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Computational Methods for Sparse Solution of Linear Inverse Problems

Tropp, Joel A. and Wright, Stephen J. (2009) Computational Methods for Sparse Solution of Linear Inverse Problems. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20111011-163243421

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Abstract

In sparse approximation problems, the goal is to find an approximate representation of a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major algorithms that are used for solving sparse approximation problems in practice. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available. Many fundamental questions in electrical engineering, statistics, and applied mathematics can be posed as sparse approximation problems, which makes the algorithms discussed in this paper versatile tools with a wealth of applications.


Item Type:Report or Paper (Technical Report)
Related URLs:
URLURL TypeDescription
http://resolver.caltech.edu/CaltechAUTHORS:20100608-080853280OtherUNSPECIFIED
Additional Information:JAT was supported by ONR N00014-08-1-2065. SJW was supported by NSF CCF-0430504, DMS-0427689, CTS-0456694, and CNS-0540147.
Group:Applied & Computational Mathematics
Funders:
Funding AgencyGrant Number
ONRN00014-08-1-2065
NSFCCF-0430504
NSFDMS-0427689
NSFCTS-0456694
NSFCNS-0540147
Subject Keywords:sparse approximation, compressed sensing, matching pursuit, convex optimization
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Applied & Computational Mathematics Technical Report2009-01
Record Number:CaltechAUTHORS:20111011-163243421
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20111011-163243421
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27171
Collection:CaltechACMTR
Deposited By: Kristin Buxton
Deposited On:19 Oct 2011 18:13
Last Modified:26 Dec 2012 14:15

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