wcrobls.m

斯坦福大学Grant和Boyd教授等开发的凸优化matlab工具箱

% Example 6.6: Comparison of worst-case robust, Tikhonov, and nominal least squares
% Section 6.4.2, Figure 6.16
% Boyd & Vandenberghe "Convex Optimization"
% Original by Lieven Vandenberghe
% Adapted for CVX Argyris Zymnis - 11/27/05
% (a figure is generated)
%
% Consider the least-squares problem:
%       minimize ||(A0 + u1*A1 + u2*A2)x - b||_2
% where u = [u1 u2]' is an uncertain parameter and ||u||_2 <= 1
% Three approximate solutions are found:
%   1- nominal optimal (i.e. letting u=0)
%   2- Tikhonov Regularized Solution:
%           minimize ||A0*x - b||_2 + delta*||x||_2
%      for some delta (in this case we set delta = 0.1)
%   3- worst-case robust approximation:
%           minimize sup{||u||_2 <= 1} ||(A0 + u1*A1 + u2*A2)x - b||_2)
%      (reduces to solving an SDP, see pages 323-324 in the book)

clear
cvx_quiet(false);

m = 50;
n = 20;
randn('state',0);
rand('state',0);

A0 = randn(m,n);
[U,S,V] = svd(A0);
S= diag(fliplr(logspace(-0.7,1,n)));
A0 = U(:,1:n)*S*V';
A1 = randn(m,n);  A1 = A1/norm(A1);
A2 = randn(m,n);  A2 = A2/norm(A2);

Aperb0 = [A1;A2];
p = 2;

b = U(:,1:n)*randn(n,1) + .1*randn(m,1);

% we consider LS problems || (A0 + u1*A1 + u2*A2) x - b||
% where  ||u|| leq rho

% Nominal Solution
xnom = A0\b;

% Tikhonov Regularized Solution
delta = .1;
xtych =  [A0; sqrt(delta)*eye(n)] \ [b; zeros(n,1)];

% Robust Least Squares solution
cvx_begin sdp
    variables t lambda xrob(n)
    minimize(t+lambda)
    subject to
        [eye(m) A1*xrob A2*xrob A0*xrob-b; ...
         [A1*xrob A2*xrob]' lambda*eye(2) zeros(2,1); ...
         [A0*xrob-b]' zeros(1,2) t] >= 0;
cvx_end

% Generate Random Trials
notrials=100000;
r = sqrt(rand(notrials,1));     % random on [0,1] with pdf g(r) = 2r;
theta = 2*pi*rand(notrials,1);  % uniform on [0,2pi]
v = [r.*cos(theta)  r.*sin(theta)];
ls_res = zeros(1,notrials);
rob2_res = zeros(1,notrials);
rob_res = zeros(1,notrials);
tych_res = zeros(1,notrials);

for i =1:notrials

  A = A0 + v(i,1)*A1 + v(i,2)*A2;
  ls_res(i) = norm(A*xnom-b);
  rob_res(i) = norm(A*xrob-b);
  tych_res(i) = norm(A*xtych-b);

end;


% Plot histograms
figure
%subplot(211)
[N1, hist1] = hist(ls_res,[min(ls_res):.1:max(ls_res)]);
freq1 = N1/notrials;
[N2, hist2] = hist(rob_res,hist1);
freq2 = N2/notrials;
[N3, hist3] = hist(tych_res,hist1);
freq3 = N3/notrials;



h = bar(hist3,freq3);
text(3, 0.07, 'Tikhonov');
set(h,'FaceColor',0.90*[1 1 1]);
hold on

h = bar(hist2,freq2);
text(4.2, 0.05, 'Nominal');
set(h,'FaceColor',0.80*[1 1 1]);

h = bar(hist2,freq2);
set(h,'FaceColor','none');
text(2.6, 0.2, 'Robust LS');

h = bar(hist3,freq3);
set(h,'FaceColor','none');
h = bar(hist1,freq1);
set(h,'FaceColor','none');

xlabel('||(A0 + u1*A1 + u2*A2)*x - b||_2')
ylabel('Frequency')
hold off