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Systems Optimization Laboratory

Stanford University
Dept of Management Science and Engineering (MS&E)

Huang Engineering Center

Stanford, CA 94305-4121  USA

ASP: Sparse Optimization

  • AUTHORS: Michael Friedlander and Michael Saunders.
  • CONTENTS: A set of Matlab routines for solving several variations of the sparse optimization problem \begin{align*} \text{minimize } & \lambda \|x\|_1 + \frac12 \|Ax-b\|_2^2, \end{align*} where \(A\) may be an explicit dense or sparse rectangular matrix or an operator for computing \(Av\) and \(A^T u\) for given vectors \(v, u\).
    There are functions for the following problems:


    • basis pursuit denoising (BPDN), including \(Ax=b\) (BP)
    • orthogonal matching pursuit
    • homotopy version of basis pursuit denoising
    • reweighted basis pursuit for approximating 0-norm solutions
    • sequential compressed sensing (adding rows to \(A\) and \(b\))
    • nonnegative least-squares
    • sparse-residual and sparse-solution regression
    • generalized Lasso for sparsity in \(Bx\)

    For certain matching values of \(\lambda\) and \(\tau\), BPDN is equivalent to the Lasso problem: minimize \(\frac12 \|Ax-b\|_2^2\) subject to \(\|x\|_1 \le \tau\).


  • REFERENCES:
    [1] Michael Friedlander and Michael Saunders (2012). A dual active-set quadratic programming method for finding sparse least-squares solutions, DRAFT Technical Report, Dept of Computer Science, University of British Columbia, July 30, 2012.


    [2] Hatef Monajemi, Sina Jafarpour, Matan Gavish, Stat 330/CME 362 Collaboration and David L. Donoho (2012). Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices, PNAS 110:4, 1181-1186.
    This paper made use of ASP (via BPdual) as well as CVX, FISTA, SPGL1, and Mosek.

  • RELEASE:
    17 Dec 2012: asp-v1.0.zip downloadable from Michael Friedlander's homepage.
    14 May 2015: BPprimal.m added to zip.
    26 May 2015: asp is now a Git repository.

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