About SparseLab

Changes and Enhancements for Release 2.0: 4 papers have been added to SparseLab 200: "Fast Solution of l1-norm Minimization Problems When the Solutions May be Sparse"; "Why Simple Shrinkage is Still Relevant For Redundant Representations"; "Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise"; "On the Stability of Basis Pursuit in the Presence of Noise." SparseLab is a library of Matlab routines for finding sparse solutions to underdetermined systems. The library is available free of charge over the Internet. Versions are provided for Macintosh, UNIX and Windows machines. Downloading and installation instructions are given here. SparseLab has over 400 .m files which are documented, indexed and cross-referenced in various ways. In this document we suggest several ways to get started using SparseLab: (a) trying out the pedagogical examples, (b) running the demonstrations, which illustrate the use of SparseLab in published papers, and (c) browsing the extensive collection of source files, which are self-documenting. SparseLab makes available, in one package, all the code to reproduce all the figures in the included published articles. The interested reader can inspect the source code to see exactly what algorithms were used, and how parameters were set in producing our figures, and can then modify the source to produce variations on our results. SparseLab has been developed, in part, because of exhortations by Jon Claerbout of Stanford that computational scientists should engage in "really reproducible" research. This document helps with installation and getting started, as well as describing the philosophy, limitations and rules of the road for this software