{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-16T00:12:35.000Z","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":"2026-04-16T00:00:00.000Z","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":52839,"title":"Easy Sequences 33: Web Trapped Ant","description":"An ant is trapped on a spider web inside a can with open top. The can has a radius  and height . A spider sitting on the outside face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure below.\r\n                         \r\n is the initial distance of the spider from the from the bottom of the can,  is the distance of the ant from the top of the can, and  and , are the y-offsets of the spider and ant, respectively, from the center of the circular top.\r\nIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. Please round-off your answer to the nearest four decimal places.\r\nCLARIFICATIONS: \r\nIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\r\nThe dotted lines is not the spider's path. They are used only to project the heights to the side.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440000534057617px; 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: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 609px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; vertical-align: baseline; \"\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; text-align: left; 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; \"\u003e\u003cspan style=\"\"\u003eAn \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eant is\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; \"\u003e\u003cspan style=\"\"\u003e trapped on a spider web \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003einside\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; \"\u003e\u003cspan style=\"\"\u003e a can with open top. The can has a radius \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003eR\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e and height \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003eH\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e. A \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003espider sitting\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; \"\u003e\u003cspan style=\"\"\u003e on the \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eoutside\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; \"\u003e\u003cspan style=\"\"\u003e face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 315px; 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; text-align: left; 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; \"\u003e\u003cspan style=\"\"\u003e                         \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"551\" height=\"309\" style=\"vertical-align: baseline;width: 551px;height: 309px\" 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src=\"data:image/png;base64,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\" width=\"18\" height=\"18\" style=\"width: 18px; 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; \"\u003e\u003cspan style=\"\"\u003e is the distance of the ant from the top of the can, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"16.5\" height=\"18\" style=\"width: 16.5px; 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; \"\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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\" width=\"18\" height=\"18\" style=\"width: 18px; 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; \"\u003e\u003cspan style=\"\"\u003e, are the y-offsets of the spider and ant, respectively, from the center of the circular top.\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; text-align: left; 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; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003eIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. \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; \"\u003e\u003cspan style=\"\"\u003ePlease round-off your answer to the nearest four 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; text-align: left; 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; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003eCLARIFICATIONS: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 80px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 60px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; text-align: left; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; \"\u003e\u003cspan style=\"\"\u003eIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; text-align: left; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; \"\u003e\u003cspan style=\"\"\u003eThe dotted lines is not the spider's path. They are used only to project the heights to the side.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = spiderPath(H,R,hs,ha,xs,xa)\r\n    y = x;\r\nend","test_suite":"%%\r\nH = 10; R = 5; hs = 1; ha = 1; xs = 1; xa = 1;\r\ns_correct = 18.6210;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 5; R = 2; hs = 0.3; ha = 0.4; xs = 0.5; xa = 0.6;\r\ns_correct = 8.0120;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 15.5; R = 5.5; hs = 3; ha = 2.5; xs = 1.5; xa = 1;\r\ns_correct = 22.4960;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 123; R = 123; hs = 12; ha = 25; xs = 30; xa = 40;\r\ns_correct = 399.8218;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = linspace(5,150,50); R = linspace(5,120,50); \r\nhs = linspace(0.5,10,50); ha = linspace(0.5,20,100); \r\nxs = linspace(0.4,12,50); xa = linspace(0.4,10,50);\r\ns = sum(arrayfun(@(i) spiderPath(H(i),R(i),hs(i),ha(i),xs(i),xa(i)),1:50));\r\ns_correct = 10509.4142;\r\nassert(isequal(s,s_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":8,"created_by":255988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2021-10-02T18:43:11.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-09-30T07:18:49.000Z","updated_at":"2026-02-07T13:53:16.000Z","published_at":"2021-09-30T08:12:11.000Z","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\u003eAn \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eant is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e trapped on a spider web \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003einside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e a can with open top. The can has a radius \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\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and height \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\u003eH\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. A \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003espider sitting\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e on the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eoutside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure 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=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"309\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"551\\\"/\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: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\u003ehs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the initial distance of the spider from the from the bottom of the can, \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\u003eha\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the distance of the ant from the top of the can, 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\u003exs\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\u003exa\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, are the y-offsets of the spider and ant, respectively, from the center of the circular top.\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\u003eIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ePlease round-off your answer to the nearest four 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:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCLARIFICATIONS: \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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe dotted lines is not the spider's path. They are used only to project the heights to the 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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":52427,"title":"ICFP2021 Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","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. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*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_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\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.\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: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; 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: 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://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: 148.5px 8px; transform-origin: 148.5px 8px; 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: 30.5px 8px; transform-origin: 30.5px 8px; 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: 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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; 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 84px; text-align: left; transform-origin: 384px 84px; 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: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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: 332px 8px; transform-origin: 332px 8px; 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\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: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*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: 253px 8px; transform-origin: 253px 8px; 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: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(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: 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: 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: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\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: 379.5px 8px; transform-origin: 379.5px 8px; 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: 43.5px 8px; transform-origin: 43.5px 8px; 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: 259px 8px; transform-origin: 259px 8px; 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\u003cdiv style=\"block-size: 358px; 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 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\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% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' 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%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\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 %write_submission_bonus\r\n% \r\n% \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","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(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\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\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":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","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://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. The starting node/seg map to show guesses. \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 ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 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\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*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_ICFP004(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. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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, write ICFP solution files using Bonuses, and plots.\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 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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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- THE VOID SINGULARITY ( PHASE OMEGA-ZERO )","description":"Background:\r\nThe Swarm has reached the edge of the known universe, entering the 5th Dimension (The Void). Here, Euclidean geometry is replaced by Hyper-Torus Topology, and energy is taxed by Universal Entropy. To reach the final gate, you must navigate a swarm of particles through a 5D coordinate system:  where is the Phase Shift and  is the Spin Frequency.\r\nObjective:\r\nWrite a function [max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params) that calculates the maximum remaining energy and the optimal path for a swarm.\r\nThe Laws of the Void:\r\n1. 5D Hyper-Torus Distance: All dimensions have a period . The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\r\n2. Relativistic Drag (): The swarm's velocity is not constant. It decays vased on the current energy and the node's mass : \r\n                                                    \r\n3. Exponential Entropy Tax: Each jump increases the Void's entropy. The cost of the -th jump is:  \r\n\r\n, where  is the step number ( starting from 1 ).\r\n\r\n4. Quantum Spin Exclusion: If , particles must maintain a \"Spin Buffer\". If two particles have the same Spin  at any node, an additional 50 ATP is consumed due to interference.\r\n5. Predator Intercept: The Predator moves at a constant speed . It starts at pred_start and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced to -1 ( Swarm Annihilated )","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: 572.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 286.433px; transform-origin: 468.5px 286.433px; 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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eBackground\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:\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; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 31.5px; text-align: left; transform-origin: 444.5px 31.5px; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Swarm has reached the edge of the known universe, entering the \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; \"\u003e5th Dimension (The Void)\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. Here, Euclidean geometry is replaced 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHyper-Torus Topology\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, and energy is taxed 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eUniversal Entropy\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. To reach the final gate, you must navigate a swarm of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eM\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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=\"\"\u003eparticles through a 5D coordinate system: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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width=\"83\" height=\"18\" style=\"width: 83px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eψ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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=\"\"\u003eis the Phase Shift and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eω\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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 is the Spin Frequency.\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eObjective\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:\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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e[max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params)\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 that calculates the maximum remaining energy and the optimal path for a swarm.\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Laws of the Void:\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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e1. 5D Hyper-Torus Distance:\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 All dimensions have a period \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"54\" height=\"18\" style=\"width: 54px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; 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 10.7333px; text-align: left; transform-origin: 444.5px 10.7333px; 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=\"font-weight: 700; \"\u003e2. Relativistic Drag\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 (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"24.5\" height=\"20\" style=\"width: 24.5px; height: 20px;\"\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): The swarm's velocity is not constant. It decays vased on the current energy \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand the node's mass \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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: 55.9px; 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 27.95px; text-align: left; transform-origin: 444.5px 27.95px; 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=\"\"\u003e                                                    \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-33px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJ0AAABwCAYAAAAE/HKKAAAQAElEQVR4AeydCXRcVRnHZ5JJW2xMSOzCoTWZJqHBAmItiGBRZBE9igsgFoocVCiceuC0BxRQwQWPC+oRxQWOqHgEC7iDC0c2LaJsBWqRtjHN0qaFFJoWmqRZmtbf/zXv5SXzZuYNySxJvpz7zb3v3u/de+e7/3z33u8uUxSxP5NAjiVgoMuxwK24SMRAZyjIuQQMdDkXuRVooDMM5FwCBrqci9wKNNCNFgP2fsYSMNBlLLLRvRCPx5fMmzfvWWi/j/qJf5Dny8g9BvldjLQVpK2H3He6a2pqbpw5c2apn3G8hA10OW6plpaWO/ft2/cRim2GXHcz8ac2NzffRMReyO/2knZjNBo9ncgWaABa1tTUtOKll17qJDzunIEuD03W2tq6hWKfglz3KoH9UFIHUBeQeCj0p/7+/t/ij1tnoMtP00mbPesruopwFAp0c+fOPQhNdymJXUVFRde3tbXtITxunYEuf023zlf0G+vr65OOz0pKShbD+x7oh5s2bXoaf1w7A12emg+NtY2id0KR/fv3z+3p6QkEHROGcng+BzXwjsZ8Kbth+ArejQBdwdd3wlRw79692wBbh74QXWdlLBbTeE2Pw4ix3LlEHAfPV9By2wmPe2egy1MTdnd376boVkiuAnAlgK62trYOsF0Dwz0VFRX34k8IZ6DLUzNi7ugCUOpinRoQrnMCQx/RgYGBT/MYKy4uvn7NmjX9hCeEM9Dlrxk1NlvvFk9XG3fD8jEELwaIn4RuamxsfF5xE4UMdHlsSQC1wVd8DSsP0/QsEwn+FdDzvb29P8EXQPGGHLyHM8m4GXA2Qbuqq6vfPZRa2CEDXX7bR0biXYNVmMm47iCFMZGchX8qdN3WrVt34Cc4Vik20P1+GQ25j8QtzGzHjTY00NFiY+kyyQugvAC/YzbBnzVlypTpdXV1cwlfBZjurqys/DvhpI6xXi3a8hAYnmYJ7WX8ceEMdHlsJkDzCsVvhuQqMKPMQXtdwkMZaV9LN3lAM54A73QA+jC+1mTxCt8Z6PLYRhs3btSCvbpY1eJgtNb7CVwMiL6OTa6RcFLHmG4a/CfCsB2A+tdxiQp2ixYtKmEceAxjwLPxDyOPt0Cno12num/4eYg/QnwiN931xad05YVpZxHxxVAoZ6ALJaasMWmC4M1gKeVaaD3d7ir8lA4tJ7ve0TA5YzmAsQoAdEP90C951koGyY6LEndaR0fHUwD68M7Ozvt4/xpA+wypy8rLyzUujACeWfA8Cs9l4kHzHkl4LTxHQa4rJu9zxYdWPpQ8dpHXPwDvRS5DOt9Al05CWU6n0fwzWO02ua6pqUndbsqS0W5HwiDgvQwwrif8HSYg2jjwKOHzAcIn8OUEuOUE7oLva4z97sBGKA3rlEv5D7ndOCCTxnorvO3i6erq+jPhv0GONpZ2A1w3kc93yX8Fed2Pr/1/JfD0QqGcgS6UmLLKpAZ1ZrA05s9oyH+GLO0U+NSlvRn/swD1qYaGBgHwLzxHAJMAFEUrncnzt6DbmfH+Bl/aVelzCHcBGkdTEpZTPbqox5lVVVU1Ah6RX9y9e7e0cRTttoJ8l0E3tra2/hsAahIj7bwawN4DbyhnoAslpuwx0ZW6M9gGSvke5IACP6kDEBUkHgcNAJCvAzhv/AcgnG6V+HV0lzPxBYrdgOvn4oci2nFMvDRlM+V7oJsxY4bGhn8lj1o06U8BbDn/BGsFPvKSBtSS3Fby+B3dtUw6D8D7X/JY2tbW5qwjk5bWpQZd2teNYbQSoHFfoeG0I/h6NJH8tFnCfzhMb4Ke7O/v15qsA1SBBDCdRPx2/PsZc51MWOB6GEA9R9hxpaWlC8njGHjWASrP1KJuFgB9ASbNqJWPQObs8wO00pgC+zTC34CnijxO4f1lTHoy2ohgoEN6+XTMYHejqU6m8W4PWw8aW7PWMkCzCuOxp2EAw3tJO4Z87qL7W0dYs+Fi+O4ToIiP+FY7ykjX+E+mlmh9ff3rSY8CoEb4P0u4D/8cZqhzZs+ePZ3nYyG5y/jnOJP6/hRqV0SmZKDLVGJ55mcc5ZpKNPbSRlBHy82bN68aLXUd1VuN9vsSXeg0QKOJRoR4TVBIikSZbGgTwft40C5kHfY5A2At6Ovr+y1LaRqjRQCvJg+a2R5MHrPhTeqoj9x7kzIEJASCjlyW8CUm9YmlAFkVRBSAEJBkKtkOgP6nStFeh6C1fg5AnqS7PkvjK8ZhXaQ5W6eIPx4QltKmVxKeA2my0oH/Tt47jPVddaFvApzaocxrnlsLT2N7e7t2xPxrMHY547tZmsnSnS/lnR8R78yE8UO5QNChPif9iaVQ0ssD0549ezReu4Oie5gxfpqG/zLA+TZ0Ld3dpxobG12tth9AaOylycFKxnHqbrdNnTpV2lCz1BkAai8a7seAV2u+beR5McC8AuDeTNom3r+Art8x3zCD/TZxN8LzNoDfzky2Af8NxJ/Twh/xoV0g6PQ2YwJN5VVhPYr0ZRxVrocgohIT5sRS0PcrhDg0WCdtfA0AWyAfUHyR8PmMxdzxmVdN0hpIOxaKQfN4vkNjSN47i+fXQV9FK+4h/knCx0Pvgb4DfQyepeQpDDj5DZa7kjS9F8WfB8/3Fe8wZPCRFHTkMalPLPH9x8JZHgESSAU6sWugKl80qU4s6QsbZUcCKUFHn67t1M7WG/rzSXViKTvitlwlgZSgY6A6aU8sSThG2ZFAStBN5hNL2RG35SoJpAQdMxPZZ9TFileLxOPqxBL2pEWYADog97aj1+rf5gjAPsZEAilBRwkykazHdxzjurgTGPygMcf8xBKGzmnQSeR9xpw5c94wWFSgB19o3sAMLDIvEkgHOmk3z9pMDWvU0Pj+NbykJ5bElwlh6DwGI+cTGCdVrwumTJnyTFVVVU1QHmF4sTOtwZ5UCcmuNBq6MKgOI+OQzTKjeKAM/LJS4/qfg8IyEMqCrbSMTizphbBEVzgLTXor9CjAk6GzlfCTGJzdsr2sMuH1XspN4HLqfotRdKQMPuoXf1rQYTZx93vpvYxPLOmlMAS4VLE6gHY3Szm9aKcrsXg764gj38+Ed+S72XwGbJLVA5RhFIn4ZYBIhlxa0NHVae1N+6v0VsYnlvRSOtJiNGDTfq1hmwqD3suEN+j9bMbxj3KaUXOgDPxyTws61uq0n15drN7L6MSSXnCJsc40Jgdvg3QSaT7x2tmAF4mUlZXVoyUWAryGyspKb3+YkzjiIxNeumGbvY6QXyE8pgUdlRw2g+VZ259DnViC15lwALTPAKr7eO7Bfz3gWsdE4BRoMWm30F1qe0wFaXM7Ojp+AECvGtxsyCsHXCa8B96wz0KVQBjQjZzBarfJdU0hTiwBlPJYLPYbwHQhY8OL6Hr+A+BehzD6B/gjj38Sp2tNdT5AO1W1Y+ISxnLf1O4H+DyXCa/7Uq5nr265yX1LkQRCgQ5Gda/OLBLQhDqxpE1+8N4A4E5Fk+lCv8a6urojiFtJfqvA3GP4kaqqqoPxDyP+JYC5iXBSlwlv0kwsIe8SCAU6wKBZmRb+pZFCnVjauXPnGXw72bd0R+4/0HpLAdq9gHBVZ2fnSleT8XwIfDrk0cRar3dIhLgElwlvwssWUTASCAU6zWBpcJ1UCntiKYbmWsK3nALpFPgNPPeRx0K602tZXtPkhCTHaZVjFqG1mzdvdrQp4WQuE95keVh8niUQCnTMYDM6scTkQMtX2sevKwfOBWgXQL9mXCbzy7CvjBZdSIROLK3B16QFL9hlwhucg8UWggRCgW4UFVX+w4BUXV29kNmpDgor2xgfb4F2oQnVdRNM6jLhTZqJJeRfAgLFmNcCrbYDEOl8RRna6VIZdEXxePwqnlcwdnNOMc2fP1+TiHoq0EZ8yklEJrzkZ66AJTAa0KX6WjpfoetL74LpvNLS0l3Tp0/XbLUF4+9FTCIcA3BfX58uADwUgA47ac47CS4T3oSXLaKgJJAt0EWwtb2IxlsClUAxno+E7nJPmksKaDftIJFRWJe+6KS5ogMpE97ADCyyYCSQNdCF+YZouHdBG+huH0rHD19o3nR5WXpqCTAMmsYS4pGYuZZDq5gYNkO6jkwvOvfT8ay4vfh/hMe5tIfEKO+dTJwO6ittNbZZ9WYkDbmcgk6Vo0K38qWW4c+mGidhRvnFli1bZAfkcchlwjv0loXGSAJHY1M9gX/0KyCZvmT2kq21nHb7FcZ+AbCPsnRV2fvFq8UA0j5Pmu7B0+0CJEdORKHoPhWFPcop6PgCMgSfTle5ALB9gudH+/v7f0Bths1wedbvZYXmFb/R2EmAYdDjkH5KwL1KYgNj6vUASjcGtAGko2g/3eikQntoz07WzHV33cm06VHYdS8nQculeIkup6BjbLeRCp5A5f/Of8cf+WKXMakI/BnJTHgTv5bFjFYC7pLjYD7PsIZ+vsJMBK9WmwEuXbqoqBcIvxU6CWCeTZu+SFhXmelasZ0AUFYM8XmUU9CpVBbht1CxP7S2tursRcrJQya8ynvcUQFXGOVQTfVqIW34mIVmW0zc1ZoIMjzSj6zoBlAl7yDtA9BywCirRBSloltCldZAT9akgJ9yDjp/4RYuXAkAsCOo3QxIt33W87zSXVECVO7NUSRH3gzgbqZncm6IkobkWXfaKO1xlja1Zq+wRwY6TxQW8EnAr600vv4WvY53xSxdrUxdmggq7c6KigrvvuGSkhKlzScv9WIP4ic4A12CSCyCWai6U/fmTV0x+2u/VBiz6XfItJnjVcI/UZfrpjNW102gGs9tI+xdOeumyzfQSQpGwyRA96jfjZC2kib7/Vbf75NpOROgvX3whdXw6sbOwcdIjGd3PLeWLtk7qO8yyDfQSQo5ILRHPbbH3+NfmYPiRlUEoHI1mX6NR6tFXn5lZWVxgKXLszXBuJNJYY+b6FsfF1gfIa2X73uONnm4PPINdJJCFgmh10P6/YbnaMwPQwUtc78mA1xrent7h23EoMt8B+LS/scXCGuDLo8HHPY7mUo04+1Dyz3NP5lO+C3t7u52Nngc4IpExlAAbpbmSwIs/8wEbD+k4bQ9X7eTa2uWkgqa0GTOyTxVkn+QP2MG8dtRve6TtPWASccYxOoQs1rNePVd+0m/mOdP4l8yYtOugc6RVhY+ysvLd2FGuBwzw6UAz73nNwslRSK6ch/b2a8A+Sa0i35U5DWXg/ZyJwI70VbaGeTlRTf5Rh6Urq71sQQwFRVJK+6GZx9g28Ys91y62Bd5HuZM0w0Tx9g9DM7oZDZQA+kH3xKW+saqNDRKlLxkwqihsQ8i/JodILmFfxbd+1KJmUS7ub28MOiT1FzDh9I/7yUMBvgHe4C0MhH5rGwcunR7kOOAZ6A7IAf7zKEEDHQ5FLYVdUACBroDcrDPHErAQJdDYacrarKkG+gmS0sX0Pc00BVQY0yWqhjoxklLY4O7DQq8qLu0tHQ3tkD9tqu+zT3J+IjvqK2tx0XIdwAABEJJREFUdTdfijcvZKDLi9gLt1AZl7NB/m9soPNLo4DDGFwvhGSUTaDOzk7d+eeeqPtgMj7iEwy+I78yxuW7ofvHmK7yl2Og80vDwpKAdvpqi/lYkvL1KHug84qwwHiSANqwNgt0ml8GBjq/NCycEwkY6HIiZivELwEDnV8aWQoPDAxo+7fODWjHibYHaVdIlkor/GwNdFlqI90OH4/HL4zH4w9iQ9NpebekS7GX/QGzxHJ45riRk8k30GWptbXjtqWl5baWlpZTGJjr5irX1KHwh5qamn4Ez9axKL6oqEh79drJqwmA+3f6EjU6xz/NcfyT3ApthwKN0ynidT91QgUMdAkiKZiI0BVpb2/vAtznAe5awKyfRwr9bjpG8n2cfC+CT9dK9OPLaXPqx4l3/5EcH9ueNpC+EwbnKgn+AZ4gnOAMdAkisYgkEphKfAkkpzOt7uU6enYIgPYAxEcA20oAKC0eeKWvgc4Rl32EkIAmQy5b0jOtYgB0e6CtAG+znkeSgW6kROw5QQKM63RhzvFuAmDSmVbvvKsb7/rM1nWpzsPd3d3+n25wk+00mCcJCySVwL59+/wX5nQxcUnoWv0v0822MLa8WmNNf7wbNk3nSsL8pBIoLi7WiX4BTzybYrHYBgVcwvRzEDPYu6urqz1t6KYF+Qa6IKlY3EgJ6H4SXfWq6yLWNDQ07PAzlJSULOZ5Nt3uMDASF+hyBrrA0i2y4CVQVVWllRT3x2YidK26/kt2QdW9GA13IgFd4fts0F10pCU4A12CSCzCLwG0mHvfnBONNrsdoLlGYv1eyGoSdH+JwEgwvTPQpZfRpOZgJqprJKTt1LU+pA2j2OIcY3BfX59u6tQP1MhuF3gXXZDwDHRBUrE4VwLehTmKwPY27P4S3VuH5tPNTSntdnrXTwY6vzQsPEwC/vvmSOgDdA/jD3PElQO8P2EmSWq3G/YCDwY6hGAuWAK+++bEsJnuVDfiK+wRgNMPmfzCiwgRMNCFEFJhsOS+FhiFT6DU6ZDcM21tbdrJorBH0nAiN0K/nLNgwQLdR+xGJfgGugSRWIQkoKUvuk6ZQ/SoSYR2jmi26jwn+Yju2LHjhp6ens8lSXeiDXSOGOxjpATQclqBOHowPu3Sl/hqamrOBKjnQPfqORkZ6JJJZpLHj1j62o5RuCWFSGQk/hTju18CuLW9vb3Pp+C1Bf9UwpmsaRqX8d3Pg5ylL/yD0XxxfM+JB812GIbiz9AVbyThVkibOO9g7Jdy97JpOiRlbkgCAGhJR0eHdpGcPRQbqUCLPQLA3JWI/fD0EadNmjeg3bQiIfZmjMl6V+GkZKBLKprJmcBM9E5WHI6FYlA0Q6rRvcTpJJcv0KWrl6VPYAkY6CZw4xbqVzPQFWrLTOB6GegmcOMW6lcz0BVqy0zgehnoJnDjFupXM9AVasukq9c4TjfQjePGG69V/z8AAAD//9q7e3kAAAAGSURBVAMA2DjnhmoeDQ4AAAAASUVORK5CYII=\" width=\"78.5\" height=\"56\" style=\"width: 78.5px; height: 56px;\"\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e3. Exponential Entropy Tax:\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 Each jump increases the Void's entropy. The cost of the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-th jump is:  \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: 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: 21.0333px; 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 10.5167px; text-align: left; transform-origin: 444.5px 10.5167px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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\" width=\"272\" height=\"19.5\" style=\"width: 272px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the step number ( starting from 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; 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: 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: 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e4. Quantum Spin Exclusion:\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 If \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"44\" height=\"18\" style=\"width: 44px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, particles must maintain a \"Spin Buffer\". If two particles have the same Spin \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eω\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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 at any node, an additional \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; \"\u003e50 ATP\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 is consumed due to interference.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.4667px; 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 21.2333px; text-align: left; transform-origin: 444.5px 21.2333px; 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=\"font-weight: 700; \"\u003e5. Predator Intercept:\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 The Predator moves at a constant speed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"52\" height=\"20\" style=\"width: 52px; height: 20px;\"\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. It starts at \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; \"\u003epred_start\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 and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e-1 ( Swarm Annihilated )\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [max_energy,best_path] = solve_exodus_singularity(nodes,pred_start,params)\r\n  [max_energy,best_path]=size(nodes);\r\nend","test_suite":"%% PHASE 1: 5D HYPER-TORUS GEOMETRY (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 1000; p.V_base = 50;\r\nbase_node.mass = 0; base_node.res = 1;\r\nn = repmat(base_node, 1, 2);\r\nn(1).coord = [0,0,0,0,0];\r\n\r\n% Test 1-5: Quấn tọa độ Torus\r\nn(2).coord = [90,0,0,0,0]; [e1,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e1 - 990) \u003c 1e-4);\r\nn(2).coord = [0,90,0,0,0]; [e2,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e2 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,90,0,0]; [e3,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e3 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,0,90,0]; [e4,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e4 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,0,0,90]; [e5,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e5 - 990) \u003c 1e-4);\r\n\r\n%% PHASE 2: RELATIVISTIC DRAG \u0026 PREDATOR (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 1000; p.V_base = 10;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 3);\r\nn(1).coord = [0,0,0,0,0]; n(2).coord = [40,0,0,0,0]; \r\nn(3).coord = [42,0,0,0,0]; \r\n\r\n% Test 6-7: Predator Interception\r\n[e6,~] = solve_exodus_singularity(n, 3, p);\r\nassert(e6 == -1);\r\np.V_base = 1000; [e7,~] = solve_exodus_singularity(n, 3, p);\r\nassert(e7 \u003e 0);\r\n\r\n% Test 8-10: Path and Format check \r\np.V_base = 50; [e8, s8] = solve_exodus_singularity(n(1:2), 0, p);\r\nassert(abs(e8 - 960) \u003c 1e-4);\r\nassert(iscell(s8));\r\nassert(s8{1}(end) == 2); % So sánh numeric == numeric\r\n\r\n%% PHASE 3: EXPONENTIAL ENTROPY TAX (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 3; p.ATP_total = 1000; p.V_base = 50;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 3);\r\nn(1).coord = [0,0,0,0,0]; n(2).coord = [10,0,0,0,0]; \r\nn(3).coord = [20,0,0,0,0]; n(3).res = 5;\r\n\r\n% Test 11-12: Forced Path 1-\u003e2-\u003e3 (Cost = 10 + 57.5 = 67.5)\r\n[e_tax, s_tax] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e_tax - 932.5) \u003c 1e-4);\r\nassert(length(s_tax{1}) == 3);\r\n\r\n% Test 13-15: Basic Tax check\r\nn(3).res = 1; [e_basic, ~] = solve_exodus_singularity(n, 0, p);\r\nassert(e_basic == 980);\r\nassert(true); assert(true);\r\n\r\n%% PHASE 4: QUANTUM SPIN \u0026 COMPLEXITY (5 Checks)\r\nclear n p;\r\np.M = 2; p.target = 2; p.ATP_total = 1000; p.V_base = 50;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 2);\r\nn(1).coord = [0,0,0,0,0.5]; n(2).coord = [10,0,0,0,1.0]; % Spin Penalty\r\n\r\n% Test 16-17: Spin Penalty \r\n[e16,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e16 - 927.5) \u003c 1e-4);\r\nn(2).coord(5) = 1.1; [e17,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e17 - 977.5) \u003c 1e-4);\r\n\r\n% Test 18-20: Stress \u0026 Finalization\r\nrng(2026); for i=1:5, n_rnd(i).coord = rand(1,5)*100; n_rnd(i).mass=0; n_rnd(i).res=1; end\r\np_r.M = 1; p_r.target = 5; p_r.ATP_total = 1000; p_r.V_base = 50;\r\n[er, sr] = solve_exodus_singularity(n_rnd, 0, p_r);\r\nassert(er \u003e 0);\r\nassert(iscell(sr));\r\nassert(length(sr) == 1);","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":4945722,"edited_by":4945722,"edited_at":"2026-03-22T14:34:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-22T14:30:22.000Z","updated_at":"2026-03-22T19:52:48.000Z","published_at":"2026-03-22T14:30:22.000Z","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:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eBackground\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\u003eThe Swarm has reached the edge of the known universe, entering the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e5th Dimension (The Void)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Here, Euclidean geometry is replaced by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHyper-Torus Topology\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and energy is taxed by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eUniversal Entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. To reach the final gate, you must navigate a swarm 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\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eparticles through a 5D coordinate system: \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\\\\left[x,y,z,\\\\psi,\\\\omega\\\\right], \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e where \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\\\\psi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis the Phase Shift 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\\\\omega\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the Spin Frequency.\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\u003eObjective\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\u003eWrite a function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that calculates the maximum remaining energy and the optimal path for a swarm.\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 Laws of the Void:\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\u003e1. 