'; '040600010'; '078900020'; '000000040'; '100000002'; '060000000'; '080001230'; '090004060'; '300007890']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n%%\r\n% Test 2\r\nmstr=['000004500'; '000003600'; '432008700'; '867000000'; '000000000'; '000000417'; '001900854'; '006400000'; '003700000']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n%%\r\n% Test 3\r\nmstr=['120034000'; '000000056'; '000200000'; '007800002'; '600000001'; '500006300'; '000008000'; '340000000'; '000560078']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntic\r\nmout=sudoku_solver(m)\r\ntoc\r\n\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nassert(valid==1)\r\n\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\nassert(valid2==1)\r\n\r\n\r\n%%\r\n% Timed Test on a Hard Sudoku\r\n% Non-Valid answer creates a Max score but not a fail\r\n% Hard Sudoku\r\nmstr=['005700009'; '030090010'; '100005300'; '600004700'; '040010050'; '002500001'; '004600002'; '080020040'; '200008600']; \r\n% convert string to array\r\nm=zeros(9);\r\nfor i=1:9\r\n m(i,:)=mstr(i,:)-'0' ; \r\nend\r\n\r\ntime0=cputime;\r\n mout=sudoku_solver(m)\r\netime=(cputime-time0)*1000 % msec\r\nvalid=all(arrayfun(@(i) all(sum([mout mout']==i)),1:length(mout)));\r\nptr=find(m\u003e0);\r\nvalid2=isequal(m(ptr),mout(ptr));\r\n% Not Asserting for Valid answer\r\nif ~valid,etime=500;end\r\nif ~valid2,etime=500;end\r\nassignin('caller','score',min(500,floor(etime)));","published":true,"deleted":false,"likes_count":8,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":51,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-01-02T00:31:17.000Z","updated_at":"2025-12-15T20:03:47.000Z","published_at":"2014-01-02T01:30:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolve a Standard 9x9\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sudoku\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSudoku\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Values 1 thru 9 occur in each row, column, and the nine non-overlapping 3x3 matrices starting at the top left corner.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.free-sudoku.com/sudoku.php?dchoix=evil\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSudoku practice site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m, a 9x9 matrix of values 0 thru 9. Unknowns are 0s.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e mout, a 9x9 matrix of values 1 thru 9 that satisfy Sudoku rules.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time (msec) to solve the Hard Sudoku\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[m mout\\n390701506 398721546\\n042890701 542896731\\n106540890 176543892\\n820600150 829674153\\n400138009 457138269\\n031002087 631952487\\n065087304 965287314\\n703065920 713465928\\n204309075 284319675]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSudoku Variations:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture challenges will involve the Sudoku variations Diagonal, Arrow(Sums), Inequality, Irregular, and others as they present themselves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Spoiler:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sudoku's can be readily solved using minimal choice recursion with consistency check. A key step is an index map of all indices that have mutual value exclusion (row,col,3x3), idxmap[81,27]. Another key step is to have an Evolve function that implements all single option values determined by the idxmap. A critical performance enhancement is a Sudoku Consistency Checker that checks for illegal replications of values. Illegal placements by Evolve occur when an incorrect value is asserted into the matrix during recursion trials. The recursive solver finds an idx with minimum options based on idxmap. The values for the idx location are asserted, Evolved, Consistency Checked, and then recursion call if Consistent. When all is Consistent and no unknowns remain the Sudoku is solved. Solution times are in the milli-seconds even for Evil, minimum 17 value, Sudokus.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60840,"title":"4D Hypercube Validation with Prime-Indexed Symmetry and Modular Trace Constraints","description":"Design a function that generates and validates a 4D hypercube matrix where: \r\n1. Prime-Indexed Symmetry: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \r\n2. Recurrence Relation: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \r\n3. Trace Validation: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 132px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 66px; transform-origin: 408px 66px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDesign a function that generates and validates a 4D hypercube matrix where: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ePrime-Indexed Symmetry\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRecurrence Relation\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eTrace Validation\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [isValid, hypercube] = hyperValidator(dims, prime_threshold)\r\n % Inputs:\r\n % dims: [n1,n2,n3,n4] hypercube dimensions\r\n % prime_threshold: max prime for trace checks (p ≤ min(dims))\r\n % Outputs:\r\n % isValid: true/false if constraints are satisfied\r\n % hypercube: 4D matrix (NaN if invalid)\r\n \r\n isValid = false;\r\n hypercube = nan(dims);\r\nend","test_suite":"%% Test 1 - Base Case \r\ndims = [2,2,2,2]; \r\nprime_threshold = 2; \r\n[isValid, A] = hyperValidator(dims, prime_threshold); \r\nassert(isValid \u0026\u0026 all(size(A) == [2,2,2,2])); \r\n\r\n%% Test 2 - Prime Trace Check \r\ndims = [5,5,5,5]; \r\nprime_threshold = 3; \r\n[~, A] = hyperValidator(dims, prime_threshold); \r\nassert(mod(trace(A(:,:,3,3)), 6) == 0); % 3! = 6 \r\n\r\n%% Test 3 - Invalid Input Handling \r\ndims = [0,5,5,5]; \r\n[isValid, ~] = hyperValidator(dims, 5); \r\nassert(~isValid); ","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4805930,"edited_by":4805930,"edited_at":"2025-04-07T18:30:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-04-07T18:27:38.000Z","updated_at":"2025-04-13T09:48:40.000Z","published_at":"2025-04-07T18:27:38.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003eDesign a function that generates and validates a 4D hypercube matrix where: \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e1. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePrime-Indexed Symmetry\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: Every 2D slice `A(i,j,:,:)` must have palindromic rows if `i` or `j` is a prime number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRecurrence Relation\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: Elements follow `A(i,j,k,l) = A(i-1,j,k,l) + A(i,j-1,k,l) + A(i,j,k-1,l) + A(i,j,k,l-1)` with seed values from twin primes (e.g., `A(0,0,0,0) = 3`). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e3. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTrace Validation\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr/\u003e\u003cw:t\u003e: For primes `p ≤ min(dimensions)`, `trace(A(:,:,p,p))` must be divisible by `p!`. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61089,"title":"Find Solutions to Edge-Matching Puzzles","description":"I was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\" (Wikipedia). For more information, see the full article: https://en.wikipedia.org/wiki/Edge-matching_puzzle. In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\r\n\r\nSource (spoilers!): https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch \r\nIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\r\nYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\r\ndeck = [2, 1,-3, -4\r\n -2, 1, 4, -3\r\n -3, 2, 4, -1\r\n -1, 2, 3, -4\r\n -4, 1, 3, -2\r\n -4, 1, 3, -1\r\n 2, 4,-1, -3\r\n -3, 2, 4, -2\r\n -3, 1, 4, -2];\r\nYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \r\nsolution = [2, 0\r\n 3, 0\r\n 8, 0\r\n 1, 1\r\n 5, 0\r\n 6, 0\r\n 4, 0\r\n 9, 0\r\n 7, 1];\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1217.61px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.494px 608.807px; transform-origin: 468.494px 608.807px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105.043px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 52.5142px; text-align: left; transform-origin: 444.51px 52.5213px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\" (Wikipedia). For more information, see the full article: \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Edge-matching_puzzle.\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003ehttps://en.wikipedia.org/wiki/Edge-matching_puzzle.