https://github.com/mpf/spgl1

A solver for large-scale sparse least squares
https://github.com/mpf/spgl1

Last synced: over 1 year ago
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A solver for large-scale sparse least squares

• Host: GitHub
• URL: https://github.com/mpf/spgl1
• Owner: mpf
• License: lgpl-2.1
• Created: 2010-06-11T03:29:21.000Z (about 16 years ago)
• Default Branch: master
• Last Pushed: 2020-10-19T11:03:55.000Z (over 5 years ago)
• Last Synced: 2025-02-27T16:21:29.144Z (over 1 year ago)
• Language: MATLAB
• Homepage: https://friedlander.io/spgl1/
• Size: 1.56 MB
• Stars: 110
• Watchers: 19
• Forks: 62
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog
  • License: COPYING

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README

          
# SPGL1: A solver for large-scale sparse least squares

[![GitHub license](https://img.shields.io/github/license/mpf/spgl1)](https://github.com/mpf/spgl1/blob/master/COPYING)

[![DOI:10.1137/080714488](https://zenodo.org/badge/DOI/10.1137/080714488.svg)](https://doi.org/10.1137/080714488)

* **Documentation**: https://friedlander.io/spgl1

## Introduction

SPGL1 is a Matlab solver

for large-scale one-norm regularized least squares.  It is designed to

solve any of the following three problems:

1. Basis pursuit denoise (BPDN):

   minimize  ||x||_1  subject to  ||Ax - b||_2 <= sigma,

2. Basis pursuit (BP):

   minimize   ||x||_1  subject to  Ax = b

 

3. Lasso:

   minimize  ||Ax - b||_2  subject to  ||x||_1 <= tau,

The matrix A can be defined explicily, or as an operator (i.e., a

function) that return both both Ax and A'y.  SPGL1 can solve these

three problems in both the real and complex domains.

Home page: https://friedlander.io/spgl1

## References :notebook:

The algorithm implemented by SPGL1 is described in the paper

- E. van den Berg and M. P. Friedlander, "Probing the Pareto frontier

  for basis pursuit solutions", SIAM J. on Scientific Computing,

  31(2):890-912, November 2008