New algorithm for computing sparse solutions to linear inverse problems

G. Harikumar, Yoram Bresler

Research output: Contribution to journalConference article

Abstract

In this paper, we present an iterative algorithm for computing sparse solutions (or sparse approximate solutions) to linear inverse problems. The algorithm is intended to supplement the existing arsenal of techniques. It is shown to converge to the local minima of a function of the form used for picking out sparse solutions, and its connection with existing techniques explained. Finally, it is demonstrated on subset selection and deconvolution examples. The fact that the proposed algorithm is sometimes successful when existing greedy algorithms fail is also demonstrated.

Original languageEnglish (US)
Pages (from-to)1331-1334
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - Jan 1 1996
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: May 7 1996May 10 1996

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'New algorithm for computing sparse solutions to linear inverse problems'. Together they form a unique fingerprint.

Cite this