test.m

% Boyd & Vandenberghe, "Convex Optimization"
% Joëlle Skaf - 08/16/05
% (a figure is generated)
%
% The goal is to find the largest Euclidean ball (i.e. its center and
% radius) that lies in a polyhedron described by linear inequalites in this
% fashion: P = {x : a_i'*x <= b_i, i=1,...,m} where x is in R^2

% Generate the input data
a1 = [ 2;  1];
a2 = [ 2; -1];
a3 = [-1;  2];
a4 = [-1; -2];
b = ones(4,1);

% Create and solve the model
cvx_begin
    variable r(1)
    variable x_c(2)
    maximize ( r )
    a1'*x_c + r*norm(a1,2) <= b(1);
    a2'*x_c + r*norm(a2,2) <= b(2);
    a3'*x_c + r*norm(a3,2) <= b(3);
    a4'*x_c + r*norm(a4,2) <= b(4);
cvx_end

% Generate the figure
x = linspace(-2,2);
theta = 0:pi/100:2*pi;
plot( x, -x*a1(1)./a1(2) + b(1)./a1(2),'b-');
hold on
plot( x, -x*a2(1)./a2(2) + b(2)./a2(2),'b-');
plot( x, -x*a3(1)./a3(2) + b(3)./a3(2),'b-');
plot( x, -x*a4(1)./a4(2) + b(4)./a4(2),'b-');
plot( x_c(1) + r*cos(theta), x_c(2) + r*sin(theta), 'r');
plot(x_c(1),x_c(2),'k+')
xlabel('x_1')
ylabel('x_2')
title('Largest Euclidean ball lying in a 2D polyhedron');
axis([-1 1 -1 1])
axis equal