% Section 6.4.2
% Boyd & Vandenberghe "Convex Optimization"
% Original by Lieven Vandenberghe
% Adapted for CVX by Joelle Skaf - 10/03/05
%
% Consider the least-squares problem:
%       minimize ||(A + tB)x - b||_2
% where t is an uncertain parameter in [-1,1]
% Three approximate solutions are found:
%   1- nominal optimal (i.e. letting t=0)
%   2- stochastic robust approximation:
%           minimize E||(A+tB)x - b||_2
%      assuming u is uniformly distributed on [-1,1] )
%      (reduces to minimizing E ||(A+tB)x-b||^2 = ||A*x-b||^2  + x^TPx
%        where P = E(t^2) B^TB = (1/3) B^TB )
%   3- worst-case robust approximation:
%           minimize sup{-1<=u<=1} ||(A+tB)x - b||_2)
%      (reduces to minimizing max{||(A-B)x - b||_2, ||(A+B)x - b||_2} )

% Input Data
randn('seed',0);
m=20;  n=10;
A = randn(m,n);
[U,S,V] = svd(A);
S = diag(logspace(-1,1,n));
A = U(:,1:n)*S*V';

B = randn(m,n);
B = B/norm(B);

b = randn(m,1);

% Case 1: Nominal optimal solution
fprintf(1,'Computing the optimal solution for: \n');
fprintf(1,'1) the nominal problem ... ');

cvx_begin quiet
    variable x_nom(n)
    minimize ( norm(A*x_nom - b) )
cvx_end
%         (reduces to minimizing max{||(A-B)x - b||_2, ||(A+B)x - b||_2}
fprintf(1,'Done! \n');

% Case 2: Stochastic robust approximation

fprintf(1,'2) the stochastic robust approximation problem ... ');

P = (1/3)*B'*B;
cvx_begin quiet
    variable x_stoch(n)
    minimize (  square_pos(norm(A*x_stoch - b)) + quad_form(x_stoch,P) )
cvx_end

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

% Case 3: Worst-case robust approximation

fprintf(1,'3) the worst-case robust approximation problem ... ');

cvx_begin quiet
    variable x_wc(n)
    minimize ( max( norm((A-B)*x_wc - b), norm((A+B)*x_wc - b) ) )
cvx_end

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

% plot residuals
novals = 100;
parvals = linspace(-2,2,novals);

errvals_ls = [];
errvals_stoch = [];
errvals_wc = [];
for k=1:novals
   errvals_ls = [errvals_ls, norm((A+parvals(k)*B)*x_nom - b)];
   errvals_stoch = [errvals_stoch, norm((A+parvals(k)*B)*x_stoch - b)];
   errvals_wc = [errvals_wc, norm((A+parvals(k)*B)*x_wc - b)];
end;

plot(parvals, errvals_ls, '-', parvals, errvals_stoch, '-', ...
     parvals, errvals_wc, '-', [-1;-1], [0; 12], 'k--', ...
     [1;1], [0; 12], 'k--');
xlabel('u');
ylabel('r(u) = ||A(u)x-b||_2');
title('Residual r(u) vs a parameter u for three approximate solutions');
% print -deps robappr.eps

Computing the optimal solution for: 
1) the nominal problem ... Done! 
2) the stochastic robust approximation problem ... Done! 
3) the worst-case robust approximation problem ... Done!