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Greedy algorithms for compressed sensing

Greedy algorithms for compressed sensing
Greedy algorithms for compressed sensing
Compressed Sensing (CS) is often synonymous with l1 based optimization. How- ever, when choosing an algorithm for a particular application, there are a range of different properties that have to be considered and weighed against each other. Important algorithm properties, such as speed and storage requirements, ease of implementation, flexibility and recovery performance have to be compared. In this chapter we will therefore present a range of alternative algorithms that can be used to solve the CS recovery problem and which outperform convex optimization based methods in some of these areas. These methods therefore add important versatility to any CS recovery toolbox
9781107005587
348-393
Cambridge University Press
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike E.
9ca3625e-5b14-4f1f-90ac-1af468f521ae
Rilling, Gabriel
87786512-d8b2-422b-b39e-cfcbf64c337f
Eldar, Yonina C.
Kutyniok, Gitta
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Davies, Mike E.
9ca3625e-5b14-4f1f-90ac-1af468f521ae
Rilling, Gabriel
87786512-d8b2-422b-b39e-cfcbf64c337f
Eldar, Yonina C.
Kutyniok, Gitta

Blumensath, Thomas, Davies, Mike E. and Rilling, Gabriel (2012) Greedy algorithms for compressed sensing. In, Eldar, Yonina C. and Kutyniok, Gitta (eds.) Compressed Sensing: Theory and Applications. Cambridge, GB. Cambridge University Press, pp. 348-393.

Record type: Book Section

Abstract

Compressed Sensing (CS) is often synonymous with l1 based optimization. How- ever, when choosing an algorithm for a particular application, there are a range of different properties that have to be considered and weighed against each other. Important algorithm properties, such as speed and storage requirements, ease of implementation, flexibility and recovery performance have to be compared. In this chapter we will therefore present a range of alternative algorithms that can be used to solve the CS recovery problem and which outperform convex optimization based methods in some of these areas. These methods therefore add important versatility to any CS recovery toolbox

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More information

Published date: May 2012
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 342650
URI: https://eprints.soton.ac.uk/id/eprint/342650
ISBN: 9781107005587
PURE UUID: c4d41511-7ced-408b-baef-f743a910b8f5

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Date deposited: 12 Sep 2012 08:41
Last modified: 16 Jul 2019 21:55

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Contributors

Author: Mike E. Davies
Author: Gabriel Rilling
Editor: Yonina C. Eldar
Editor: Gitta Kutyniok

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