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Challenging Restricted Isometry Constants with Greedy Pursuit

Abstract : This paper proposes greedy numerical schemes to compute lower bounds of the restricted isometry constants that are central in compressed sensing theory. Matrices with small restricted isometry constants enable stable recovery from a small set of random linear measurements. We challenge this compressed sampling recovery using greedy pursuit algorithms that detect ill-conditionned sub-matrices. It turns out that these sub-matrices have large isometry constants and hinder the performance of compressed sensing recovery.
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Contributor : Gabriel Peyré <>
Submitted on : Monday, July 13, 2009 - 11:02:03 AM
Last modification on : Monday, August 3, 2020 - 3:41:21 AM
Document(s) archivé(s) le : Saturday, November 26, 2016 - 10:50:46 AM


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  • HAL Id : hal-00373450, version 2


Charles Dossal, Gabriel Peyré, Jalal M. Fadili. Challenging Restricted Isometry Constants with Greedy Pursuit. ITW'09, Oct 2009, Taormina, Italy. pp.475-479. ⟨hal-00373450v2⟩



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