Sparse optimization with least-squares constraints

TitleSparse optimization with least-squares constraints
Publication TypeJournal Article
Year of Publication2011
AuthorsEwout van den Berg, Michael P. Friedlander
JournalSIAM Journal on Optimization
Volume21
Number4
Page1201–1229
Month11
Keywordsbasis pursuit, compressed sensing, convex program, duality, group sparsity, matrix completion, Newton’s method, root-finding, sparse solutions
Abstract

The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a general class of sparsifying formulations. For several common types of sparsity we provide applications, along with details on how to apply the algorithm, and experimental results.

URLhttp://www.math.ucdavis.edu/%7Empf/2010-sparse-optimization-with-least-squares.html
DOI10.1137/100785028
Citation Keyvandenberg2011SIAMsol