Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Proximal-Gradient Homotopy Method for Sparse Least Squares

Proximal-gradient homotopy is an efficient numerical method for solving the L1-regularized least-squares problem—minimize_x (1/2) ||A*x-b||_2^2 + lambda*||x||_1—where A is an m-by-n matrix, and lambda is a positive regularization parameter. This method is especially effective for sparse recovery applications in which the dimensions satisfies m < n and the optimal solution x* is provably sparse. The implementation in MATLAB can solve the more general problem—minimize_x f(x) + lambda*R(x)—where f(x) is a differentiable convex function and R(x) is a simple convex function whose proximal mapping can be computed efficiently.


Date Published23 March 2012
Download Size0.02 MB

Note By installing, copying, or otherwise using this software, you agree to be bound by the terms of its license. Read the license.