DOSSAL, Charles; PEYRÉ, Gabriel; FADILI, Jalal M.

doi:10.1016/j.laa.2009.11.022

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DOSSAL, Charles
Institut de Mathématiques de Bordeaux [IMB]

PEYRÉ, Gabriel
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]

FADILI, Jalal M.
Equipe Image - Laboratoire GREYC - UMR6072

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Linear Algebra and its Applications. 2010-03, vol. 432, n° 7, p. 1663-1679

Elsevier

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This 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-conditionned. 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.< Leer menos

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polytopes

Compressed sensing

L1 minimization

restricted isometry constant

polytopes.

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A Numerical Exploration of Compressed Sampling Recovery

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