5D Hyper-Torus Distance:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e All dimensions have a period \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\u003eL = 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\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\u003e2. Relativistic Drag\u003c/w:t\u003e\u003c/w:r\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\u003eV_{eff\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e): The swarm's velocity is not constant. It decays vased on the current energy \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\u003eand the node's mass \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\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\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\u003eV_{eff} = \\\\frac{V_{base}}{1+\\\\frac{m}{E}\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:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3. Exponential Entropy Tax:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Each jump increases the Void's entropy. The cost of the \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-th jump is:  \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: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\u003eCost = (Dist \\\\times Saturation \\\\times Res) \\\\times 1.15^{(n-1)}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, where \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 is the step number ( starting from 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\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\u003e4. Quantum Spin Exclusion:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 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\u003eM \u0026gt; 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, particles must maintain a \\\"Spin Buffer\\\". If two particles have the same Spin \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\\\\omega\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at any node, an additional \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e50 ATP\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is consumed due to interference.\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\u003e5. Predator Intercept:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The Predator moves at a constant speed \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\u003eV_{p} = 15\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. It starts at \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epred_start\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1 ( Swarm Annihilated )\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":502,"title":"Would Homer Like It?","description":"\r\nGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a 4-connected region of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or genus 0) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\r\nReturn true if any doughnuts are present, otherwise false.\r\nExamples:\r\nThis is a doughnut:\r\n 1 1 1\r\n 1 0 1 \r\n 1 1 1\r\nHere is another doughnut:\r\n 0 0 0 1 1 1\r\n 0 1 1 1 0 1\r\n 0 0 1 0 0 1\r\n 0 0 1 1 1 1\r\nThis is not a doughnut:\r\n 0 1 0\r\n 1 0 1\r\n 0 1 0\r\nbecause the ones are not 4-connected and so can't surround anything.","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: 655.833px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 327.917px; transform-origin: 407px 327.917px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 145.5px; font-family: Helvetica, Arial, sans-serif; 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\" data-image-state=\"image-loaded\"\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: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e4-connected region\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: 261.5px 8px; transform-origin: 261.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or\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\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003egenus 0\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: 231px 8px; transform-origin: 231px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\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: 182px 8px; transform-origin: 182px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn true if any doughnuts are present, otherwise false.\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=\"\"\u003eExamples:\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: 60.5px 8px; transform-origin: 60.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is a doughnut:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; 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 30.65px; transform-origin: 404px 30.65px; 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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 1\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: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 \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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 1\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: 82.5px 8px; transform-origin: 82.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere is another doughnut:\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: 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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 0 1 1 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 1 0 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 1 0 0 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 1 1 1 1\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: 72.5px 8px; transform-origin: 72.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is not a doughnut:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; 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 30.65px; transform-origin: 404px 30.65px; 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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 0\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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1\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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 0\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: 224.5px 8px; transform-origin: 224.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebecause the ones are not 4-connected and so can't surround anything.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function tf = has_doughnut(a)\r\n  tf = true;\r\nend","test_suite":"%%\r\na = [ ...\r\n 1 1 1;\r\n 1 0 1;\r\n 1 1 1;\r\n];\r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 1 0 0;\r\n 1 0 0;\r\n 1 1 0;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%% \r\na = [ ...\r\n 1 1 1 0;\r\n 1 0 1 0;\r\n 1 1 1 0;\r\n 0 0 0 0;\r\n]; \r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 0 0 0;\r\n 0 1 1 0;\r\n 0 1 1 0;\r\n 0 0 0 0;\r\n];\r\ntf = false\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 0 1 0;\r\n 0 1 0 1 1;\r\n 0 1 0 0 1;\r\n 0 1 1 1 1;\r\n 0 0 0 0 0;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 1 1 0;\r\n 0 1 0 1 1;\r\n 0 1 0 0 1;\r\n 0 1 1 1 1;\r\n 0 0 0 0 0;\r\n];\r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 0 1;\r\n 1 0 1 0;\r\n 0 1 0 1;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = zeros(3,4);\r\na(1,3)=1;\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))","published":true,"deleted":false,"likes_count":5,"comments_count":4,"created_by":7,"edited_by":223089,"edited_at":"2022-10-28T10:25:50.000Z","deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2022-10-28T10:25:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-15T17:46:33.000Z","updated_at":"2026-01-22T13:03:28.000Z","published_at":"2012-03-19T15:21:37.000Z","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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"140\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"200\\\"/\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\u003eGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e4-connected region\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or\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=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003egenus 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\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\u003eReturn true if any doughnuts are present, otherwise false.\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\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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a doughnut:\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 1 1\\n 1 0 1 \\n 1 1 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere is another doughnut:\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[ 0 0 0 1 1 1\\n 0 1 1 1 0 1\\n 0 0 1 0 0 1\\n 0 0 1 1 1 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is not a doughnut:\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[ 0 1 0\\n 1 0 1\\n 0 1 0]]\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\u003ebecause the ones are not 4-connected and so can't surround 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42765,"title":"Maximize Non-Co-Planar Points in an N-Cube","description":"This Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\r\n\r\n  N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\n  N=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\r\nOutput is a Qx3 matrix of the non-co-planar points.\r\n\r\nReference: The \u003chttp://68.173.157.131/Contest/Tetrahedra March 2016 Al Zimmermann Non-Coplanar contest\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\r\n\r\nTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar Cody Co-Planar Check\u003e may improve speed. ","description_html":"\u003cp\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\u003c/pre\u003e\u003cp\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/p\u003e\u003cp\u003eReference: The \u003ca href = \"http://68.173.157.131/Contest/Tetrahedra\"\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/a\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/p\u003e\u003cp\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\"\u003eCody Co-Planar Check\u003c/a\u003e may improve speed.\u003c/p\u003e","function_template":"function m=MaxNonCoplanarPts(N,Q);\r\n% Place Q or more points in an 0:N-1 #D grid such that each plane created uses only 3 points from the set provided\r\n% N is Cube size\r\n% Q is expected number of points in solution\r\n% Hint: Point [0,0,0] can be assumed to be in the solution for N=2 and N=3\r\n% N=3 is the 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n  m=[];\r\nend","test_suite":"%%\r\nN=2;\r\nQ=5;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n%%\r\nN=3;\r\nQ=8;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\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":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-07T00:22:02.000Z","updated_at":"2016-03-07T01:01:33.000Z","published_at":"2016-03-07T01:01:33.000Z","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\",\"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 find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar. Given the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\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[N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]]]\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput is a Qx3 matrix of the non-co-planar points.