A proximal Peaceman–Rachford splitting method for compressive sensing

被引：17

作者：

Sun M. ^{[1
]}

Liu J. ^{[2
]}

机构：

[1] School of Mathematics and Statistics, Zaozhuang University, Zaozhuang, 277160, Shandong

[2] School of Mathematics and Statistics, Zhejiang University of Finance and Economics, Hangzhou

来源：

Journal of Applied Mathematics and Computing | 2016年 / 50卷 / 1-2期

关键词：

Compressive sensing; Global convergence; Proximal Peaceman–Rachford splitting method;

D O I：

10.1007/s12190-015-0874-x

中图分类号：

学科分类号：

摘要：

Recently, He et al. proposed a modified Peaceman–Rachford splitting method (MPRSM) for separable convex programming, which includes compressive sensing (CS) as a special case. In this paper, we further study MPRSM for CS, and regularize its first subproblem by the proximal regularization. Thus the computational load of the subproblem is substantially alleviated. That is, it is easy enough to have a closed-form solution for CS. Convergence of the new method can be guaranteed under the same assumptions as MPRSM. Finally, numerical results, including comparisons with MPPSM are reported to demonstrate the efficiency of the new method. © 2015, Korean Society for Computational and Applied Mathematics.

引用

页码：349 / 363

页数：14

共 11 条

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