Conference proceeding
Breaking the ℓ1 recovery thresholds with reweighted ℓ1 optimization
2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp.1026-1030
09/2009
DOI: 10.1109/ALLERTON.2009.5394882
Abstract
It is now well understood that l 1 minimization algorithm is able to recover sparse signals from incomplete measurements and sharp recoverable sparsity thresholds have also been obtained for the l 1 minimization algorithm. In this paper, we investigate a new iterative reweighted l 1 minimization algorithm and showed that the new algorithm can increase the sparsity recovery threshold of l 1 minimization when decoding signals from relevant distributions. Interestingly, we observed that the recovery threshold performance of the new algorithm depends on the behavior, more specifically the derivatives, of the signal amplitude probability distribution at the origin.
Details
- Title: Subtitle
- Breaking the ℓ1 recovery thresholds with reweighted ℓ1 optimization
- Creators
- Weiyu Xu - California Institute of TechnologyM Amin Khajehnejad - California Institute of TechnologyA. Salman Avestimehr - California Institute of TechnologyBabak Hassibi - California Institute of Technology
- Resource Type
- Conference proceeding
- Publication Details
- 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp.1026-1030
- DOI
- 10.1109/ALLERTON.2009.5394882
- Publisher
- IEEE
- Language
- English
- Date published
- 09/2009
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197455902771
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