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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 (Unpublished)

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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
Tropp, Joel A.0000-0003-1024-1791
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
Funding AgencyGrant Number
Subject Keywords:sparse approximation, compressed sensing, matching pursuit, convex optimization
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Other Numbering System NameOther Numbering System ID
Applied & Computational Mathematics Technical Report2009-01
Record Number:CaltechAUTHORS:20111011-163243421
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27171
Deposited By: Kristin Buxton
Deposited On:19 Oct 2011 18:13
Last Modified:06 Mar 2015 23:09

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