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Performance comparisons of greedy algorithms in compressed sensing

Abstract:

Compressed sensing has motivated the development of numerous sparse approximation algorithms designed to return a solution to an underdetermined system of linear equations where the solution has the fewest number of nonzeros possible, referred to as the sparsest solution. In the compressed sensing setting, greedy sparse approximation algorithms have been observed to be both able to recover the sparsest solution for similar problem sizes as other algorithms and to be computationally efficient;...

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Publication status:
Published
Version:
Preprint

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Publisher copy:
10.1002/nla.1948

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Institution:
University of Oxford
Department:
Oxford, MPLS, Mathematical Inst
Role:
Author
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Funding agency for:
Tanner, J
Journal:
Numerical Linear Algebra with Applications
Volume:
22
Issue:
2
Pages:
254-282
Publication date:
2015-03-05
DOI:
EISSN:
1099-1506
ISSN:
1070-5325
URN:
uuid:4f8b7b8f-cf0b-450f-90f6-59883b0c5255
Source identifiers:
478275
Local pid:
pubs:478275

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