Paper Info
Title
A fast proximal point algorithm for ℓ1-minimization problem in compressed sensing.
Abstract
In this paper, a fast proximal point algorithm (PPA) is proposed for solving ¿1-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.
Year
DOI
Venue
2015
10.1016/j.amc.2015.08.082
Applied Mathematics and Computation
Keywords
DocType
Volume
Proximal point algorithm,ℓ1-regularized least square,Compressed sensing
Journal
270
Issue
ISSN
Citations 
C
0096-3003
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Yun Zhu100.34
Jian Wu2716.69
Gaohang Yu318914.51