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dc.contributor.authorFadili, Jalal
dc.contributor.authorPeyré, Gabriel
dc.contributor.authorDossal, Charles
dc.date.accessioned2009-06-30T12:31:13Z
dc.date.available2009-06-30T12:31:13Z
dc.date.issued2009-04
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/568
dc.language.isoenen
dc.subjectL1 minimizationen
dc.subjectsparsityen
dc.subjectCompressed sensingen
dc.subject.ddc519en
dc.titleA Numerical Exploration of Compressed Sampling Recoveryen
dc.typeCommunication / Conférenceen_US
dc.contributor.editoruniversityotherUniversité de Bordeaux I;France
dc.contributor.editoruniversityotherUniversité de Caen;France
dc.description.abstractenThis paper explores numerically the efficiency of $\lun$ minimization for the recovery of sparse signals from compressed sampling measurements in the noiseless case. Inspired by topological criteria for $\lun$-identifiability, a greedy algorithm computes sparse vectors that are difficult to recover by $\ell_1$-minimization. We evaluate numerically the theoretical analysis without resorting to Monte-Carlo sampling, which tends to avoid worst case scenarios. This allows one to challenge sparse recovery conditions based on polytope projection and on the restricted isometry property.en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00365028/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitleSPARS'09, Signal Processing with Adaptive Sparse Structured Representationsen
dc.relation.confdate2009-04
dc.relation.confcitySaint-Maloen
dc.relation.confcountryFranceen


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