A fast proximal point algorithm for ℓ1-minimization problem in compressed sensing

Yun Zhu, Jian Wu and Gaohang Yu

Applied Mathematics and Computation, 2015, vol. 270, issue C, 777-784

Abstract: In this paper, a fast proximal point algorithm (PPA) is proposed for solving ℓ1-minimization problem arising from compressed sensing. The proposed algorithm can be regarded as a new adaptive version of customized proximal point algorithm, which is based on a novel decomposition for the given nonsymmetric proximal matrix M. Since the proposed method is also a special case of the PPA-based contraction method, its global convergence can be established using the framework of a contraction method. Numerical results illustrate that the proposed algorithm outperforms some existing proximal point algorithms for sparse signal reconstruction.

Keywords: Proximal point algorithm; ℓ1-regularized least square; Compressed sensing (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:270:y:2015:i:c:p:777-784

DOI: 10.1016/j.amc.2015.08.082

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