Software

This page contains some usable software (in Matlab) mainly to solve problems involving sparsity, and code for experiments conducted in published papers. Some implementations can also be found as a part of paper submissions.

Orthogonal projections onto closed convex sets

This package contains functions that compute the orthogonal projection onto commonly used sets. It is used in all the other packages.

  • Functions to compute the orthogonal projection onto sets: l1,l2-norm balls, unit-simplex, box, nonnegative orthant, hyperplane, polytope, binary.

[projection package]

Optimization over sparse symmetric sets

Beck Amir, and Nadav Hallak. "On the minimization over sparse symmetric sets: Projections, optimality conditions, and algorithms." Mathematics of Operations Research 41.1 (2015): 196-223.

paper: [submitted version] [published version]

full package: (to be published, see standalone packages for now)

  • Function to compute sparse orthogonal projection onto a selection of symmetric sets.

  • Projected-gradient method to solve sparsity constrained optimization problems.

  • Coordinate-wise methods to solve sparsity constrained optimization problems.

standalone packages:

  • Orthogonal projection onto the sparse symmetric sets : [file]

Optimizing a function with sparsity penalty over symmetric sets

Beck Amir, and Nadav Hallak. “Proximal Mapping for Symmetric Penalty and Sparsity.” To appear in SIAM Optimization (accepted in 2017).

paper: [submitted version]

optimization package:

  • Function to compute sparse proximal mapping over a selection of symmetric sets.

  • Proximal-gradient method to solve sparsity penalized optimization problems over symmetric sets.

  • Coordinate-wise methods to solve sparsity penalized optimization problems over symmetric sets.

[optimization package]

Optimization Problems Involving Group Sparsity Terms

Beck Amir, and Nadav Hallak. “Optimization Problems Involving Group Sparsity Terms .” Submitted (2017).

paper: [submitted version]

optimization package: (to be published)

  • Function to compute group-sparse proximal mapping (penalized/constrained) over a selection of sets.

  • Proximal-gradient method to solve group-sparsity penalized optimization problems over a selection of sets.

  • Coordinate-wise methods to solve group-sparsity penalized optimization problems over a selection of sets.