\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 439.545px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 219.773px; text-align: left; transform-origin: 444.51px 219.773px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cimg class=\"imageNode\" width=\"433\" height=\"434\" style=\"vertical-align: baseline;width: 433px;height: 434px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 10.4972px; text-align: left; transform-origin: 444.51px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSource (spoilers!): \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003ehttps://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84.0341px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 42.017px; text-align: left; transform-origin: 444.51px 42.017px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105.043px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 52.5142px; text-align: left; transform-origin: 444.51px 52.5213px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 161.974px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.503px 80.9801px; transform-origin: 464.503px 80.9872px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003edeck = [2, 1,-3, -4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -2, 1, 4, -3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 2, 4, -1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -1, 2, 3, -4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -4, 1, 3, -2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -4, 1, 3, -1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 2, 4,-1, -3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 2, 4, -2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e -3, 1, 4, -2];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 21.0085px; text-align: left; transform-origin: 444.51px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 161.974px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 464.503px 80.9801px; transform-origin: 464.503px 80.9872px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003esolution = [2, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 8, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1, 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 5, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 6, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 4, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9, 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 17.9972px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 464.503px 8.99148px; text-wrap-mode: nowrap; transform-origin: 464.503px 8.99858px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 7, 1];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.503px 10.4972px; text-align: left; transform-origin: 444.51px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function solution = EdgeMatchPuzzleSolver(deck)\r\n\r\n solution = zeros(9,2);\r\n\r\nend","test_suite":"%% Test Case 1\r\n%==========================================================================\r\n\r\ndeck = [ 2, 1,-3, -4\r\n -2, 1, 4, -3\r\n -3, 2, 4, -1\r\n -1, 2, 3, -4\r\n -4, 1, 3, -2\r\n -4, 1, 3, -1\r\n 2, 4,-1, -3\r\n -3, 2, 4, -2\r\n -3, 1, 4, -2];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n\r\n\r\n%% Test Case 2\r\n%==========================================================================\r\n\r\ndeck = [-1,-4, 2, 3;\r\n -4,-2, 3, 4;\r\n -1,-4,-3,-2;\r\n -3,-1, 4, 1;\r\n -3,-2, 4, 1;\r\n -3, 2, 1, 3;\r\n 3,-2, 4, 1;\r\n 4,-2,-3, 1;\r\n 4,-1, 3, 2];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n\r\n\r\n%% Test Case 3\r\n%==========================================================================\r\n\r\ndeck = [ 1,-2,-3, 4\r\n -1, 2, 1, 4\r\n 4,-3,-2,-1\r\n -4, 1, 2,-3\r\n -3, 4,-2,-4\r\n 2, 1,-4, 3\r\n -2,-4, 3,-1\r\n -2,-3, 4,-1\r\n 3,-2, 1,-3];\r\n\r\nsolution = EdgeMatchPuzzleSolver(deck)\r\n\r\nfor i = 1:9\r\n board(i,:) = circshift(deck(solution(i,1),:),solution(i,2));\r\nend\r\n\r\nedge_checks = [board(1,2)+board(2,4); ...\r\n board(2,2)+board(3,4); ...\r\n board(1,3)+board(4,1); ...\r\n board(2,3)+board(5,1); ...\r\n board(3,3)+board(6,1); ...\r\n board(4,2)+board(5,4); ...\r\n board(5,2)+board(6,4); ...\r\n board(4,3)+board(7,1); ...\r\n board(5,3)+board(8,1); ...\r\n board(6,3)+board(9,1); ...\r\n board(7,2)+board(8,4); ...\r\n board(8,2)+board(9,4)];\r\n\r\nassert(all(edge_checks == 0))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":4910069,"edited_by":4910069,"edited_at":"2026-03-06T17:13:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-11-30T05:11:39.000Z","updated_at":"2026-03-06T17:13:00.000Z","published_at":"2025-11-30T06:33:57.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI was intrigued by some edge-matching puzzles I came across when visiting my parents over Thanksgiving. \\\"An edge-matching puzzle is a type of tiling puzzle involving tiling an area with (typically regular) polygons whose edges are distinguished with colours or patterns, in such a way that the edges of adjacent tiles match\\\" (Wikipedia). For more information, see the full article: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Edge-matching_puzzle.\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttps://en.wikipedia.org/wiki/Edge-matching_puzzle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e In our case, we will be dealing with a 3x3 square grid, although your solutions will be easily adaptable to larger grids and different polygons. Let's take a look at a typical example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"434\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"433\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSource (spoilers!): \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttps://web.stanford.edu/class/archive/cs/cs106b/cs106b.1216/assignments/4-backtracking/tilematch\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example there are eight options for each tile edge: one of four colors paired with either the top or bottom half of a bottle. The goal is to arrange all 9 tiles so that the edges match colors and make a complete bottle. You can see that this puzzle is unsolved as the top left and bottom left tile edges do not match. These puzzles are surprisingly difficult to solve by hand as I quickly realized. There are a total of 9! * 4^9, or over 95 billion, possible ways to arrange these 9 tiles in a 3x3 grid. I gave up and decided to let the computer do the thinking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task is to write a function that will take a deck of 9 cards and find a valid solution. The deck will be given to you as a matrix where each row represents a tile, and each column represents the edges in clockwise order. For the example, I decided to assign each edge a number based on the color: red = 1, green = 2, blue = 3, cream = 4. The number is positive if it is the top half of the bottle and negative if it is the bottom half. (The numbers can represent anything, in my case they were 4 different cats). The tiles from left to right and top to bottom would have an input that looks like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[deck = [2, 1,-3, -4\\n -2, 1, 4, -3\\n -3, 2, 4, -1\\n -1, 2, 3, -4\\n -4, 1, 3, -2\\n -4, 1, 3, -1\\n 2, 4,-1, -3\\n -3, 2, 4, -2\\n -3, 1, 4, -2];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour solution should output a matrix specifying a valid tile order going from left to right and top to bottom (first column) and how many times to rotate the tile clockwise (second column). \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1301,"title":"RISK Calculator - Large Armies, High Accuracy, Fast","description":"This Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u003e= 2 and Defense \u003e=1 ].\r\n\r\nRelated to \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map Cody 1260 RISK Board Game Battle Simulation\u003e\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Risk_%28game%29#Official Link to official Risk Rules\u003e\r\n\r\n*Simplified explanation of the dice play:*\r\n \r\n Attacker with 2 armies will throw one die.\r\n Attacker with 3 armies will throw two die.\r\n Attacker with 4 or more armies will throw three die.\r\n \r\n Defense with 1 army will use one die.\r\n Defense with 2 or more armies will throw 2 die.\r\n \r\n The attacker High is compared to the Defender High.\r\n If Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\r\n If the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \r\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\r\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose). \r\n\r\n*Input:* a,d where a is number of attacking armies and d is number defending\r\n\r\n*Output:* pwin, the probability of the Attacker Winning\r\n\r\n*Accuracy:* Accurate to +/- 1e-6\r\n\r\n*Scoring:* Time (msec) to solve 10 Battle Scenarios \r\n\r\n\r\n\u003chttp://recreationalmath.com/Risk/ Risk Calculator\u003e","description_html":"\u003cp\u003eThis Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u003e= 2 and Defense \u003e=1 ].\u003c/p\u003e\u003cp\u003eRelated to \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map\"\u003eCody 1260 RISK Board Game Battle Simulation\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Risk_%28game%29#Official\"\u003eLink to official Risk Rules\u003c/a\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSimplified explanation of the dice play:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAttacker with 2 armies will throw one die.\r\nAttacker with 3 armies will throw two die.\r\nAttacker with 4 or more armies will throw three die.\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eDefense with 1 army will use one die.\r\nDefense with 2 or more armies will throw 2 die.\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eThe attacker High is compared to the Defender High.\r\nIf Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\r\nIf the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \r\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\r\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose). \r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e a,d where a is number of attacking armies and d is number defending\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e pwin, the probability of the Attacker Winning\u003c/p\u003e\u003cp\u003e\u003cb\u003eAccuracy:\u003c/b\u003e Accurate to +/- 1e-6\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Time (msec) to solve 10 Battle Scenarios\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://recreationalmath.com/Risk/\"\u003eRisk Calculator\u003c/a\u003e\u003c/p\u003e","function_template":"function pwin = risk_prob(a, d)\r\n pwin=0;\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',5000); % msec\r\n%%\r\na=[100 99 100 10 9 2 2 10 30 70];\r\nd=[100 100 99 9 10 1 2 2 30 80];\r\ny_c=[0.8079031789315619 0.7888693135658454 0.8230449788340404 0.5580697529719042 0.3798720048109818 0.4166666666666667 0.10609567901234569 0.9901146432872121 0.633266311153744 0.5011352886279803];\r\n\r\ntsum=0;\r\nfor i=1:length(a)\r\n ta=clock;\r\n y=risk_prob(a(i), d(i));\r\n t1=etime(clock,ta)*1000; % time in msec\r\n tsum=tsum+t1;\r\n assert(abs(y - y_c(i)) \u003c= 1e-6,sprintf('A=%i D=%i Expect=%.9f pwin=%.9f',a(i),d(i),y_c(i),y))\r\n fprintf('A %3i D %3i Time(msec) %7.3f\\n',a(i),d(i),t1);\r\nend\r\n\r\nfeval( @assignin,'caller','score',floor(min( 5000,tsum )) );","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-02-25T00:07:52.000Z","updated_at":"2026-02-15T07:40:59.000Z","published_at":"2013-02-25T04:24:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to quickly provide the high precision probability of legal RISK battles up to 100 vs 100. [ Attack \u0026gt;= 2 and Defense \u0026gt;=1 ].