demo_adpWBPDN

 demo_adpWBPDN.m

 Solves the following basis pursuit denoising (BPDN) problem
 min_x  \Sum \w_i |x_i| + 1/2*||y-Ax||_2^2

 while adaptively selecting the weights w_i

 Written by: Salman Asif, Georgia Tech
 Email: sasif@gatech.edu
 Created: June 16, 2011

0001 % demo_adpWBPDN.m
0002 %
0003 % Solves the following basis pursuit denoising (BPDN) problem
0004 % min_x  \Sum \w_i |x_i| + 1/2*||y-Ax||_2^2
0005 %
0006 % while adaptively selecting the weights w_i
0007 %
0008 % Written by: Salman Asif, Georgia Tech
0009 % Email: sasif@gatech.edu
0010 % Created: June 16, 2011
0011 
0012 clear
0013 close all
0014 
0015 %% parfor setup
0016 % numCores = str2double(getenv('NUMBER_OF_PROCESSORS'));
0017 % mpSize = numCores-1;
0018 % if matlabpool('size') ~= mpSize
0019 %     if matlabpool('size')~=0
0020 %         matlabpool close;
0021 %     else
0022 %         matlabpool('open', mpSize);
0023 %     end
0024 % end
0025 
0026 %% Setup path
0027 mname = mfilename;
0028 mpath = mfilename('fullpath');
0029 mdir = mpath(1:end-length(mname));
0030 cd(mdir);
0031 
0032 addpath ../Pursuits_Homotopy/
0033 addpath ../utils/
0034 addpath ../utils/utils_Wavelet
0035 addpath ../utils/utils_meas
0036 addpath ../solvers/
0037 addpath src/
0038 
0039 disp(['--------------------',datestr(now),'-------------------------'])
0040 
0041 % simulation parameters
0042 rwt_mode = 5;
0043 lambda = 0;
0044 
0045 
0046 SAVE_RESULTS = false;
0047 
0048 % SAVE_RESULTS = true;  diary(sprintf('%s-reproduce.txt',mname));
0049 
0050 largescale = 1;
0051 SNR = 40;
0052 R = 2; 
0053 
0054 IMG_LIST = {'barbara','boats', 'cameraman','house','peppers','shapes','lena','airplane','baboon','sailboat','tiffany'};
0055 sType = IMG_LIST{3};
0056 
0057 if largescale
0058     mType = 'noiselets';
0059     % sType = 'cameraman'; N = (256)^2;
0060     % sType = 'boats'; N = (256)^2;
0061     % sType = 'peppers'; N = (256)^2;
0062     % sType = 'house'; N = (256)^2;
0063     % sType = 'shapes'; N = (256)^2;
0064     % sType = 'pirate'; N = (512)^2;
0065     N = (256)^2;
0066     M = round(N/R);
0067     %M = 30000;
0068     T = N;
0069     str0 = sprintf('mType-%s, sType-%s, SNR = %d, (N,M) = %d, %d, rwt_mode-%d, lambda%3.4g.', mType, sType, SNR, N, M, rwt_mode, lambda);
0070     disp(str0);
0071 else
0072     mType = 'randn';
0073     sType = 'HeaviSine';
0074     N = 512;   % signal length
0075     M = round(N/2);    % no. of measurements
0076     T = round(M/3);    % sparsity level
0077     str0 = sprintf('mType-%s, sType-%s, SNR = %d, (N,M,T) = %d, %d, %d, rwt_mode-%d, lambda%3.4g.', mType, sType, SNR, N, M, T, rwt_mode, lambda);
0078     disp(str0);
0079 end
0080 
0081 % rank-1 update mode
0082 delx_mode = 'mil'; % mil or qr
0083 
0084 %% Simulation
0085 maxsim = 1;
0086 script_simulation_adpWBPDN