Basis Pursuit Denoising with Forward-Backward

Test the use of Forward-backward-like splitting for the resolution of a compressed sensing regularization.


Dimension of the problem.

n = 600;
p = n/4;

Regularization parameter.

lambda = 1;

Matrix and observation.

A = randn(p,n);
y = randn(p,1);

List of benchmarked algorithms.

methods = {'fb', 'fista', 'nesterov'};

operator callbacks

F = @(x)lambda*norm(x,1);
G = @(x)1/2*norm(y-A*x)^2;

Proximal operator of F.

ProxF = @(x,tau)perform_soft_thresholding(x,lambda*tau);

Gradient operator of G.

GradG = @(x)A'*(A*x-y);

Lipschitz constant.

L = norm(A)^2;

Function to record the energy. = @(x)F(x)+G(x);

Bench the algorithm

options.niter = 5000;
E = [];
for i=1:length(methods)
    options.method = methods{i};
    [x,e] = perform_fb(zeros(n,1), ProxF, GradG, L, options);
    E(:,i) = e(:);
e = min(E(:));

Display the decays of the energies.

IMPORTANT: Note that the comparison with Nesterov is unfair, since each Nesterov iteration cost twice more.

sel = 1:round(options.niter/10);
axis tight;