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRelated to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1260-risk-board-game-battle-simulation/solutions/map\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody 1260 RISK Board Game Battle Simulation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Risk_%28game%29#Official\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLink to official Risk Rules\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eSimplified explanation of the dice play:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Attacker with 2 armies will throw one die.\\nAttacker with 3 armies will throw two die.\\nAttacker with 4 or more armies will throw three die.\\n\\nDefense with 1 army will use one die.\\nDefense with 2 or more armies will throw 2 die.\\n\\nThe attacker High is compared to the Defender High.\\nIf Attacker High \u003e Defender High then defender loses 1 army otherwise Attacker loses 1 army. Tie goes to defender.\\nIf the Defender threw two die and the Attacker threw 2 or more die then the Second Highest of each is compared. \\nIf Attack \u003e Defense then Defense loses an army otherwise Attack loses an army.\\nAttack continues until No defenders remain (Win) or Attack is reduced to 1 army (Lose).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e a,d where a is number of attacking armies and d is number defending\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e pwin, the probability of the Attacker Winning\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAccuracy:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Accurate to +/- 1e-6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Time (msec) to solve 10 Battle Scenarios\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://recreationalmath.com/Risk/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRisk Calculator\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47478,"title":"Slitherlink V: Assert/Evolve/Check (large)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 678.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 339.333px; transform-origin: 407px 339.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 210px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 105px; text-align: left; transform-origin: 384px 105px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 168.4px 7.91667px; transform-origin: 168.4px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink V: Assert/Evolve/Check(large size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 207.3px 7.91667px; transform-origin: 207.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method. The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board. The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 314.917px 7.91667px; transform-origin: 314.917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n \r\n %Implement Assert/Check/Evolve\r\n [p]=assert(p,bsegs,s,c,emap,pmap); \r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n if valid\r\n fprintf('sv Assert solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n end\r\n \r\n % Start recursive processing\r\n if ~valid\r\n [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n fprintf('sv recursion solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\nfunction [p]=assert(p,bsegs,s,c,emap,pmap)\r\n %Insert code here to assert a segment as Red/Black\r\n %Check if evolve of is valid\r\n %If not valid then Assert segment as Black/Red depending on case and then evolve\r\n %Keep asserting until no more p updates and/or s is solved\r\n %Asserting ends of red segments first may reduce total time\r\n pb=p*0;\r\n valid=0;\r\n while ~isequal(p,pb) \u0026\u0026 ~valid\r\n pb=p;\r\n [pr,pc]=find(p==1);\r\n % insert code here\r\n end % while\r\nend\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3) \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n pn=p;\r\n pn(ptr,i)=5;pn(i,ptr)=5; % make linkage\r\n \r\n %This modified pn may be invalid and create an invalid evolve result\r\n [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n if ~evalid,continue;end\r\n \r\n [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n if valid\r\n solved=1;\r\n p=pn;\r\n return;\r\n end\r\n \r\n %Invoke the next level of recursion build with the recursion assert and Evolve\r\n [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n if solved\r\n p=pn;\r\n return\r\n end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n % if invalid try next option\r\n % call next level recur\r\n % if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n end %i s3 3\r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n p(1,2)=5; p(2,1)=5;\r\n p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n end\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n end\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n end\r\n end % j L col\r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vud\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hLR\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n if p(cv(2),cv(2)+1)==5 % rr;xr\r\n if i\u003e1\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)+1)==5 % rr;rx\r\n if i\u003e1\r\n p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n if p(cv(2),cv(2)-1)==5 % xr;rr\r\n if i\u003cnrc\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)-1)==5 % rx;rr\r\n if i\u003cnrc\r\n p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n \r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5)\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %need an isequal(p,pb)\r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars\r\n ps=sum(p);\r\n pv= ps\u003e4 \u0026 ~(ps==10);\r\n pidx=find(pv);\r\n for i=pidx\r\n v=[i find(p(i,:)==5)];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n \r\n end\r\n if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n if Lv\u003cnnz(p==5)/2\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n else % Possible solve\r\n pchk=p;\r\n pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n if valid\r\n p=pchk;\r\n return\r\n else % invalid loop connect thus must be 0\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n end\r\n end % Lv\r\n end % p( v 1 end)\r\n end % pidx\r\n end % isequal p pb after cells, ends make no change\r\n \r\n %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n \r\n% isequal(p,pb)\r\n% show_pfig(s,p,c,emap,pmap,3)\r\n% show_pfig(s,p,c,emap,pmap,2)\r\n \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); % .48 17K\r\n %if nnz(sum(p)==5) % Node with no escape %.48\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created **********************************\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n %pidx=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; \r\n %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n 5 5 5 2 5 5 5 5 5 5;\r\n 1 5 5 5 5 1 1 5 5 2;\r\n 0 5 5 5 5 2 5 5 3 3;\r\n 0 5 5 5 1 3 5 5 5 5;\r\n 5 5 5 5 2 3 5 5 5 0;\r\n 3 2 5 5 1 5 5 5 5 2;\r\n 3 5 5 2 0 5 5 5 5 2;\r\n 5 5 5 5 5 5 2 5 5 5;\r\n 3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\ns=['053552235013';\r\n '505555535555';\r\n '355135525552';\r\n '521552155535';\r\n '555305555553';\r\n '535555335551';\r\n '525050255352';\r\n '325255555505';\r\n '525555552521';\r\n '152552253525';\r\n '255533555535';\r\n '255555522555';\r\n '535551355315';\r\n '355535512553';\r\n '555525555515';\r\n '132523255153']-'0'; % Solves with Assert\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=['225355223525';\r\n '555235535535';\r\n '555255555555';\r\n '232535355512';\r\n '355555535515';\r\n '255035555502';\r\n '555555522555';\r\n '055515555315';\r\n '513555535550';\r\n '555025555555';\r\n '015555522552';\r\n '505535555553';\r\n '315553525223';\r\n '555555553555';\r\n '525515531555';\r\n '535312551533']-'0'; % solves with Assert, Dies in Recursion\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n 5 0 5 5 5 3 3 5 5 5;\r\n 5 5 5 1 2 5 5 5 3 5;\r\n 2 5 5 5 5 5 2 0 5 2;\r\n 0 5 5 5 5 5 5 5 5 5;\r\n 5 5 5 5 5 5 5 5 5 3;\r\n 3 5 1 2 5 5 5 5 5 1;\r\n 5 3 5 5 5 3 0 5 5 5;\r\n 5 5 5 0 0 5 5 5 3 5;\r\n 2 1 5 5 5 1 5 5 3 5]; % solves with recursive/assert\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T21:28:57.000Z","updated_at":"2024-12-14T18:13:16.000Z","published_at":"2020-11-12T23:19:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink V: Assert/Evolve/Check(large size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving and Recursion due to time and depth issues. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking the Assert/Evolve/Check/Update method. The advanced solving techniques on the web are weak and complicated. The simple method is not to immediately invoke recursion due to the sparseness of data leading to too many false options. Ther actual simple method is to use Try/Catch by asserting segments as Black/Red and then checking if the layout using a robust Evolve creates an invalid state. If the state became invalid when asserting a single segment as Black then it must be Red with the same being true of Red assertion being invalid must mean the segment is Black. If an Evolve is invalid then Assert the right Bar type and perform an evolve to update the board. The two large test cases are from Games World of Puzzles October 2020. I was completely hopeless for the large puzzles. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive(medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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IV: Recursive (medium size)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.667px 7.91667px; transform-origin: 144.