# Candes.m

(Difference between revisions)
 Revision as of 21:06, 16 December 2008 (edit)← Previous diff Revision as of 21:08, 16 December 2008 (edit) (undo)Next diff → Line 38: Line 38: randsample() is from Matlab Statistics Toolbox. randsample() is from Matlab Statistics Toolbox. - Failure modes are reparable by [[Convex Iteration]] from [http://www.convexoptimization.com/TOOLS/0976401304.pdf Convex Optimization & Euclidean Distance Geometry, Ch.4]: + Failure modes are reparable by [[Convex Iteration]] from [http://www.convexoptimization.com/TOOLS/0976401304.pdf ''Convex Optimization & Euclidean Distance Geometry'', Ch.4]:

%Emmanuel Candes, California Institute of Technology, June 6 2007, IMA Summerschool.                                                                                                                                                         %Emmanuel Candes, California Institute of Technology, June 6 2007, IMA Summerschool.

## Revision as of 21:08, 16 December 2008

This Matlab demonstration of compressive sampling (a.k.a. compressed sensing) by Emmanuel Candes
comes from his June 6 2007 video on the Optimization Videos page.

```%Emmanuel Candes, California Institute of Technology, June 6 2007, IMA Summerschool.
%Transcribed by Jon Dattorro.
%Fails using SDP solver SDPT3  on 7th consecutive run after Matlab R2007b startup.  CVX version 1.2 (build 656).
%Fails using SDP solver Sedumi on 4th consecutive run after Matlab R2007b startup.  CVX version 1.2 (build 656).
clear all, close all
n = 512;                            % Size of signal
m = 64;                             % Number of samples (undersample by a factor 8)

k = 0:n-1;  t = 0:n-1;
F = exp(-i*2*pi*k'*t/n)/sqrt(n);    % Fourier matrix
freq = randsample(n,m);
A = [real(F(freq,:));
imag(F(freq,:))];              % Incomplete Fourier matrix

S = 28;
support = randsample(n,S);
x0 = zeros(n,1);  x0(support) = randn(S,1);
b = A*x0;

% Solve l1 using CVX
cvx_quiet(true);
%cvx_solver('sedumi');
cvx_begin
variable x(n);
minimize(norm(x,1));
A*x == b;
cvx_end

norm(x - x0)/norm(x0)
figure, plot(1:n,x0,'b*',1:n,x,'ro'), legend('original','decoded')
```

Code between `cvx_begin` and `cvx_end` requires CVX.

`randsample()` is from Matlab Statistics Toolbox.

Failure modes are reparable by Convex Iteration from Convex Optimization & Euclidean Distance Geometry, Ch.4:

```%Emmanuel Candes, California Institute of Technology, June 6 2007, IMA Summerschool.
%Convex Iteration implementation by Jon Dattorro.
%Failure modes repaired.
clear all, close all
n = 512;                            % Size of signal
m = 64;                             % Number of samples (undersample by a factor 8)

k = 0:n-1;  t = 0:n-1;
F = exp(-i*2*pi*k'*t/n)/sqrt(n);    % Fourier matrix
freq = randsample(n,m);
A = [real(F(freq,:));
imag(F(freq,:))];              % Incomplete Fourier matrix

S = 28;
support = randsample(n,S);
x0 = zeros(n,1);  x0(support) = randn(S,1);
b = A*x0;

cvx_quiet(true);
%cvx_solver('sedumi');

%convex iteration
y = ones(n,1);
while 1
% Solve l0 using CVX and Convex Iteration
cvx_begin
variable x(n);
minimize(norm(y.*x,1));
A*x == b;
cvx_end

% update search direction y
[x_sorted, indices] = sort(abs(x), 'descend');
y = ones(n,1);
y(indices(1:S)) = 0;

cardx = sum(abs(x) > 1e-6)
if cardx <= S, break, end
end
norm(x - x0)/norm(x0)
figure, plot(1:n,x0,'b*',1:n,x,'ro'), legend('original','decoded')
```