Authors
Description
ASP is a set of Matlab routines for solving several variations of the sparse optimization problem
$$
\mathop{\mbox{minimize}}_{x} \quad \lambda\x\_1 + \frac12\Axb\_2^2
$$
It implements algorithms for the following:
 basis pursuit denoising (including \(Ax=b\))
 orthogonal matching pursuit
 homotopy version of basis pursuit denoising
 reweighted basis pursuit for approximating 0norm solutions
 sequential compressed sensing (adding rows to \(A\) and \(b\))
 nonnegative leastsquares
 sparseresidual and sparsesolution regression
 generalized Lasso for sparsity in \(Bx\)
References

M. P. Friedlander and M. A. Saunders (2012). A dual activeset quadratic programming method for finding sparse leastsquares solutions, DRAFT Technical Report, Dept of Computer Science, University of British Columbia, July 30, 2012. PDF

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, 11811186. [This paper made use of ASP (via BPdual) as well as CVX, FISTA, SPGL1, and Mosek.]
Download
Version 1.0, December 17, 2012: aspv1.0.zip