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink IV: Recursive (medium size)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 239.35px 7.91667px; transform-origin: 239.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion. When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 324.633px 7.91667px; transform-origin: 324.633px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv init solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n% show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv evolve solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n \r\n % Check if solved\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n \r\n % Start recursive processing\r\n if ~valid\r\n [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap);\r\n [sv,valid]=pcheck(s,p,bsegs);\r\n end\r\n%\r\n if valid\r\n fprintf('sv recursion solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n else\r\n fprintf('No solution found\\n')\r\n end\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n solved=0;\r\n \r\n %work thru options of first end found with minimum options (2 or 3) \r\n %(first 2 then 3 if any found)\r\n % extend a segment\r\n ps=sum(p);\r\n ptr=find(ps==7,1,'first'); % First Segment with 2 options\r\n if isempty(ptr)\r\n ptr=find(ps==8,1,'first'); % First Segment with 3 options\r\n end\r\n pc=find(p(ptr,:)==1);\r\n \r\n for i=pc\r\n pn=p;\r\n %insertion of code required here\r\n \r\n %This modified pn may be invalid and create an invalid evolve result\r\n [pn,evalid]=evolve(pn,bsegs,s,c,emap,pmap);\r\n if ~evalid,continue;end\r\n \r\n [v,valid]=pcheck(s,pn,bsegs); % check if segment add and evolve solved\r\n if valid\r\n solved=1;\r\n p=pn;\r\n return;\r\n end\r\n \r\n %Invoke the next level of recursion build with the recursion assert and Evolve\r\n [pn,solved]=slither_recur(pn,bsegs,s,c,emap,pmap);\r\n if solved\r\n p=pn;\r\n return\r\n end\r\n end %i\r\n % Loop through options\r\n % Perform evolve\r\n % if invalid try next option\r\n % call next level recur\r\n % if solved return\r\nend %[p,solved]=slither_recur(p,bsegs,s,c,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb)\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e10 % 0 non-5 segments, have 2 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==6 || sum(wv)==2 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e15 % 0 non-5 segments, have 3 links\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==11 || sum(wv)==3 || sum(wv)==7 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e10\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n end %i s3 3\r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=0; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=0;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=0;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=0;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=0; p(nrc*ncc-1,nrc*ncc)=0;\r\n p(nrc*ncc,nrc*ncc-nrc)=0;p(nrc*ncc-nrc,nrc*ncc)=0;\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n p(1,2)=5; p(2,1)=5;\r\n p(1,nrc+1)=5;p(nrc+1,1)=5;\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n p(nrc-1,nrc)=5; p(nrc,nrc-1)=5;\r\n p(nrc,2*nrc)=5;p(2*nrc,nrc)=5;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n p((ncc-2)*nrc+1,(ncc-1)*nrc+1)=5; p((ncc-1)*nrc+1,(ncc-2)*nrc+1)=5;\r\n p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)=5;p((ncc-1)*nrc+1+1,(ncc-1)*nrc+1)=5;\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n p(nrc*ncc,nrc*ncc-1)=5; p(nrc*ncc-1,nrc*ncc)=5;\r\n p(nrc*ncc,nrc*ncc-nrc)=5;p(nrc*ncc-nrc,nrc*ncc)=5;\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up, virtual cv(2)+1==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert vert up\r\n end\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor, virt cv(2)-nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert hor rt\r\n end\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor, virt cv(2)+nrc==0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hor\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert hor lt\r\n end\r\n end % j L col\r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+1)==0\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vud\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0; % Insert v up\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)+nrc)==0\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B hLR\r\n p(cv(2),cv(2)-nrc)=0;p(cv(2)-nrc,cv(2))=0; % Insert h L\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n if p(cv(2),cv(2)+1)==5 % rr;xr\r\n if i\u003e1\r\n p(cv(2),cv(2)-1)=0;p(cv(2)-1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)+1)==5 % rr;rx\r\n if i\u003e1\r\n p(cv(1),cv(1)-1)=0;p(cv(1)-1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n if p(cv(2),cv(2)-1)==5 % xr;rr\r\n if i\u003cnrc\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0;\r\n end\r\n if j\u003cncc-1\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0;\r\n end\r\n end\r\n \r\n if p(cv(1),cv(1)-1)==5 % rx;rr\r\n if i\u003cnrc\r\n p(cv(1),cv(1)+1)=0;p(cv(1)+1,cv(1))=0;\r\n end\r\n if j\u003e1\r\n p(cv(1),cv(1)-nrc)=0;p(cv(1)-nrc,cv(1))=0;\r\n end\r\n end\r\n \r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)+1)==0 % down dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+1)==0 % down dead end, rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n if p(cv(1),cv(1)-1)==0 % up dead end left side\r\n if j\u003e1\r\n p(cv(1)-nrc,cv(1))=0;p(cv(1),cv(1)-nrc)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-1)==0 % up dead end rt side\r\n if j\u003cncc-1\r\n p(cv(2)+nrc,cv(2))=0;p(cv(2),cv(2)+nrc)=0;\r\n end\r\n end\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)-nrc)==0 % rt dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)-nrc)==0 % rt dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n if p(cv(1),cv(1)+nrc)==0 % left dead end up side\r\n if i\u003e1\r\n p(cv(1)-1,cv(1))=0;p(cv(1),cv(1)-1)=0;\r\n end\r\n end\r\n if p(cv(2),cv(2)+nrc)==0 % left dead end down side\r\n if i\u003cnrc-1\r\n p(cv(2)+1,cv(2))=0;p(cv(2),cv(2)+1)=0;\r\n end\r\n end\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5)\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %need an isequal(p,pb)\r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars\r\n ps=sum(p);\r\n pv= ps\u003e4 \u0026 ~(ps==10);\r\n pidx=find(pv);\r\n for i=pidx\r\n v=[i find(p(i,:)==5)];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n \r\n end\r\n if Lv\u003c4,continue;end % Need at least 3 segments to make a loop\r\n if p(v(1),v(end)) % path ends are currently adjacent, likely sb 0 but may be final solve\r\n if Lv\u003cnnz(p==5)/2\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n else % Possible solve\r\n pchk=p;\r\n pchk(v(1),v(end))=5;pchk(v(end),v(1))=5;\r\n [sv,valid]=pcheck(s,pchk,bsegs); % check if solved\r\n if valid\r\n p=pchk;\r\n return\r\n else % invalid loop connect thus must be 0\r\n p(v(1),v(end))=0;p(v(end),v(1))=0;\r\n end\r\n end % Lv\r\n end % p( v 1 end)\r\n end % pidx\r\n end % isequal p pb after cells, ends make no change\r\n \r\n %possible evolve is try seg to see if evolve base leads to a fail thus must be black\r\n \r\n% isequal(p,pb)\r\n% show_pfig(s,p,c,emap,pmap,3)\r\n% show_pfig(s,p,c,emap,pmap,2)\r\n \r\n\r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n %if s(sptr)==5,continue;end % what if a 4 seg circle occurs around a 5?\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); % .48 17K\r\n %if nnz(sum(p)==5) % Node with no escape %.48\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n %if nnz(sum(p)\u003e14) % Node with too many segments % .47\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created **********************************\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n %pidx=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n% v=unique([v find(p(v(end),:)==5)],'stable'); %.118\r\n \r\n% v=[v find(p(v(end),:)==5)]; % fast add unique node to end\r\n% if nnz(v(1:end-2)==v(end))\r\n% v(end)=[];\r\n% elseif nnz(v(1:end-2)==v(end))\r\n% v(end-1)=[];\r\n% end\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; \r\n %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[3 3 2 3 5 5 3 3 5 1;\r\n 5 5 5 2 5 5 5 5 5 5;\r\n 1 5 5 5 5 1 1 5 5 2;\r\n 0 5 5 5 5 2 5 5 3 3;\r\n 0 5 5 5 1 3 5 5 5 5;\r\n 5 5 5 5 2 3 5 5 5 0;\r\n 3 2 5 5 1 5 5 5 5 2;\r\n 3 5 5 2 0 5 5 5 5 2;\r\n 5 5 5 5 5 5 2 5 5 5;\r\n 3 5 1 3 5 5 3 3 2 3]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n '151';\r\n '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n '15551';\r\n '25055';\r\n '55253';\r\n '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n%Source: Games World of Puzzles October 2020\r\n s=[5 1 5 5 3 5 5 5 0 1;\r\n 5 0 5 5 5 3 3 5 5 5;\r\n 5 5 5 1 2 5 5 5 3 5;\r\n 2 5 5 5 5 5 2 0 5 2;\r\n 0 5 5 5 5 5 5 5 5 5;\r\n 5 5 5 5 5 5 5 5 5 3;\r\n 3 5 1 2 5 5 5 5 5 1;\r\n 5 3 5 5 5 3 0 5 5 5;\r\n 5 5 5 0 0 5 5 5 3 5;\r\n 2 1 5 5 5 1 5 5 3 5]; % solves with recursive\r\n\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T20:32:38.000Z","updated_at":"2020-11-12T23:28:31.000Z","published_at":"2020-11-12T23:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink IV: Recursive (medium size)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques with a single Evolving but is solveable using Recursion with limited Guessing. Cases of Trivial, Gimmes, and single Evolve should be solved prior to invoking Recursion. When Evolve is used within a recursive routine that asserts incorrect content the Evolve may produce an invalid output for the invalid input. The two medium test cases are from Games World of Puzzles October 2020. I was unable to manually solve these puzzles on my first attempt prior to making an error thus I decided to program this simple pencil puzzle. This set of five Cody Challenges is the result of five days banging my keyboard to solve Slitherlink.