\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\u003eReference: The\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://68.173.157.131/Contest/Tetrahedra\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\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\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving\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/42762-is-3d-point-set-co-planar\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Co-Planar Check\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may improve speed.\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":52839,"title":"Easy Sequences 33: Web Trapped Ant","description":"An ant is trapped on a spider web inside a can with open top. The can has a radius  and height . A spider sitting on the outside face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure below.\r\n                         \r\n is the initial distance of the spider from the from the bottom of the can,  is the distance of the ant from the top of the can, and  and , are the y-offsets of the spider and ant, respectively, from the center of the circular top.\r\nIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. Please round-off your answer to the nearest four decimal places.\r\nCLARIFICATIONS: \r\nIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\r\nThe dotted lines is not the spider's path. They are used only to project the heights to the side.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440000534057617px; 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: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 609px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; vertical-align: baseline; \"\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; text-align: left; 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; \"\u003e\u003cspan style=\"\"\u003eAn \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eant is\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; \"\u003e\u003cspan style=\"\"\u003e trapped on a spider web \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003einside\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; \"\u003e\u003cspan style=\"\"\u003e a can with open top. The can has a radius \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003eR\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e and height \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003eH\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e. A \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003espider sitting\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; \"\u003e\u003cspan style=\"\"\u003e on the \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; \"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003eoutside\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; \"\u003e\u003cspan style=\"\"\u003e face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 315px; 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; text-align: left; 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; \"\u003e\u003cspan style=\"\"\u003e                         \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"551\" height=\"309\" style=\"vertical-align: baseline;width: 551px;height: 309px\" 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src=\"data:image/png;base64,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\" width=\"18\" height=\"18\" style=\"width: 18px; 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; \"\u003e\u003cspan style=\"\"\u003e is the distance of the ant from the top of the can, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"16.5\" height=\"18\" style=\"width: 16.5px; 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; \"\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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\" width=\"18\" height=\"18\" style=\"width: 18px; 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; \"\u003e\u003cspan style=\"\"\u003e, are the y-offsets of the spider and ant, respectively, from the center of the circular top.\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; text-align: left; 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; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003eIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. \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; \"\u003e\u003cspan style=\"\"\u003ePlease round-off your answer to the nearest four 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; text-align: left; 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; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003eCLARIFICATIONS: \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 80px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 60px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; text-align: left; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; \"\u003e\u003cspan style=\"\"\u003eIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; text-align: left; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; \"\u003e\u003cspan style=\"\"\u003eThe dotted lines is not the spider's path. They are used only to project the heights to the side.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function s = spiderPath(H,R,hs,ha,xs,xa)\r\n    y = x;\r\nend","test_suite":"%%\r\nH = 10; R = 5; hs = 1; ha = 1; xs = 1; xa = 1;\r\ns_correct = 18.6210;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 5; R = 2; hs = 0.3; ha = 0.4; xs = 0.5; xa = 0.6;\r\ns_correct = 8.0120;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 15.5; R = 5.5; hs = 3; ha = 2.5; xs = 1.5; xa = 1;\r\ns_correct = 22.4960;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = 123; R = 123; hs = 12; ha = 25; xs = 30; xa = 40;\r\ns_correct = 399.8218;\r\nassert(isequal(spiderPath(H,R,hs,ha,xs,xa),s_correct))\r\n%%\r\nH = linspace(5,150,50); R = linspace(5,120,50); \r\nhs = linspace(0.5,10,50); ha = linspace(0.5,20,100); \r\nxs = linspace(0.4,12,50); xa = linspace(0.4,10,50);\r\ns = sum(arrayfun(@(i) spiderPath(H(i),R(i),hs(i),ha(i),xs(i),xa(i)),1:50));\r\ns_correct = 10509.4142;\r\nassert(isequal(s,s_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":8,"created_by":255988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2021-10-02T18:43:11.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2021-09-30T07:18:49.000Z","updated_at":"2026-02-07T13:53:16.000Z","published_at":"2021-09-30T08:12:11.000Z","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\u003eAn \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eant is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e trapped on a spider web \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003einside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e a can with open top. The can has a radius \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\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and height \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\u003eH\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. A \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003espider sitting\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e on the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eoutside\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e face of the can, sensed the trapped ant and started crawling towards it. The relative positions of the ant and spider are shown in the figure 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=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"309\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"551\\\"/\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: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\u003ehs\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the initial distance of the spider from the from the bottom of the can, \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\u003eha\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the distance of the ant from the top of the can, 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\u003exs\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\u003exa\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, are the y-offsets of the spider and ant, respectively, from the center of the circular top.\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\u003eIf the spider moves at a constant crawling speed, calculate the total length of the path to reach the ant at the quickest possible time. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ePlease round-off your answer to the nearest four 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:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCLARIFICATIONS: \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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is understood there can be 2 possible positions, given a single y-offset. In this exercise please consider only the position as drawn above. That means at the top view, the ant and the spidert shall only be within the first and third quadrant, respectively.\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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe dotted lines is not the spider's path. They are used only to project the heights to the 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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":52427,"title":"ICFP2021 Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","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. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*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_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\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.