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Conference Papers Year : 2009

A Numerical Exploration of Compressed Sampling Recovery


This 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.
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hal-00365028 , version 1 (02-03-2009)


  • HAL Id : hal-00365028 , version 1


Charles H Dossal, Gabriel Peyré, Jalal M. Fadili. A Numerical Exploration of Compressed Sampling Recovery. SPARS'09, Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint-Malo, France. 5p. ⟨hal-00365028⟩
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