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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the number","description":"Using the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\r\n\r\nExamples:\r\n\r\n '10007.76' should return 'Ten thousand and seven point seven six'\r\n '001102.34' should return 'One thousand one hundred and two point three four'\r\n '.4' should return 'Zero point four'\r\n '4.' should return 'Four'\r\n '0.' and '.' should return 'Zero'\r\n '340,139' should return 'Three hundred and forty thousand one hundred and thirty nine'\r\n\r\n\r\nIn the British short scale notation:\r\n\r\n 1 Hundred = 10^2\r\n 1 Thousand = 10^3\r\n 1 Million = 10^6\r\n 1 Billion = 10^9\r\n 1 Trillion = 10^12\r\n 1 Quadrillion = 10^15\r\n 1 Quintillion = 10^18\r\n 1 Sextillion = 10^21\r\n 1 Septillion = 10^24\r\n 1 Octillion = 10^27\r\n 1 Nonillion = 10^30\r\n 1 Decillion = 10^33\r\n 1 Vigintillion = 10^63\r\n 1 Centillion = 10^303\r\n","description_html":"\u003cp\u003eUsing the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e'10007.76' should return 'Ten thousand and seven point seven six'\r\n'001102.34' should return 'One thousand one hundred and two point three four'\r\n'.4' should return 'Zero point four'\r\n'4.' should return 'Four'\r\n'0.' and '.' should return 'Zero'\r\n'340,139' should return 'Three hundred and forty thousand one hundred and thirty nine'\r\n\u003c/pre\u003e\u003cp\u003eIn the British short scale notation:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 Hundred = 10^2\r\n1 Thousand = 10^3\r\n1 Million = 10^6\r\n1 Billion = 10^9\r\n1 Trillion = 10^12\r\n1 Quadrillion = 10^15\r\n1 Quintillion = 10^18\r\n1 Sextillion = 10^21\r\n1 Septillion = 10^24\r\n1 Octillion = 10^27\r\n1 Nonillion = 10^30\r\n1 Decillion = 10^33\r\n1 Vigintillion = 10^63\r\n1 Centillion = 10^303\r\n\u003c/pre\u003e","function_template":"function out_str = n2t(in_str)\r\n out_str = in_str;\r\nend","test_suite":"%%\r\nx = '0';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0.003';\r\ny_correct = 'Zero point zero zero three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0.';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '.343';\r\ny_correct = 'Zero point three four three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n%%\r\nx = '.';\r\ny_correct = 'Zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx='23000000011.2330';\r\ny_correct = 'Twenty three billion and eleven point two three three zero';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '1,700,343.04014';\r\ny_correct = 'One million seven hundred thousand three hundred and forty three point zero four zero one four';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '4,003,900,030,001';\r\ny_correct = 'Four trillion three billion nine hundred million thirty thousand and one';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '50506';\r\ny_correct = 'Fifty thousand five hundred and six';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '5001000';\r\ny_correct = 'Five million one thousand';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '101000';\r\ny_correct = 'One hundred and one thousand';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '100003.0000341';\r\ny_correct = 'One hundred thousand and three point zero zero zero zero three four one';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '3.14159265358979';\r\ny_correct = 'Three point one four one five nine two six five three five eight nine seven nine';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '0497, 367, 158, 401, 387, 490, 191, 320, 142.72931574943292322499550584316';\r\ny_correct = 'Four hundred and ninety seven septillion three hundred and sixty seven sextillion one hundred and fifty eight quintillion four hundred and one quadrillion three hundred and eighty seven trillion four hundred and ninety billion one hundred and ninety one million three hundred and twenty thousand one hundred and forty two point seven two nine three one five seven four nine four three two nine two three two two four nine nine five five zero five eight four three one six';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '10000000000000000000000000600000000000000000000000000000000000000';\r\ny_correct = 'Ten vigintillion six hundred thousand decillion';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n%%\r\nx = '2878366142600744685534686064407323355445697471111330511689595534';\r\ny_correct = 'Two vigintillion eight hundred and seventy eight octillion three hundred and sixty six septillion one hundred and forty two sextillion six hundred quintillion seven hundred and forty four quadrillion six hundred and eighty five trillion five hundred and thirty four billion six hundred and eighty six million sixty four thousand four hundred and seven decillion three hundred and twenty three nonillion three hundred and fifty five octillion four hundred and forty five septillion six hundred and ninety seven sextillion four hundred and seventy one quintillion one hundred and eleven quadrillion three hundred and thirty trillion five hundred and eleven billion six hundred and eighty nine million five hundred and ninety five thousand five hundred and thirty four';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nx = '34000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001.580023';\r\ny_correct = 'Thirty four centillion and one point five eight zero zero two three';\r\nassert(isequal(n2t(x),y_correct), [x ' ' n2t(x)])\r\n\r\n%%\r\nfiletext = fileread('n2t.m');\r\nassert(isempty(strfind(filetext, 'regexp')));\r\nassert(isempty(strfind(filetext, 'assert')));\r\nassert(isempty(strfind(filetext, 'eval'))) \r\nassert(isempty(strfind(filetext, '!'))) \r\nassert(isempty(strfind(filetext, 'eighty seven trillion four'))) \r\nassert(isempty(strfind(filetext, 'three point zero four zero one four'))) \r\nassert(~isempty(strfind(filetext, 'three'))) \r\nassert(~isempty(strfind(filetext, 'n2t'))) \r\nassert(isempty(dir ('assert.m')))\r\nassert(isempty(dir ('fileread.m')))\r\nassert(isempty(dir ('isempty.m')))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":310490,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-20T13:01:17.000Z","updated_at":"2019-08-22T07:17:53.000Z","published_at":"2019-08-22T07:17:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing the British short scale notation, write a function to return the correct spelling of a number passed as a numeric string including decimal places. The function should be able to accept spaces, commas and leading zeros. Numbers beginning with '.' should return 'Zero point something' and numbers ending in '.' should state only the integer part.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['10007.76' should return 'Ten thousand and seven point seven six'\\n'001102.34' should return 'One thousand one hundred and two point three four'\\n'.4' should return 'Zero point four'\\n'4.' should return 'Four'\\n'0.' and '.' should return 'Zero'\\n'340,139' should return 'Three hundred and forty thousand one hundred and thirty nine']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the British short scale notation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 Hundred = 10^2\\n1 Thousand = 10^3\\n1 Million = 10^6\\n1 Billion = 10^9\\n1 Trillion = 10^12\\n1 Quadrillion = 10^15\\n1 Quintillion = 10^18\\n1 Sextillion = 10^21\\n1 Septillion = 10^24\\n1 Octillion = 10^27\\n1 Nonillion = 10^30\\n1 Decillion = 10^33\\n1 Vigintillion = 10^63\\n1 Centillion = 10^303]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47468,"title":"Slitherlink III: Evolve","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 615.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 307.833px; transform-origin: 407px 307.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 147px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 73.5px; text-align: left; transform-origin: 384px 73.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.75px 7.91667px; transform-origin: 84.75px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink III: Evolve\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265.65px 7.91667px; transform-origin: 265.65px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges. Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20] %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.783px 7.91667px; transform-origin: 366.783px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p' as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n %show_pfig(s,p1,c,emap,pmap,4)\r\n fprintf('sv trivial solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\ntic\r\nif nnz(sum(p,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n %show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv init solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n') \r\n return\r\n end\r\nend\r\n\r\n%Implement First Evolve\r\n [p,evalid]=evolve(p,bsegs,s,c,emap,pmap); % evalid not used in first evolve\r\n [sv,valid]=pcheck(s,p,bsegs); \r\n if valid\r\n% show_pfig(s,p,c,emap,pmap,4)\r\n fprintf('sv evolve solution\\n')\r\n fprintf('%i ',sv);fprintf('\\n')\r\n return\r\n end\r\n\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\nfunction [p,evalid]=evolve(p,bsegs,s,c,emap,pmap)\r\n evalid=0;\r\n [nr,nc]=size(s);\r\n pb=p+1;\r\n sp=s; % update sp for completed nodes by +10 0,10 1,11 2,12 3,13 to avoid reprocess\r\n while ~isequal(p,pb) %Keep evolving while there is any update to p\r\n pb=p;\r\n s1=find(sp==1)';\r\n for i=s1 %1 \r\n v=bsegs(i,:);\r\n %wv=[p(21,22) p(21,32) p(22,33) p(32,33)]; % \r\n wv=[p(v(1),v(2)) p(v(3),v(4)) p(v(5),v(6)) p(v(7),v(8))]; %LUDR values 0,1,5\r\n if sum(wv)\u003e5 % 0 non-5 segments, have single link\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=0;p(vz(2),vz(1))=0;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n elseif sum(wv)==1 % set 1 to 5\r\n for j=1:4\r\n if wv(j)==1\r\n vz=v(2*j-1:2*j);\r\n p(vz(1),vz(2))=5;p(vz(2),vz(1))=5;\r\n end\r\n end\r\n sp(i)=sp(i)+10;\r\n end % if sum \u003e5\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n end % i s1 1\r\n \r\n \r\n s2=find(sp==2)';\r\n for i=s2 %2\r\n v=bsegs(i,:);\r\n %insert code\r\n end %i s2 2\r\n \r\n