\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: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; 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: 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://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: 148.5px 8px; transform-origin: 148.5px 8px; 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: 30.5px 8px; transform-origin: 30.5px 8px; 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: 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: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; 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 84px; text-align: left; transform-origin: 384px 84px; 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: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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: 332px 8px; transform-origin: 332px 8px; 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\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: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*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: 253px 8px; transform-origin: 253px 8px; 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: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(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: 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: 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: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\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: 379.5px 8px; transform-origin: 379.5px 8px; 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: 43.5px 8px; transform-origin: 43.5px 8px; 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: 259px 8px; transform-origin: 259px 8px; 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\u003cdiv style=\"block-size: 358px; 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 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\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% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' 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%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\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 %write_submission_bonus\r\n% \r\n% \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","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(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\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\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":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","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://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. The starting node/seg map to show guesses. \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 ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled 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. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 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\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*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_ICFP004(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. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\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, write ICFP solution files using Bonuses, and plots.\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 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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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- THE VOID SINGULARITY ( PHASE OMEGA-ZERO )","description":"Background:\r\nThe Swarm has reached the edge of the known universe, entering the 5th Dimension (The Void). Here, Euclidean geometry is replaced by Hyper-Torus Topology, and energy is taxed by Universal Entropy. To reach the final gate, you must navigate a swarm of particles through a 5D coordinate system:  where is the Phase Shift and  is the Spin Frequency.\r\nObjective:\r\nWrite a function [max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params) that calculates the maximum remaining energy and the optimal path for a swarm.\r\nThe Laws of the Void:\r\n1. 5D Hyper-Torus Distance: All dimensions have a period . The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\r\n2. Relativistic Drag (): The swarm's velocity is not constant. It decays vased on the current energy and the node's mass : \r\n                                                    \r\n3. Exponential Entropy Tax: Each jump increases the Void's entropy. The cost of the -th jump is:  \r\n\r\n, where  is the step number ( starting from 1 ).\r\n\r\n4. Quantum Spin Exclusion: If , particles must maintain a \"Spin Buffer\". If two particles have the same Spin  at any node, an additional 50 ATP is consumed due to interference.\r\n5. Predator Intercept: The Predator moves at a constant speed . It starts at pred_start and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced to -1 ( Swarm Annihilated )","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: 572.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 286.433px; transform-origin: 468.5px 286.433px; 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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eBackground\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:\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; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 31.5px; text-align: left; transform-origin: 444.5px 31.5px; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Swarm has reached the edge of the known universe, entering the \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; \"\u003e5th Dimension (The Void)\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. Here, Euclidean geometry is replaced 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHyper-Torus Topology\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, and energy is taxed 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eUniversal Entropy\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. To reach the final gate, you must navigate a swarm of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eM\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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=\"\"\u003eparticles through a 5D coordinate system: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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width=\"83\" height=\"18\" style=\"width: 83px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eψ\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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=\"\"\u003eis the Phase Shift and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eω\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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 is the Spin Frequency.\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eObjective\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:\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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e[max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params)\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 that calculates the maximum remaining energy and the optimal path for a swarm.\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Laws of the Void:\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; 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e1. 5D Hyper-Torus Distance:\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 All dimensions have a period \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"54\" height=\"18\" style=\"width: 54px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.4667px; 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 10.7333px; text-align: left; transform-origin: 444.5px 10.7333px; 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=\"font-weight: 700; \"\u003e2. Relativistic Drag\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 (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"24.5\" height=\"20\" style=\"width: 24.5px; height: 20px;\"\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): The swarm's velocity is not constant. It decays vased on the current energy \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand the node's mass \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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: 55.9px; 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 27.95px; text-align: left; transform-origin: 444.5px 27.95px; 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=\"\"\u003e                                                    \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-33px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"78.5\" height=\"56\" style=\"width: 78.5px; height: 56px;\"\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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e3. Exponential Entropy Tax:\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 Each jump increases the Void's entropy. The cost of the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e-th jump is:  \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: 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: 21.0333px; 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 10.5167px; text-align: left; transform-origin: 444.5px 10.5167px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg 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\" width=\"272\" height=\"19.5\" style=\"width: 272px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is the step number ( starting from 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; 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: 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: 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e4. Quantum Spin Exclusion:\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 If \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFgAAAAkCAYAAAANdf2OAAAGWUlEQVR4AeyZaWhcVRTH3yxJmolG9DUJptkmCYkV6tZqixtuVBSKLYJW0NIqfvBDcQMpofpBtFAXDBUKKs0XERoUW2kLUhWRlpCmplqlaZ2mySQmdoklGpPYTJbp7zzmDm+GWZN5MxN44fznru+8c//v3PPOfXFq9p+lDNgEW0qvptkE2wRbzIDF6m0Ptgm2mAGL1dsenGuC6+rqVnu93s/BKAiasC5F29zo2G26Lkh7COwoKyu7JkUdeTOtqqqquL6+/nnW01VbW3tjMsOSerDf7z/W39//HIo2gbA4HI7GcCNBBUMeZ+5m0xQf9XvR2zIyMjJOfVGIItbtdp8NBoN7wA2pGJ6UYKUEhZWqLiXtYikTobGxsYrxd4F57mHIHaBv0QieuqGgoGAvBjfjLB7KlCVVgt0ofjhKa1NUO7rpnpmZaaGzGcwCESm/oxIEi0UcJSUlh9jFT/T19W3D6EMgZUmJYLZHBRpvB2eBkEShVVZUVJRIJRZCoUHCyn7GrwCRS3Nzc6JD6mmBuPcacXs33pQ07qWlOPnkYE9PTyA0TRxDrT/UlbhIieDCwsLlqFkGvgRjQKS0qKioUCrRMIWGo4xJzDUeBGHl9OTk5J/0pS08GNlFLzmdzkGIboNob9pKcnBBSgRDzIPY9jflYcp/gEZ9KWFjidSjoEJDOaS8zditwBDmd873xabr+kdc/yz3vUi5BaJ97JKvgIQgQ38+/iQlWFIpFrUG40+yMD/lNNCoXwckdEgzDBYsWcMmxnZy3QUGFMEB+n6kPS/p7u6eJgZ+AdFe9BhEo+hJcIrwsY/75iXRSQn2eDzVLGg5ZB0ZHx+/TDnEokSK8aKIPNYUGjqYsIeUpp5SPYTBQCBwmvaCJAbRF7BpPUpPQfK34BbqDpAXkpRgl8t1B5aWOp3ODrb3BGT/RVukhIVdL5UQwqGB9na87V/GJbSoOP3L0NDQRcYyImaiuc9GILvrUZSfgOQjxOnV1HNOdDKCHcRRSc/O4Y1nMFjeoiojkDARPmywKCM0MOcDUprjptBClyYx+wcqMyCjIkSTV7frui67TBF9D47QiU0duSY6IcFNTU06hq6Ekd98Pt9lSiEqnGbhNUulzxQajjP/U/qCKrRQFxllJ/wsFasQg+hz3GsN9gjRXQ0NDQ/RdoGsSkKCOSjchDUNECnplnivRqjopU+JpG4qNCxj3jYJDTLocrkktKic1Tc9Pd0n/VZDEc3OWwu5J0L3W0X7IN68NdTOWpGQYIy6G0sCkBX2vtnZ2f/pU8l2JW/wp1jIZvAeW7WLMUMg+wGjwg/1nsHBQSO9o2mp4KnVENmGzX9wX3nI/3HD93nBVmNfK/WsSlyCMXIJpN2HNRHeRyy+RN8YEGlmETupnJyamvqM0vDympoaefmtoG2I0+mU+GuMGR0W/MjBA5vbcIo+7N7CLeRD0psQXcU74Y3h4WEjxNGfVYlLMIbKxx3JYY/hfaPKKoyXTEB5Yw39pSBiAXwYkfRMfas4j9errcrUzIoilofowzYhdpSH/jIpZTXEvtPb26ucIbM3TlFbXIIx+C50VGL0T5RhgSw5aAhU3y4WclQ1pGTOKkrxYnkpzvt4jI64EotYbH2BWF/r9/t3kVKKB8e9PlsD8Qh2Y4B8UB/D6IhPi3jGBB6iDhudxLZW5pq3v3wzkNSObk1SuXkfj7UYf9HEYoscnQ1iecG2kWvLOyLGlbnpikkwsWwlxD6GSYUswPwtV8Mz1GFDFvJWdGzjJXM/10nCT2HIhPG7wB/0VpPX7mVnGaEAuwbARl3XvdkilrRV0tKbZSnwcy3vI13qiRBBsBwOIHcHFx/gItniclr7moV9jOeolEu89QqL+4TQ8D3zDGHOOjKKfcTug3RIXKYwZLv0o/dpWrIzKNIXws4zXCU6znDv9X6/vxm0S1pGv6WC/StAK7v1V250JxApx6b9rPtDxh6hI+baIgjGO8cxugXiyoEjBKlvHRgYOI8SQ+h/kXmv0hCyKTSNvgN40gZKD1DXSumRfua3M3EhJzkJVWvRfRu6vkGXShWpWivY/zt4hftKRiJrMkC7EXteZ0wcLebaIgjW8viPxbSzGPlvSNaIzQQdi4bgTCw2Fzpsgi1m3SbYJthiBixWb3uwTbDFDCRSn4Ex24MzQGIiFVcBAAD//2R8RA0AAAAGSURBVAMAQM/SZ9ddsUgAAAAASUVORK5CYII=\" width=\"44\" height=\"18\" style=\"width: 44px; 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; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, particles must maintain a \"Spin Buffer\". If two particles have the same Spin \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(33, 33, 33);\"\u003eω\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-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 at any node, an additional \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; \"\u003e50 ATP\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 is consumed due to interference.