120 lines (103 with data), 3.4 kB

clear, clc;

% This is an example for running the function LeastR
% 
%  min  1/2 || A x - y||^2 + 1/2 * rsL2 * ||x||_2^2 + rho * ||x||_1 
%
% For detailed description of the function, please refer to the Manual.
%
%% Related papers
%
% [1]  Jun Liu and Jieping Ye, Efficient Euclidean Projections
%      in Linear Time, ICML 2009.
%
% [2]  Jun Liu and Jieping Ye, Sparse Learning with Efficient Euclidean
%      Projections onto the L1 Ball, Technical Report ASU, 2008.
%
% [3]  Jun Liu, Jianhui Chen, and Jieping Ye, 
%      Large-Scale Sparse Logistic Regression, KDD, 2009.
%
%% ------------   History --------------------
%
% First version on August 10, 2009.
%
% September 5, 2009: adaptive line search is added
%
% For any problem, please contact Jun Liu (j.liu@asu.edu)

cd ..
cd ..

root=cd;
addpath(genpath([root '/SLEP']));
                     % add the functions in the folder SLEP to the path
                   
% change to the original folder
cd Examples/L1;

m=1000;  n=1000;    % The data matrix is of size m x n

% for reproducibility
randNum=1;

% ---------------------- Generate random data ----------------------
randn('state',(randNum-1)*3+1);
A=randn(m,n);       % the data matrix

randn('state',(randNum-1)*3+2);
xOrin=randn(n,1);

randn('state',(randNum-1)*3+3);
noise=randn(m,1);
% y=A*xOrin +...
%     noise*0.01;     % the response
[u,s,v]=svd(A,0);
v=v(:,1);
y=A*v;

rho=0;            % the regularization parameter
                    % it is a ratio between (0,1), if .rFlag=1

%----------------------- Set optional items ------------------------
opts=[];

% Starting point
opts.init=2;        % starting from a zero point

% termination criterion
opts.tFlag=5;       % run .maxIter iterations
opts.maxIter=1000;   % maximum number of iterations

% normalization
opts.nFlag=0;       % without normalization

% regularization
opts.rFlag=0;       % the input parameter 'rho' is a ratio in (0, 1)
opts.rsL2=1;     % the squared two norm term

%----------------------- Run the code LeastR -----------------------
fprintf('\n mFlag=1, lFlag=1 \n');
opts.mFlag=1;       % treating it as compositive function 
opts.lFlag=1;       % Nemirovski's line search
tic;
[x1, funVal1, ValueL1]= LeastR(A, y, rho, opts);
toc;

% opts.maxIter=1000;
% 
% fprintf('\n mFlag=1, lFlag=0 \n');
% opts.mFlag=1;       % smooth reformulation 
% opts.lFlag=0;       % Nemirovski's line search
% opts.tFlag=2; opts.tol= funVal1(end);
% tic;
% [x2, funVal2, ValueL2]= LeastR(A, y, rho, opts);
% toc;
% 
% fprintf('\n mFlag=1, lFlag=1 \n');
% opts.mFlag=1;       % smooth reformulation 
% opts.lFlag=1;       % adaptive line search
% opts.tFlag=2; opts.tol= funVal1(end);
% tic;
% [x3, funVal3, ValueL3]= LeastR(A, y, rho, opts);
% toc;
% 
% figure;
% plot(funVal1,'-r');
% hold on;
% plot(funVal2,'--b');
% hold on;
% plot(funVal3,':g');
% legend('mFlag=0, lFlag=0', 'mFlag=1, lFlag=0', 'mFlag=1, lFlag=1');
% xlabel('Iteration (i)');
% ylabel('The objective function value');
% 
% % % --------------------- compute the pathwise solutions ----------------
% opts.fName='LeastR';      % set the function name to 'LeastR'
% Z=[0.5, 0.2, 0.1, 0.01];  % set the parameters
% 
% % run the function pathSolutionLeast
% fprintf('\n Compute the pathwise solutions, please wait...');
% X=pathSolutionLeast(A, y, Z, opts);