s3=find(sp==3)';\r\n for i=s3 %3\r\n v=bsegs(i,:);\r\n %insert code\r\n end %i s3 3\r\n \r\n if ~isequal(p,pb) % s update created new walls\r\n %show_pfig(s,p,c,emap,pmap,2);\r\n continue;\r\n end\r\n %show_pfig(s,p,c,emap,pmap,2)\r\n \r\n %Process links for new walls\r\n % RR straight blocks perp, Binto corner makes B outcorner\r\n % RR corner blocks to corner\r\n % R into corner extends R\r\n % BB straight b1 b2 b3; need b2-1 to block b2+1, need b2+1 to block b2-1\r\n % R node with one option extends R\r\n [nrc,ncc]=size(c);\r\n % Bcorners if either corner edge B then both B\r\n if p(1,2)==0 || p(1,nrc+1)==0 %TLC\r\n p(1,2)=0; p(2,1)=0;\r\n p(1,nrc+1)=0;p(nrc+1,1)=0;\r\n end\r\n if p(nrc-1,nrc)==0 || p(nrc,2*nrc)==0 %BLC\r\n p(nrc-1,nrc)=0; p(nrc,nrc-1)=0;\r\n p(nrc,2*nrc)=0;p(2*nrc,nrc)=0;\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==0 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==0 %TRC\r\n %insert code\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==0 || p(nrc*ncc,nrc*ncc-nrc)==0 %BRC\r\n %insert code\r\n end\r\n \r\n % Rcorners if either corner edge R then both R\r\n if p(1,2)==5 || p(1,nrc+1)==5 %TLC\r\n %insert code\r\n end\r\n if p(nrc-1,nrc)==5 || p(nrc,2*nrc)==5 %BLC\r\n %insert code\r\n end\r\n if p((ncc-2)*nrc+1,(ncc-1)*nrc+1)==5 || p((ncc-1)*nrc+1,(ncc-1)*nrc+1+1)==5 %TRC\r\n %insert code\r\n end\r\n if p(nrc*ncc,nrc*ncc-1)==5 || p(nrc*ncc,nrc*ncc-nrc)==5 %BRC\r\n %insert code\r\n end\r\n \r\n % BB edges\r\n %Top Row\r\n for j=1:ncc-2 % Top Row Black seg pairs, fill down\r\n cv=c(1,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down, virtual cv(2)-1 == 0\r\n end\r\n if p(cv(1),cv(2))==5 \u0026\u0026 p(cv(2),cv(3))==5 % R seg also makes a B vert\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert vert down\r\n end\r\n end % j Top row\r\n %Bottom Row\r\n for j=1:ncc-2 % Bot Row Black seg pairs, fill down\r\n cv=c(nrc,j:j+2);\r\n %insert code\r\n end % j Bot row\r\n \r\n %Left Col edge\r\n for i=1:nrc-2 % L col Black seg pairs, fill hor rt\r\n cv=c(i:i+2,1);\r\n %insert code\r\n end % j L col\r\n %Right Col edge\r\n for i=1:nrc-2 % R col Black seg pairs, fill hor lt\r\n cv=c(i:i+2,ncc);\r\n %insert code\r\n end % \r\n \r\n %Hor segs not on an edge\r\n for i=2:nrc-1\r\n for j=1:ncc-2\r\n cv=c(i,j:j+2);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-1)==0\r\n p(cv(2),cv(2)+1)=0;p(cv(2)+1,cv(2))=0; % Insert v d\r\n end\r\n %insert code\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n %Ver segs not on an edge\r\n for i=1:nrc-2\r\n for j=2:ncc-1\r\n cv=c(i:i+2,j);\r\n if p(cv(1),cv(2))==0 \u0026\u0026 p(cv(2),cv(3))==0 \u0026\u0026 p(cv(2),cv(2)-nrc)==0\r\n p(cv(2),cv(2)+nrc)=0;p(cv(2)+nrc,cv(2))=0; % Insert h R\r\n end\r\n %insert code\r\n end % j 1:ncc-2\r\n end % i 2:nrc-1\r\n \r\n \r\n % RR corner blocks to corner\r\n %[rr;xr] [rr;rx] [xr;rr] [rx;rr]\r\n %RR;xR or RR;Rx\r\n for i=1:nrc-1\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab top pair\r\n if p(cv(1),cv(2))==5 % Top Red\r\n %insert code\r\n end % Top RR\r\n end %j\r\n end %i\r\n \r\n for i=2:nrc % Rx;RR xR;RR\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1); % grab lower pair\r\n if p(cv(1),cv(2))==5 % Bot Red\r\n %insert code\r\n end %Bot RR\r\n end %j\r\n end %i\r\n \r\n % Edge Bs xBB;xBx possible into a BB Tee is a B on the edges\r\n i=1; % Top\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n %insert code\r\n end\r\n end % j\r\n \r\n i=nrc; % Bottom % error 2nd time thru meant +nrc cv(2)\r\n for j=1:ncc-1\r\n cv=c(i,j:j+1);\r\n if p(cv(1),cv(2))==0 % BB Top\r\n %insert code\r\n end\r\n end % j\r\n \r\n j=ncc; % Right\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n %insert code\r\n end\r\n end % i\r\n \r\n j=1; % Left\r\n for i=1:nrc-1\r\n cv=c(i:i+1,j);\r\n if p(cv(1),cv(2))==0 % BB Right\r\n %insert code\r\n end\r\n end % i\r\n \r\n if ~isequal(p,pb),continue;end\r\n \r\n % R node with one option extends R \r\n% [pr5,pc5]=find(p==5);\r\n% for i=1:length(pr5)\r\n% if nnz(p(pr5(i),:)==5)==1 \u0026\u0026 nnz(p(pr5(i),:)\u003e0)==2 % single Red, 1 path out\r\n% new_node=find(p(pr5(i),:)==1);\r\n% p(pr5(i),new_node)=5;p(new_node,pr5(i))=5;\r\n% end\r\n% end\r\n \r\n [pr5,pc5]=find(p==5);\r\n pr5=unique(pr5); % could sort then remove dupes which are mids\r\n while ~isempty(pr5) %Extend Red Bars where there is only 1 option\r\n if nnz(p(pr5(1),:)==5)==1 \u0026\u0026 nnz(p(pr5(1),:)\u003e0)==2 % single Red, 1 path out\r\n new_node=find(p(pr5(1),:)==1);\r\n p(pr5(1),new_node)=5;p(new_node,pr5(1))=5;\r\n pr5(1)=new_node;\r\n else\r\n pr5(1)=[];\r\n end\r\n end\r\n \r\n %check if red seg closes a loop of less than X thus seg must be black\r\n if isequal(p,pb) % check for bad R bars only if no prior evolves have updated p\r\n % insert code\r\n end % isequal p pb after cells, ends make no change\r\n \r\n end % while p~=pb\r\n \r\n % Valid checks\r\n for sptr=1:nr*nc %invalid set/clear segment count\r\n vsptr=bsegs(sptr,:);\r\n psegs=[p(vsptr(1),vsptr(2)) p(vsptr(3),vsptr(4)) p(vsptr(5),vsptr(6)) p(vsptr(7),vsptr(8))];\r\n if s(sptr)==5\r\n if nnz(psegs==5)==4\r\n evalid=0;\r\n return\r\n else\r\n continue\r\n end\r\n end % s 5\r\n \r\n if s(sptr)\u003cnnz(psegs==5) % Too many set segments\r\n evalid=0;\r\n return\r\n end\r\n if s(sptr)\u003e4-nnz(psegs==0) % Too few set/settable segments\r\n evalid=0;\r\n return\r\n end\r\n ps=sum(p); %\r\n if nnz(ps==5) % Node with no escape\r\n evalid=0;\r\n return\r\n end\r\n if nnz(ps\u003e14) % Node with too many segments\r\n evalid=0;\r\n return\r\n end\r\n end % sptr\r\n \r\n %check for any loops created\r\n %show_pfig(s,p,c,emap,pmap,3)\r\n ps=sum(p);\r\n pidx=find(ps==10);\r\n pchecked=[];\r\n for i=pidx\r\n if nnz(pchecked==i),continue;end % Previously checked in a segment\r\n vn=find(p(i,:)==5); % Guaranteed 2 points\r\n if nnz(pchecked==vn(1)) || nnz(pchecked==vn(2))\r\n pchecked=[pchecked i];\r\n continue;\r\n end\r\n v=[i find(p(i,:)==5,1,'first')];\r\n Lv=0;\r\n while length(v)\u003eLv\r\n Lv=length(v);\r\n vn=find(p(v(end),:)==5);\r\n if length(vn)==1,break;end % No loop\r\n if vn(1)==v(end-1)\r\n v=[v vn(2)];\r\n else\r\n v=[v vn(1)];\r\n end\r\n if v(1)==v(end),break;end % Loop created\r\n end % while extending\r\n pchecked=[pchecked v];\r\n \r\n if Lv\u003c5,continue;end % Need at least 4 segments to make a loop [1 2 4 3 1]\r\n if v(1)==v(end) % Loop created, may be final solve or a Failed small loop\r\n if (length(v)-1)\u003cnnz(p==5)/2 %invalid loop [1 2 4 3 1] loop\r\n evalid=0;\r\n return\r\n else % Possible solve\r\n [sv,valid]=pcheck(s,p,bsegs); % check if solved\r\n if valid\r\n evalid=1;\r\n return\r\n else % invalid loop connect thus must be 0\r\n evalid=0;\r\n return\r\n end\r\n end % Lv-1 compare to total current segments\r\n end % v 1 end)\r\n end % pidx\r\n \r\n evalid=1;\r\n \r\nend % evolve\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge\r\n\r\n [nr,nc]=size(s);\r\n \r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n bidx=idx0(i);\r\n vb=bsegs(bidx,:);\r\n for j=1:2:7\r\n p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n p(vb(j+1),vb(j))=0;\r\n end\r\n \r\n if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(2,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n elseif nr0(i)==1 %T non-corner\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nr0(i)==nr %B non-corner\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==1 %L non-corner\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n \r\n elseif nc0(i)==nc\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end % if TL/TR/BL/BR/T/B/L/R\r\n \r\n end %i nr0 corners/edges/mid s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n bidx=idx1(i);\r\n if nr1(i)==1 \u0026\u0026 nc1(i)==1 %TL1\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==1 \u0026\u0026 nc1(i)==nc %TR1\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==1 %BL1\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n \r\n elseif nr1(i)==nr \u0026\u0026 nc1(i)==nc %BR1\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1 %TL3\r\n vbsegs=bsegs(bidx,1:4); %bidx, L,T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==1 \u0026\u0026 nc3(i)==nc %TR3\r\n vbsegs=bsegs(bidx,[3 4 7 8]); %bidx, T,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==1 %BL3\r\n vbsegs=bsegs(bidx,[1 2 5 6]); %bidx, L,B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n elseif nr3(i)==nr \u0026\u0026 nc3(i)==nc %BR3\r\n vbsegs=bsegs(bidx,5:8); %bidx, B,R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n \r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n bidx=idx2(i);\r\n \r\n if nr2(i)==1 \u0026\u0026 nc2(i)==1 %TL0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==1 \u0026\u0026 nc2(i)==nc %TR0\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==1 %BL\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n elseif nr2(i)==nr \u0026\u0026 nc2(i)==nc %BR\r\n if nr\u003e1\r\n vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if nc\u003e1\r\n vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n \r\n end % if TL/TR/BL/BR\r\n \r\n end %i s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n %0-3mid sets4 segs, clears 4 segs\r\n %0-3edge sets 4 segs, clears 2 segs on edge\r\n bidx=idx3(i);\r\n if nr3(i)==1 % Top edge\r\n if s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n \r\n elseif nr3(i)==nr % Bot Edge\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n \r\n elseif nc3(i)==1 %Left Edge\r\n if s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n \r\n elseif nc3(i)==nc % Rt edge\r\n if s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n \r\n else %non-edge 3\r\n if s(nr3(i)-1,nc3(i))==0 % Top 0 3below0\r\n vbsegs=bsegs(bidx,:); %bidx, L,B,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tset,Bclear\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i)+1,nc3(i))==0 % Below 0, 3above0\r\n vbsegs=bsegs(bidx,:); %bidx, L,T,R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L,R clear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Bset,Tclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n elseif s(nr3(i),nc3(i)-1)==0 % Left 0 3rt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBR set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Lset,Rclear\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n elseif s(nr3(i),nc3(i)+1)==0 % Right 0 3Lt0\r\n vbsegs=bsegs(bidx,:); %bidx, TBL set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx+1, Rset,Lclear\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, Tclear,Bclear\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n \r\n end\r\n end % Edges/Mid 3\r\n \r\n \r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n bidx=idx3(i);\r\n if nr3(i)==1\r\n if nc3(i)==1 % TL only one R or D possible\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % TR only one L or D possible. