Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

A proximal strictly contractive Peaceman-Rachford splitting method for convex programming with applications to imaging

Language

English

Abstract

© 2015 Society for Industrial and Applied Mathematics.A strictly contractive Peaceman–Rachford splitting method was proposed recently for solving separable convex programming problems. In this paper we further discuss a proximal version of this method, where a subproblem at each iteration is regularized by a proximal point term. The resulting regularized subproblem thus may have closed-form or easily computable solutions, especially in some interesting applications such as a class of sparse and low-rank optimization models. We establish the worst-case convergence rate measured by the iteration complexity in both the ergodic and nonergodic senses for the new algorithm. Some applications arising in image processing are tested to demonstrate the efficiency of the new algorithm.

Keywords

Contraction, Convergence rate, Convex programming, Image processing, Peaceman–Rachford splitting method

Publication Date

2015

Source Publication Title

SIAM Journal on Imaging Sciences

Volume

8

Issue

2

Start Page

1332

End Page

1365

Publisher

Society for Industrial and Applied Mathematics

DOI

10.1137/14099509X

Link to Publisher's Edition

http://dx.doi.org/10.1137/14099509X

ISSN (electronic)

19364954

This document is currently not available here.

Share

COinS