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dc.contributor.authorFadili, Jalal
dc.contributor.authorPeyré, Gabriel
dc.contributor.authorDossal, Charles
dc.date.accessioned2010-02-23T08:21:12Z
dc.date.available2010-02-23T08:21:12Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/3547
dc.language.isoenen
dc.subjectCompressed sensingen
dc.subjectRestricted isometry constanten
dc.subjectPolytopesen
dc.subjectℓ1 minimizationen
dc.subject.ddc512en
dc.titleA numerical exploration of compressed sampling recoveryen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper explores numerically the efficiency of ℓ1 minimization for the recovery of sparse signals from compressed sampling measurements in the noiseless case. This numerical exploration is driven by a new greedy pursuit algorithm that computes sparse vectors that are difficult to recover by ℓ1 minimization. The supports of these pathological vectors are also used to select sub-matrices that are ill-conditioned. This allows us to challenge theoretical identifiability criteria based on polytopes analysis and on restricted isometry conditions. We evaluate numerically the theoretical analysis without resorting to Monte-Carlo sampling, which tends to avoid worst case scenarios.en
dc.relation.isversionofjnlnameLinear Algebra and its Applications
dc.relation.isversionofjnlvol432en
dc.relation.isversionofjnlissue7en
dc.relation.isversionofjnldate2010-03
dc.relation.isversionofjnlpages1663-1679en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.laa.2009.11.022en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00402455/en/en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelAlgèbreen


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