| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 8898191 | Applied and Computational Harmonic Analysis | 2018 | 37 Pages |
Abstract
Moreover, we observe Serial-â0 and Parallel-â0 to be able to solve large scale problems with a larger fraction of nonzeros than other algorithms when the number of measurements is substantially less than the signal length; in particular, they are able to reliably solve for a k-sparse vector xâRn from m expander measurements with n/m=103 and k/m up to four times greater than what is achievable by â1-regularisation from dense Gaussian measurements. Additionally, due to their low computational complexity, Serial-â0 and Parallel-â0 are observed to be able to solve large problems sizes in substantially less time than other algorithms for compressed sensing. In particular, Parallel-â0 is structured to take advantage of massively parallel architectures.
Keywords
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Physical Sciences and Engineering
Mathematics
Analysis
Authors
Rodrigo Mendoza-Smith, Jared Tanner,