- 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;...
Expand abstract - Publication status:
- Published
- Version:
- Preprint
- Files:
-
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(pdf, 4.6MB)
-
- Publisher copy:
- 10.1002/nla.1948
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- 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
- Language:
- English
- Keywords:
- Copyright holder:
- John Wiley and Sons, Inc.
- Copyright date:
- 2015
- Notes:
- This is the pre-peer reviewed version of the article which has been published in final form at DOI: 10.1002/nla.1948. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving
Journal article
Performance comparisons of greedy algorithms in compressed sensing
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