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Top Row L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n \r\n if nr3(i)\u003cnr % Mid section 33\r\n if nc3(i)==1 % check only one R and D p\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3 %D\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n if nc3(i)==nc % check only D. Process only D\r\n if s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end\r\n % Mid Row (not col 1 or nc) L or R or D possible, check only R/D\r\n if s(bidx+nr)==3\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+1 R Clr, idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n elseif s(bidx+1)==3\r\n vbsegs=bsegs(bidx,:); %bidx, TB set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx-nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx-nr B Clr, idx+1 B set,idx+nr B Clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n continue \r\n end\r\n \r\n if nr3(i)==nr % Bot row 33\r\n if nc3(i)==nc,continue;end % No process BR corner\r\n if s(bidx+nr)==3 %R\r\n vbsegs=bsegs(bidx,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx-1,:); %bidx, LR set,idx+nr R set,idx-1 R Clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n continue\r\n end\r\n \r\n end % i nr3 3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n \r\n bidx=idx3(i);\r\n if nr3(i)==1 % double diagonal zeros possible \r\n if s(bidx+1-nr)==0 %BL\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx+1+nr)==0 %BR\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==1\r\n \r\n if nr3(i)==nr % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %TL\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n if s(bidx-1+nr)==0 %TR\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n continue\r\n end % nr3==nr\r\n \r\n if nc3(i)==1 % double diagonal zeros possible \r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==1\r\n \r\n if nc3(i)==nc % double diagonal zeros possible \r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n continue\r\n end % nc3==nc\r\n \r\n %mid : check 4 courners\r\n if s(bidx-1-nr)==0 %LT\r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1-nr)==0 %LB\r\n vbsegs=bsegs(bidx,:); %bidx, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n if s(bidx-1+nr)==0 %RT\r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n end\r\n if s(bidx+1+nr)==0 %RB\r\n vbsegs=bsegs(bidx,:); %bidx, RB set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[1 0;0 1],'same');\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, LT set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+1+nr,:); %bidx+1+nr down diag, RB set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n \r\n if nr3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n if nc3(i)\u003e1 % Not left edge\r\n vbsegs=bsegs(bidx-nr,:); %bidx-nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003c=nc-2 % Not near right edge\r\n vbsegs=bsegs(bidx+1+2*nr,:); %bidx+1+2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2+nr,:); %bidx+2+nr, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1 % No single row/col\r\n nr3=[];\r\n else\r\n sp=s;\r\n sp(sp==5)=0;\r\n sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n [nr3,nc3]=find(sc==6); %3-0 adjacent set segs to 0/5\r\n nc3=nc3+1;\r\n idx3=find(sc==6)+nr; \r\n \r\n end\r\n \r\n for i=1:length(nr3)\r\n bidx=idx3(i); \r\n vbsegs=bsegs(bidx,:); %bidx, RT set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n \r\n vbsegs=bsegs(bidx+1-nr,:); %bidx+1+nr down diag, LB set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n \r\n if nr3(i)\u003e1 % Not top edge\r\n vbsegs=bsegs(bidx-1,:); %bidx-1, R clr\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n \r\n if nc3(i)\u003cnc % Not Right edge\r\n vbsegs=bsegs(bidx+nr,:); %bidx+nr, T clr\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n end\r\n \r\n if nc3(i)\u003e=3 % Not near Left edge\r\n vbsegs=bsegs(bidx+1-2*nr,:); %bidx+1-2nr, B clr\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n end\r\n if nr3(i)\u003c=nr-2 % Not near bottom edge\r\n vbsegs=bsegs(bidx+2-nr,:); %bidx+2-nr, L clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n \r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end % No single row/col\r\n \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, T set\r\n p(vbsegs(3),vbsegs(4))=5;\r\n p(vbsegs(4),vbsegs(3))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=1; %Top Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LB clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n p(vbsegs(7),vbsegs(8))=0;\r\n p(vbsegs(8),vbsegs(7))=0;\r\n end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n p(vbsegs(5),vbsegs(6))=5;\r\n p(vbsegs(6),vbsegs(5))=5;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(1),vbsegs(2))=0;\r\n p(vbsegs(2),vbsegs(1))=0;\r\n end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n p(vbsegs(1),vbsegs(1))=0;\r\n p(vbsegs(2),vbsegs(2))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n p(vbsegs(7),vbsegs(8))=5;\r\n p(vbsegs(8),vbsegs(7))=5;\r\n end\r\n end\r\n \r\n \r\n j=1; %Left Edge 31\r\n for i=1:nr-1\r\n if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n p(vbsegs(5),vbsegs(6))=0;\r\n p(vbsegs(6),vbsegs(5))=0;\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n bidx=i+(j-1)*nr; \r\n vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n p(vbsegs(7),vbsegs(7))=0;\r\n p(vbsegs(8),vbsegs(8))=0;\r\n p(vbsegs(3),vbsegs(4))=0;\r\n p(vbsegs(4),vbsegs(3))=0;\r\n vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n p(vbsegs(1),vbsegs(2))=5;\r\n p(vbsegs(2),vbsegs(1))=5;\r\n end\r\n end\r\n \r\nend % init basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n p=p*0;\r\n %p(?)=5\r\n p=p+p';\r\n return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index, indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n vnext=find(p(v(end),:)==5);\r\n if isempty(vnext),return;end % No connector - no solution\r\n p(v(end),vnext)=0;\r\n p(vnext,v(end))=0;\r\n v=[v vnext];\r\n end\r\n % v(1)==v(end) [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % bsegs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n for j=1:nc\r\n txt=num2str(s(i,j));\r\n t=text(j-.6,i-.5,txt); % reverse i,j j is y-row, i is col graph [col,row]\r\n t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if pv==0\r\n plot([b,d],[a,c],'k','LineWidth',5);\r\n end\r\n end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n pr=pmap(i,1);\r\n pc=pmap(i,2);\r\n pv=p(pr,pc);\r\n if pv~=1\r\n a=emap(pr,1);\r\n b=emap(pr,2);\r\n c=emap(pc,1);\r\n d=emap(pc,2);\r\n if b==d\r\n if a\u003cc\r\n a=max(0,a-.05);\r\n c=min(nr,c+.05);\r\n else % a\u003ec\r\n a=min(nr,a+.05);\r\n c=max(0,c-.05);\r\n end\r\n else %a==c\r\n if b\u003cd\r\n b=max(0,b-.05);\r\n d=min(nc,d+.05);\r\n else % b\u003ed\r\n b=min(nc,b+.05);\r\n d=max(0,d-.05);\r\n end\r\n end\r\n if pv==5\r\n plot([b,d],[a,c],'r','LineWidth',5);\r\n end\r\n end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine. Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8] four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1 1 4 7 1 5 9\r\n% A A C A D\r\n%2 5 2 5 8 2 6 10\r\n% B B D B E\r\n%3 6 3 6 9 3 7 11\r\n% C F\r\n% 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['252';\r\n '151';\r\n '212']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n s=['3553';\r\n '1551';\r\n '2112']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['3212';\r\n '1521';\r\n '0532';\r\n '1322']-'0'; % evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=['33353';\r\n '15551';\r\n '25055';\r\n '55253';\r\n '13511']-'0';% evolves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5]; % Trivial 33\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n for j=1:nc\r\n ptr=i+nr*(j-1);\r\n bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n for j=1:2:7\r\n p(bsegs(i,j),bsegs(i,j+1))=1;\r\n end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n for j=1:2:7 % besgs pairs loop\r\n if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n schk(i)=schk(i)+1;\r\n end\r\n end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n valid=1;\r\n else\r\n valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T19:13:03.000Z","updated_at":"2020-11-12T23:28:07.000Z","published_at":"2020-11-12T23:28:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c. The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink III: Evolve\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is not solved using only the Gimmes from Slitherlink Starting Techniques but requires additional Evolving that is always valid for a valid input. Evolve examples are a Red bar into a corner must continue that Red bar out of the corner, an s=1 cell with a Red bar must have Black bars on its other 3 edges. Cases of Trivial and Gimmes should be solved prior to invoking Evolve. The Evolve subroutine is the most critical routine and must be very comprehensive. A general Evolve routine should check if the output State is valid. When Evolve is used within a recursive routine that asserts possibly incorrect content the Evolve may produce an invalid output for the invalid input.