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.4667px; 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 21.2333px; text-align: left; transform-origin: 444.5px 21.2333px; 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=\"font-weight: 700; \"\u003e5. Predator Intercept:\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 The Predator moves at a constant speed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"52\" height=\"20\" style=\"width: 52px; height: 20px;\"\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. It starts at \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; \"\u003epred_start\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 and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced 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: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003e-1 ( Swarm Annihilated )\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [max_energy,best_path] = solve_exodus_singularity(nodes,pred_start,params)\r\n  [max_energy,best_path]=size(nodes);\r\nend","test_suite":"%% PHASE 1: 5D HYPER-TORUS GEOMETRY (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 1000; p.V_base = 50;\r\nbase_node.mass = 0; base_node.res = 1;\r\nn = repmat(base_node, 1, 2);\r\nn(1).coord = [0,0,0,0,0];\r\n\r\n% Test 1-5: Quấn tọa độ Torus\r\nn(2).coord = [90,0,0,0,0]; [e1,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e1 - 990) \u003c 1e-4);\r\nn(2).coord = [0,90,0,0,0]; [e2,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e2 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,90,0,0]; [e3,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e3 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,0,90,0]; [e4,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e4 - 990) \u003c 1e-4);\r\nn(2).coord = [0,0,0,0,90]; [e5,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e5 - 990) \u003c 1e-4);\r\n\r\n%% PHASE 2: RELATIVISTIC DRAG \u0026 PREDATOR (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 1000; p.V_base = 10;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 3);\r\nn(1).coord = [0,0,0,0,0]; n(2).coord = [40,0,0,0,0]; \r\nn(3).coord = [42,0,0,0,0]; \r\n\r\n% Test 6-7: Predator Interception\r\n[e6,~] = solve_exodus_singularity(n, 3, p);\r\nassert(e6 == -1);\r\np.V_base = 1000; [e7,~] = solve_exodus_singularity(n, 3, p);\r\nassert(e7 \u003e 0);\r\n\r\n% Test 8-10: Path and Format check \r\np.V_base = 50; [e8, s8] = solve_exodus_singularity(n(1:2), 0, p);\r\nassert(abs(e8 - 960) \u003c 1e-4);\r\nassert(iscell(s8));\r\nassert(s8{1}(end) == 2); % So sánh numeric == numeric\r\n\r\n%% PHASE 3: EXPONENTIAL ENTROPY TAX (5 Checks)\r\nclear n p;\r\np.M = 1; p.target = 3; p.ATP_total = 1000; p.V_base = 50;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 3);\r\nn(1).coord = [0,0,0,0,0]; n(2).coord = [10,0,0,0,0]; \r\nn(3).coord = [20,0,0,0,0]; n(3).res = 5;\r\n\r\n% Test 11-12: Forced Path 1-\u003e2-\u003e3 (Cost = 10 + 57.5 = 67.5)\r\n[e_tax, s_tax] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e_tax - 932.5) \u003c 1e-4);\r\nassert(length(s_tax{1}) == 3);\r\n\r\n% Test 13-15: Basic Tax check\r\nn(3).res = 1; [e_basic, ~] = solve_exodus_singularity(n, 0, p);\r\nassert(e_basic == 980);\r\nassert(true); assert(true);\r\n\r\n%% PHASE 4: QUANTUM SPIN \u0026 COMPLEXITY (5 Checks)\r\nclear n p;\r\np.M = 2; p.target = 2; p.ATP_total = 1000; p.V_base = 50;\r\nb.mass = 0; b.res = 1; n = repmat(b, 1, 2);\r\nn(1).coord = [0,0,0,0,0.5]; n(2).coord = [10,0,0,0,1.0]; % Spin Penalty\r\n\r\n% Test 16-17: Spin Penalty \r\n[e16,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e16 - 927.5) \u003c 1e-4);\r\nn(2).coord(5) = 1.1; [e17,~] = solve_exodus_singularity(n, 0, p);\r\nassert(abs(e17 - 977.5) \u003c 1e-4);\r\n\r\n% Test 18-20: Stress \u0026 Finalization\r\nrng(2026); for i=1:5, n_rnd(i).coord = rand(1,5)*100; n_rnd(i).mass=0; n_rnd(i).res=1; end\r\np_r.M = 1; p_r.target = 5; p_r.ATP_total = 1000; p_r.V_base = 50;\r\n[er, sr] = solve_exodus_singularity(n_rnd, 0, p_r);\r\nassert(er \u003e 0);\r\nassert(iscell(sr));\r\nassert(length(sr) == 1);","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":4945722,"edited_by":4945722,"edited_at":"2026-03-22T14:34:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":2,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-22T14:30:22.000Z","updated_at":"2026-03-22T19:52:48.000Z","published_at":"2026-03-22T14:30:22.000Z","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:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eBackground\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\u003eThe Swarm has reached the edge of the known universe, entering the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e5th Dimension (The Void)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Here, Euclidean geometry is replaced by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHyper-Torus Topology\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and energy is taxed by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eUniversal Entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. To reach the final gate, you must navigate a swarm 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\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eparticles through a 5D coordinate system: \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\\\\left[x,y,z,\\\\psi,\\\\omega\\\\right], \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e where \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\\\\psi\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis the Phase Shift 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\\\\omega\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is the Spin Frequency.\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\u003eObjective\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\u003eWrite a function \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e[max_energy, best_path] = solve_exodus_singularity(nodes, pred_start, params)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that calculates the maximum remaining energy and the optimal path for a swarm.\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 Laws of the Void:\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\u003e1. 5D Hyper-Torus Distance:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e All dimensions have a period \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\u003eL = 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The distance between two points is the L-inf (Chebyshev) distance across the 5-dimensional Torus.\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\u003e2. Relativistic Drag\u003c/w:t\u003e\u003c/w:r\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\u003eV_{eff\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e): The swarm's velocity is not constant. It decays vased on the current energy \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\u003eand the node's mass \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\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\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\u003eV_{eff} = \\\\frac{V_{base}}{1+\\\\frac{m}{E}\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:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e3. Exponential Entropy Tax:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Each jump increases the Void's entropy. The cost of the \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-th jump is:  \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: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\u003eCost = (Dist \\\\times Saturation \\\\times Res) \\\\times 1.15^{(n-1)}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, where \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 is the step number ( starting from 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\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\u003e4. Quantum Spin Exclusion:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 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\u003eM \u0026gt; 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, particles must maintain a \\\"Spin Buffer\\\". If two particles have the same Spin \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\\\\omega\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at any node, an additional \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e50 ATP\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is consumed due to interference.\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\u003e5. Predator Intercept:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e The Predator moves at a constant speed \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\u003eV_{p} = 15\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. It starts at \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epred_start\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and moves directly toward the swarm's next intended node. If the Predator arrives before or at the same time as the swarm, the energy is reduced to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1 ( Swarm Annihilated )\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":502,"title":"Would Homer Like It?","description":"\r\nGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a 4-connected region of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or genus 0) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\r\nReturn true if any doughnuts are present, otherwise false.