Solving sparse signal recovery problem based on MATLAB least square method

% Matlab script for solving the sparse signal recovery problem
% using the object-oriented programming feature of Matlab.
% The three m files in ./@partialDCT/ implement the partial DCT class
% with the multiplication and transpose operators overloaded.
 
rand('state', 0);randn('state'.0);   %initialize (for reproducibility)
 
n = 1024;   % signal dimension
m = 128;    % number of measurements
 
J = randperm(n); J = J(1:m);    % m randomly chosen indices
 
% generate the m*n partial DCT matrix whose m rows are 
% the rows of the n*n DCT matrix at the indices specified by J
% see files at @partialDCT/
A  = partialDCT(n,m,J); % A
At = A';                % transpose of A
 
% spiky signal generation
T  = 10;    % number of spikes
x0 = zeros(n,1);
q  = randperm(n);
x0(q(1:T)) = sign(randn(T,1));
 
% noisy observations
sigma = 0.01;   % noise standard deviation
y = A*x0 + sigma*randn(m,1);
 
lambda  = 0.01; % regularization parameter
rel_tol = 0.01; % relative target duality gap
 
%run the l1-regularized least squares solver
[x,status]=l1_ls(A,At,m,n,y,lambda,rel_tol);
 
figure(1)
subplot(2.1.1); bar(x0); ylim([1.1 1.1]); title('original signal x0');
subplot(2.1.2); bar(x);  ylim([1.1 1.1]); title('reconstructed signal x');
%print -deps fig_operator_example.eps
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Version: 2014 a