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s, matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap) are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5 9 13 17 % c matrix [3 1 1 2; % s matrix [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes 2 1 0 1; %qty edges % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners 1 2 1 2] %adjacent % Red Line path\\n% 4 8 12 16 20] %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink II: Gimmes, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check 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Circular Perfect Square Sequence ","description":"A sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u003cN\u003c52 as solutions beyond 51 may take significant processing time.","description_html":"\u003cp\u003eA sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u0026lt;N\u0026lt;52 as solutions beyond 51 may take significant processing time.\u003c/p\u003e","function_template":"function [v] = solve_PerfectSqr_Seq(n)\r\n v(1:n)=1:n;\r\nend","test_suite":"%%\r\n n=32;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));\r\n%%\r\n n=33;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));\r\n%%\r\n n=41;\r\n [vr]=solve_PerfectSqr_Seq(n);\r\n \r\n % Verification of solutions for 32 thru 51\r\n Tests=[1 1 1]; % start in Pass : Looks for fails\r\n if n\u003c33\r\n sqrs=[4 9 16 25 36 49];\r\n elseif n\u003c41\r\n sqrs=[4 9 16 25 36 49 64];\r\n elseif n\u003c51\r\n sqrs=[4 9 16 25 36 49 64 81];\r\n else % valid up thru n=61\r\n sqrs=[4 9 16 25 36 49 64 81 100];\r\n end\r\n \r\n \r\n if isempty(vr)\r\n Tests=[0 0 0];\r\n else\r\n % Check use all 1 thru 32\r\n if length(unique(vr))~=n || max(vr)\u003en || min(vr)\u003c1, Tests(1)=0;end\r\n \r\n % Check Squareness\r\n for i=1:length(vr)-1\r\n if isempty(intersect(vr(i)+vr(i+1),sqrs))\r\n Tests(2)=0;\r\n end\r\n end\r\n \r\n % Check Ends Squareness\r\n if isempty(intersect(vr(1)+vr(length(vr)),sqrs)),Tests(3)=0;end\r\n end\r\n \r\n assert(isequal(Tests,[1 1 1]));","published":true,"deleted":false,"likes_count":1,"comments_count":7,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-04-29T01:10:25.000Z","updated_at":"2026-01-29T06:26:26.000Z","published_at":"2012-04-29T01:10:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA sequence v(1:N) made of values 1:N can be created for N\u003e31 such that v(i)+v(i+1) is a perfect square. The sum of v(N)+v(1) must also be a perfect square. All values 1 thru N are required and the vector must be of length N. (e.g. For N=32 the possible perfect squares are [4 9 16 25 36 49]. By inspection the value 32 must be bracketed by 4 and 17). The Test set will be limited to 31\u0026lt;N\u0026lt;52 as solutions beyond 51 may take significant processing time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1173,"title":"Binpack Contest: Retro - - Best Packing","description":"The \u003chttp://www.mathworks.com/matlabcentral/contest/contests/3/rules Full Binpack Rules and examples\u003e.\r\n\r\nThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\r\n\r\nBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\r\n\r\n*Input:* [songList, mediaLength]\r\n\r\n*Output:* indexList\r\n\r\n*Example:*\r\n\r\nInput: [ 0.5 2 3 1.5 4], [5.6]\r\n\r\nOutput: [4 5] as 1.5+4 is very near and below 5.6.\r\n\r\nThe answer of [1 2 3] is also valid and also gives 5.5.\r\n\r\n*Scoring:* 1000*(Known_Best_Possibles - sum(songList(indexList))\r\n\r\n*Initial Leader:* Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\r\n\r\n\r\nFinal Score of 1 achieved in \u003c 6 Cody seconds for entry 10.\r\n\r\nFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\r\n\r\n","description_html":"\u003cp\u003eThe \u003ca href=\"http://www.mathworks.com/matlabcentral/contest/contests/3/rules\"\u003eFull Binpack Rules and examples\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\u003c/p\u003e\u003cp\u003eBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [songList, mediaLength]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e indexList\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInput: [ 0.5 2 3 1.5 4], [5.6]\u003c/p\u003e\u003cp\u003eOutput: [4 5] as 1.5+4 is very near and below 5.6.\u003c/p\u003e\u003cp\u003eThe answer of [1 2 3] is also valid and also gives 5.5.\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e 1000*(Known_Best_Possibles - sum(songList(indexList))\u003c/p\u003e\u003cp\u003e\u003cb\u003eInitial Leader:\u003c/b\u003e Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\u003c/p\u003e\u003cp\u003eFinal Score of 1 achieved in \u0026lt; 6 Cody seconds for entry 10.\u003c/p\u003e\u003cp\u003eFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\u003c/p\u003e","function_template":"function indexList = binpack_scr(songList,mediaLength)\r\n indexList=[1 2];\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',200);\r\n%%\r\nmediaLength=45;\r\nrng(0)\r\nsongList=floor(10000*rand(1,100))/10000;\r\nindexList = binpack_scr(songList,mediaLength) ;\r\nsum(songList(indexList))\r\n%[songList(indexList)' indexList']\r\nassert(sum(songList(indexList))\u003e44.8) % anti hardcode\r\n%%\r\na{1} = [4.3078 2.5481 1.4903 5.4302 3.4142 2.9736 3.3768 ...\r\n 2.1612 3.3024 0.3269 2.6761 4.2530 2.6648 1.9644 ...\r\n 3.3389 22.122 4.1015 3.2104 2.3945 4.7151];\r\na{2} = [1.2671 3.1377 4.0687 4.1459 3.6469 6.1881 8.2452 ...\r\n 7.3962 9.7071 10.4798 11.4082 12.2282 12.6320 13.9705 ...\r\n 13.8851 15.6195 17.0187 18.5778 18.4140 20.0473];\r\na{3} = [1.6283 6.0703 8.1323 2.6226 3.1230 3.0081 6.1405 ...\r\n 1.1896 4.2769 5.0951 6.4869 3.9215 2.5858 4.7130 ...\r\n 4.5529];\r\na{4} = [40:-1:1]+.1;\r\na{5} = [1.0979 3.5540 1.8627 0.0849 3.2110 3.6466 4.8065 ...\r\n 3.2717 0.1336 2.5008 0.4508 3.0700 3.1658 0.8683 ...\r\n 3.5533 3.7528 2.7802 4.2016 1.6372 9.6254 1.3264 ...\r\n 0.3160 4.3212 3.0192 0.7744 2.3970 1.7416 2.4751 ...\r\n 1.0470 1.9091];\r\na{6} = [1 1 2 3 5 8 13 21 34]+.1;\r\na{7} = [0.8651 3.3312 0.2507 0.5754 2.2929 2.3818 2.3783 ...\r\n 0.0753 0.6546 0.3493 0.3734 1.4516 1.1766 4.3664 ...\r\n 0.2728 20.279 2.1335 0.1186 0.1913 1.6647 0.5888 ...\r\n 2.6724 1.4286 3.2471 1.3836 1.7160 2.5080 3.1875 ...\r\n 2.8819 1.1423 0.7998 1.3800 1.6312 1.4238 2.5805 ...\r\n 1.3372 2.3817 2.4049 0.0396 0.3134];\r\na{8} = [pi*ones(1,10) exp(1)*ones(1,10)];\r\na{9} = [1.6041 0.2573 1.0565 1.4151 0.8051 0.5287 0.2193 ...\r\n 0.9219 2.1707 0.0592 1.0106 0.6145 0.5077 1.6924 ...\r\n 0.5913 0.6436 0.3803 1.0091 0.0195 0.0482 20.000 ...\r\n 0.3179 1.0950 1.8740 0.4282 0.8956 0.7310 0.5779 ...\r\n 0.0403 0.6771 0.5689 0.2556 0.3775 0.2959 1.4751 ...\r\n 0.2340 8.1184 0.3148 1.4435 0.3510 0.6232 0.7990 ...\r\n 0.9409 0.9921 0.2120 0.2379 1.0078 0.7420 1.0823 ...\r\n 0.1315];\r\na{10}= [1.6041 0.2573 1.0565 1.4151 0.8051 0.5287 0.2193 ...\r\n 0.9219 2.1707 0.0592 1.0106 0.6145 0.5077 1.6924 ...\r\n 0.5913 0.6436 0.3803 10.091 0.0195 0.0482 20.000 ...\r\n 0.3179 1.0950 1.8740 44.999 0.8956 0.7310 0.5779 ...\r\n 0.0403 0.6771 0.5689 0.2556 0.3775 0.2959 1.4751 ...\r\n 0.2340 0.1184 0.3148 1.4435 0.3510 0.6232 0.7990 ...\r\n 0.9409 0.9921 0.2120 0.2379 1.0078 0.7420 1.0823 ...\r\n 0.1315];\r\na{11}= [40*ones(1,50) ones(1,20)]+0.05;\r\na{12}= 4.3 + sin(1:100);\r\n\r\nmediaLength=45;\r\n\r\nnet_gap=0;\r\nt0=clock;\r\nfor j=1:1\r\nfor i=1:12\r\n songList=a{i};\r\n indexList = binpack_scr(songList,mediaLength) ;\r\n indexList=unique(indexList); % No dupes\r\n total(i)=sum(songList(indexList));\r\n if total(i)\u003e45+1.5*eps(mediaLength) % Rqmt \u003c= 45\r\n total(i)=-Inf;\r\n end\r\n net_gap=net_gap+45-total(i) ;\r\nend\r\nend\r\ntte=etime(clock,t0);\r\nfprintf('Total Time E %f\\n',tte)\r\nfprintf('Totals: ');fprintf('%.5f ',total);fprintf('\\n')\r\nfprintf('Net Gap: %.2f\\n',net_gap)\r\n%format long\r\nfprintf('Performance: %.4f\\n',net_gap/(12*45))\r\nfprintf('Score=1000*(12*45-1.76987514-sum(total): %.3f\\n',1000*(12*45-1.76987514-sum(total)))\r\nfprintf('Final Score %i\\n',round(1000*(12*45-1.76987514-sum(total))))\r\n\r\nScore=round(1000*(12*45-1.76987514-sum(total)));\r\n\r\nassert(Score\u003e=0)\r\n\r\n\r\nfeval( @assignin,'caller','score',floor(min( 200,Score )) );\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":"2013-01-06T00:41:02.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-01-04T16:28:33.000Z","updated_at":"2013-01-06T00:52:17.000Z","published_at":"2013-01-04T18:16:41.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/contest/contests/3/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFull Binpack Rules and examples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is a partial replay of the First Matlab Contest, 1998 BinPack. The Twist is to Achieve the Best Packing, no time or code size penalty. The twelve songList sets will be scored once each.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBrief Challenge statement: Pack a 45(mediaLength) minute CD as maximally as possible given a list of songs of varying lengths. No penalty for unused songs. No song duplication allowed. Return the indices of the songs used.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [songList, mediaLength]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e indexList\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInput: [ 0.5 2 3 1.5 4], [5.6]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput: [4 5] as 1.5+4 is very near and below 5.6.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe answer of [1 2 3] is also valid and also gives 5.5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 1000*(Known_Best_Possibles - sum(songList(indexList))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInitial Leader:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Cases [2 3 4 6 8 11] have best possible scores of [44.9990 44.9971 44.8 44.6 44.584024853.. 44.25]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFinal Score of 1 achieved in \u0026lt; 6 Cody seconds for entry 10.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFinal Score of 0, optimal, achieved using recursive searches with iter limits and thresholds.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" 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