\r\nExamples:\r\nThis is a doughnut:\r\n 1 1 1\r\n 1 0 1 \r\n 1 1 1\r\nHere is another doughnut:\r\n 0 0 0 1 1 1\r\n 0 1 1 1 0 1\r\n 0 0 1 0 0 1\r\n 0 0 1 1 1 1\r\nThis is not a doughnut:\r\n 0 1 0\r\n 1 0 1\r\n 0 1 0\r\nbecause the ones are not 4-connected and so can't surround anything.","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: 655.833px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 327.917px; transform-origin: 407px 327.917px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 145.5px; font-family: Helvetica, Arial, sans-serif; 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\" data-image-state=\"image-loaded\"\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: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e4-connected region\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: 261.5px 8px; transform-origin: 261.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or\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\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003egenus 0\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: 231px 8px; transform-origin: 231px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\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: 182px 8px; transform-origin: 182px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn true if any doughnuts are present, otherwise false.\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=\"\"\u003eExamples:\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: 60.5px 8px; transform-origin: 60.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is a doughnut:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; 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 30.65px; transform-origin: 404px 30.65px; 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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 1\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: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1 \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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 1 1\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: 82.5px 8px; transform-origin: 82.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHere is another doughnut:\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: 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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 0 1 1 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 1 1 0 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 1 0 0 1\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: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 0 1 1 1 1\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: 72.5px 8px; transform-origin: 72.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis is not a doughnut:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; 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 30.65px; transform-origin: 404px 30.65px; 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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 0\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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 0 1\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: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 0 1 0\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: 224.5px 8px; transform-origin: 224.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebecause the ones are not 4-connected and so can't surround anything.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function tf = has_doughnut(a)\r\n  tf = true;\r\nend","test_suite":"%%\r\na = [ ...\r\n 1 1 1;\r\n 1 0 1;\r\n 1 1 1;\r\n];\r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 1 0 0;\r\n 1 0 0;\r\n 1 1 0;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%% \r\na = [ ...\r\n 1 1 1 0;\r\n 1 0 1 0;\r\n 1 1 1 0;\r\n 0 0 0 0;\r\n]; \r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 0 0 0;\r\n 0 1 1 0;\r\n 0 1 1 0;\r\n 0 0 0 0;\r\n];\r\ntf = false\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 0 1 0;\r\n 0 1 0 1 1;\r\n 0 1 0 0 1;\r\n 0 1 1 1 1;\r\n 0 0 0 0 0;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 1 1 0;\r\n 0 1 0 1 1;\r\n 0 1 0 0 1;\r\n 0 1 1 1 1;\r\n 0 0 0 0 0;\r\n];\r\ntf = true;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = [ ...\r\n 0 1 0 1;\r\n 1 0 1 0;\r\n 0 1 0 1;\r\n];\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))\r\n\r\n%%\r\na = zeros(3,4);\r\na(1,3)=1;\r\ntf = false;\r\nassert(isequal(has_doughnut(a),tf))","published":true,"deleted":false,"likes_count":5,"comments_count":4,"created_by":7,"edited_by":223089,"edited_at":"2022-10-28T10:25:50.000Z","deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":"2022-10-28T10:25:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-15T17:46:33.000Z","updated_at":"2026-01-22T13:03:28.000Z","published_at":"2012-03-19T15:21:37.000Z","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:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"140\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"200\\\"/\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\u003eGiven a matrix of ones and zeros, you must determine if there are any doughnuts present (would Homer Simpson like it?). A doughnut is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e4-connected region\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e of ones completely enclosing a 4-connected region of zeros. You can assume that any regions of ones are either blobs (or\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=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003egenus 0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e) or doughnuts (genus 1). Thus if you find any hole in a region of ones, it is a doughnut. Also, there are no nested doughnuts.\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\u003eReturn true if any doughnuts are present, otherwise false.\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\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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a doughnut:\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 1 1\\n 1 0 1 \\n 1 1 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere is another doughnut:\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[ 0 0 0 1 1 1\\n 0 1 1 1 0 1\\n 0 0 1 0 0 1\\n 0 0 1 1 1 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\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is not a doughnut:\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[ 0 1 0\\n 1 0 1\\n 0 1 0]]\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\u003ebecause the ones are not 4-connected and so can't surround 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42765,"title":"Maximize Non-Co-Planar Points in an N-Cube","description":"This Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\r\n\r\n  N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\n  N=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\r\nOutput is a Qx3 matrix of the non-co-planar points.\r\n\r\nReference: The \u003chttp://68.173.157.131/Contest/Tetrahedra March 2016 Al Zimmermann Non-Coplanar contest\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\r\n\r\nTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003chttp://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar Cody Co-Planar Check\u003e may improve speed. ","description_html":"\u003cp\u003eThis Challenge is to find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar.\r\nGiven the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eN=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\r\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n\u003c/pre\u003e\u003cp\u003eOutput is a Qx3 matrix of the non-co-planar points.\u003c/p\u003e\u003cp\u003eReference: The \u003ca href = \"http://68.173.157.131/Contest/Tetrahedra\"\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/a\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\u003c/p\u003e\u003cp\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/42762-is-3d-point-set-co-planar\"\u003eCody Co-Planar Check\u003c/a\u003e may improve speed.\u003c/p\u003e","function_template":"function m=MaxNonCoplanarPts(N,Q);\r\n% Place Q or more points in an 0:N-1 #D grid such that each plane created uses only 3 points from the set provided\r\n% N is Cube size\r\n% Q is expected number of points in solution\r\n% Hint: Point [0,0,0] can be assumed to be in the solution for N=2 and N=3\r\n% N=3 is the 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]\r\n  m=[];\r\nend","test_suite":"%%\r\nN=2;\r\nQ=5;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\r\n%%\r\nN=3;\r\nQ=8;\r\nm=MaxNonCoplanarPts(N,Q);\r\nm=floor(abs(m))\r\n% Perform m check\r\nvalid=1;\r\nif size(m,1)\u003cQ,valid=0;end % Must be Q pts or more\r\nif max(m(:))\u003eN-1,valid=0;end\r\npset=nchoosek(1:size(m,1),4);\r\nfor i=1:length(pset)\r\n m4=m(pset(i,:),:);\r\n% Coplanar check method courtesy of Tim\r\n if ~det([m4 ones(4,1)]) % coplanar det=0\r\n  valid=0;\r\n  break\r\n end\r\nend\r\nassert(isequal(1,valid))\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":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-07T00:22:02.000Z","updated_at":"2016-03-07T01:01:33.000Z","published_at":"2016-03-07T01:01:33.000Z","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\",\"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 find a set with the maximum number of integer points that create planar surfaces with a maximum of three points from the set. No four points may be co-planar. Given the size N and the number of expected points Q find a set of Q points. Only N=2/Q=5 and N=3/Q=8 will be tested. N=4/Q=10 or N=5/Q=13 are too large to process.\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[N=2 contains 8 points [0,0,0;0,1,0;1,0,0;1,1,0;0,0,1;0,1,1;1,0,1;1,1,1]\\nN=3 contains 27 points [0,0,0;0,0,1;0,0,2;...2,2,2]]]\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\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOutput is a Qx3 matrix of the non-co-planar points.\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\u003eReference: The\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://68.173.157.131/Contest/Tetrahedra\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMarch 2016 Al Zimmermann Non-Coplanar contest\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is N=primes less than 100. Maximize the number of points in an NxNxN cube with no 4 points in a common plane.\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\u003eTheory: The N=2 and N=3 cases can be processed by brute force if care is taken. Assumption of [0,0,0] greatly reduces number of cases. Solving\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/42762-is-3d-point-set-co-planar\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eCody Co-Planar Check\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e may improve speed.\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 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