Tropp, Joel A. and Wright, Stephen J.
Computational Methods for Sparse Solution of Linear Inverse Problems.
California Institute of Technology
, Pasadena, CA.
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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
|Item Type:||Report or Paper (Technical Report)|
|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 Agency||Grant Number|
|Subject Keywords:||sparse approximation, compressed sensing,
matching pursuit, convex optimization|
|Other Numbering System:|
|Other Numbering System Name||Other Numbering System ID|
|Applied & Computational Mathematics Technical Report||2009-01|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited On:||19 Oct 2011 18:13|
|Last Modified:||06 Mar 2015 23:09|
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