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About SparseLab
David Donoho, Victoria Stodden, Yaakov Tsaig
Stanford University
Version 2.0
March, 2007
Abstract
Changes and Enhancements for Release 2.0: 4 papers have been added to Sparse-
lab 200: ”Fast Solution of l1-norm Minimization Problems When the Solutions May be
Sparse”; ”Why Simple Shrinkage is Still Relevant For Redundant Representations”; ”Sta-
ble 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.
Acknowledgment of Support. This work was partially supported by NSF DMS-05-05303
(Stanford).
1
Contents
1 Introduction 3
2 Access and Installation 4
2.1 Platform-Specific Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 WEB Acess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Pathnames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.5 Checklists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.5.1 UNIX Checklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.5.2 Macintosh Checklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.5.3 PC Checklist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.6 Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Getting Started 8
3.1 Snooping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 Contents Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.2 Help for Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1.3 Source Browsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.1.4 Documentation Directory . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1.5 Dataset Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Demos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.1 Demo Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Reproducible Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.5 Freeware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Fine Print 16
4.1 Dependence on Matlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Registration – SparseLab Registration . . . . . . . . . . . . . . . . . . . . . . . . 16
4.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.4 Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5 No Charge – No Charge for SparseLab Software . . . . . . . . . . . . . . . . . . . 17
4.6 No Warranty – No Warranty on SparseLab software . . . . . . . . . . . . . . . . 17
4.7 Copyright – SparseLab Copying Permissions . . . . . . . . . . . . . . . . . . . . . 17
4.8 Thanks – Thanks to contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2
1 Introduction
SparseLab is a library of Matlab routines for finding sparse solutions to underdetermined systems.
The library provides the research community with open source tools for sparse representation,
as well as being the basis for research by the authors, and may b e used to reproduce the figures
in their published articles, and to redo those figures with variations in the parameters.
The library is available free of charge over the Internet by WWW access; instructions are
given below. The material is, however, copyrighted, so that advance permission is required for
any commercial use.
The package approaches the problem of sparse representation from both signal processing and
statistical viewpoints. The user is free to choose the terminology he or she is comfortable with.
SparseLab incorporates software for several published solvers, for example Michael Saunders’
Primal-Dual method for Optimization with Convex Objectives, Mallat and Zhang’s Matching
Pursuit, Donoho and Johnstone’s Iterative Hard and Soft Thresholding, Efron et al’s Least Angle
Regression, and a number of others.
In addition to routines finding sparse solutions to systems, the library contains scripts which
give a quick examples in a variety of different settings. We believe that by studying these scripts
one can quickly learn the practical aspects of sparse representation and one can learn how to use
the SparseLab software library
In this guide we give information which will help you access and install the software on your
machine and get started in exploring the resources contained in the SparseLab distribution. We
also explain the philosophy which underlies our distribution of the software, and some of the fine
print associated with the software.
There are other resources for obtaining information about SparseLab. First, there is a Sparse-
Lab Architecture guide which gives details about how SparseLab is constructed and maintained.
Secondly, we give more information on the SparseLab website.
This body of software is under continuing development by a team of researchers supported
by a grant from the NSF, and from other sponsors. We conduct our research with the idea, from
the beginning, that we will implement our tools in SparseLab. We believe that the discipline
this entails makes our research of a higher quality than otherwise possible.
We welcome your suggestions for further enhancements, and any contributions you might
make.
3
2 Access and Installation
The SparseLab library contains .m files (Matlab code), datasets, documentation scripts and
workouts (both also .m files) for reproducing the figures in articles by the authors.
The whole library consists of over 400 files. It requires more than 200MB and less than
400MB space on disk once it is downloaded, decompressed and installed. The largest data files
for two demos are included in separate packages: Sparselab100 DataSupplementExtCS.zip and
Sparselab100 DataSupplementStOMP.zip – the majority of the size comes from these compo-
nents.
This documentation refers to Version 2.0 of SparseLab.
2.1 Platform-Specific Information
SparseLab is available for use in Matlab 6.x or 7.x on three different platforms: Windows XP or
2000, UNIX/Linux and Macintosh. The package is made available as a compressed archive, in a
.zip format.
You do have to know about one convention used in the documentation. We always use the UNIX
pathname conventions rather than PC or Macintosh, e.g. Matlab/Toolbox/Sparselab rather
than Matlab\Toolbox\SparseLab or Matlab:Toolbox:WaveLab. You have to transliterate what
we say into the version appropriate for your platform.
2.2 WEB Acess
To download the compressed archive from the web, point your web browser to http://sparselab.stanford.edu
to access the SparseLab web-page. Once there, mouse click the ”Download” link in the left frame.
2.3 Installation
In this section we first describe the installation process in narrative form, and later give a step-
by-step checklist.
Once the appropriate compressed archive has been transferred to your machine, it should
be decompressed and installed. You will need an appropriate software to decompress .zip file
Sparselab200.zip. On a personal computer (Macintosh or Windows), the archives should be
decompressed and installed as a subdirectory of the Toolbox directory inside the matlab folder.
On a UNIX workstation or server, the archives could either be installed in the systemwide matlab
directory, if you have permission to do this, or in your own personal matlab directory, if you do
not.
Once the actual files are installed, you should have a number of files and subdirectories in
the directory Sparselab. If you look in the files Contents.m inside of the Sparselab directory, you
will see a plan of what is inside:
% SparseLab Main Directory, Version 100
%
% This is the main directory of the SparseLab package.
%
% .m files in this directory
%
% Contents.m - This file
% SparsePath.m - Sets up global variables and pathnames
%
% Subdirectories
% Documentation - System-Wide Documentation
% /About SparseLab
% /SparseLab Architecture
% Examples - Detailed examples of SparseLab finding sparse solutions
% /nnfEX - Nonnegative Factorization Example
4
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