Hello,
I want the solution (x,y) of my optimization problem to belong to a specific region of the search-space as depicted below. This region can have an arbitrary shape.
1st case: The red region is known, by knowing the equation of each red line (segment). Is it possible to create a set of inequalities?
2nd case: The red region is known, by knowing the coordinates of all points of its perimeter. Is it possible to create a set of inequalities?
Thanks in advance.

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Matt J
Matt J am 22 Dez. 2020
Bearbeitet: Matt J am 22 Dez. 2020

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Assuming the region is convex, the answer is yes in both cases. Otherwise, the answer is no - inequalitites will not be enough to specify a nonconvex polygon.

4 Kommentare

Alan Weiss
Alan Weiss am 22 Dez. 2020
To expand on Matt's answer, for convex regions you would generally use a set of linear inequality constraints.
Alan Weiss
MATLAB mathematical toolbox documentation
John Kioulaxidis
John Kioulaxidis am 22 Dez. 2020
Bearbeitet: John Kioulaxidis am 22 Dez. 2020
Thank both of you for the answers. Indeed, let's assume it is a convex region.
1st case: The (line) equations of the sides of the region are known. I guess it is easy then to procceed according to linear inequality constraints
2nd case: To better explain, let's assume that my convex region is in fact a grid of points with known coordinates. I dont know which points define the boundaries of the region or any any line equations. How is it possible to define this region by linear inequalities?
For the second case, you can use vert2lconin
[A,b]=vert2lcon(V);
Although the instructions assume the rows of V are the vertices of the polygon, it will actually work if there are additional points as well.
John Kioulaxidis
John Kioulaxidis am 23 Dez. 2020
Wow this function is what I needed. Thanks

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Bruno Luong
Bruno Luong am 22 Dez. 2020
Bearbeitet: Bruno Luong am 22 Dez. 2020

0 Stimmen

% Your points that define the domain
xg = randn(10,1);
yg = randn(10,1);
% The domain is concerted to the set defined by inequalities { xy : A*xy <= b }
xyg = [xg(:) yg(:)];
K = convhull(xyg);
xyh = xyg(K,:);
dxy = diff(xyh,1,1);
A = [dxy(:,2),-dxy(:,1)];
b = sum(A.*xyh(1:end-1,:),2);
% Check with grid points
close all
xi = linspace(min(xg),max(xg),30);
yi = linspace(min(yg),max(yg),30);
[X,Y] = ndgrid(xi,yi);
xy = [X(:),Y(:)]';
in = all(A*xy <= b,1); % inequalities
plot(xg,yg,'+',...
xyh(:,1),xyh(:,2),'-k',...
X(in),Y(in),'or', ...
X(~in),Y(~in),'.y');

1 Kommentar

John Kioulaxidis
John Kioulaxidis am 23 Dez. 2020
Thank you for the answer. Function convhull seems quite interesting..

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