ex_5_33.m

来自「斯坦福大学Grant和Boyd教授等开发的凸优化matlab工具箱」· M 代码 · 共 42 行

% Exercise 5.33: Parametrized l1-norm approximation
% Boyd & Vandenberghe "Convex Optimization"
% Jo雔le Skaf - 08/29/05
% (a figure is generated)
%
% Let p_star(epsilon) be the optimal value of the following problem:
%               minimize    ||Ax + b + epsilon*d||_1
% Plots p_star(epsilon) versus epsilon and demonstrates the fact that it's
% affine on an interval that includes epsilon = 0.

cvx_quiet(true);
% Input data
A = [-2  7  1; ...
     -5 -1  3; ...
     -7  3 -5; ...
     -1  4 -4; ...
      1  5  5; ...
      2 -5 -1];
b = [-4 3 9 0 -11 5]';
d = [-10 -13 -27 -10 -7 14]';
epsilon = [-1:0.05:1];
p_star = zeros(size(epsilon));

fprintf(1,'Computing p*(epsilon) for -1 <= epsilon <= 1 ...');

for i=1:length(epsilon)
    cvx_begin
        variable x(3);
        minimize ( norm( A*x + b + epsilon(i)*d, 1) )
    cvx_end
    p_star(i)= cvx_optval;
end

fprintf(1,'Done! \n');

% Plots
plot(epsilon, p_star)
line([-.2 -.2], [2 14], 'LineStyle', '--')
line([.5 .5], [2 14], 'LineStyle', '--')
xlabel('\epsilon');
ylabel('p^*(\epsilon)');
title('p^*(\epsilon) vs \epsilon');