Proximal Splitting Methods in Signal Processing

被引:1629
作者
Combettes, Patrick L. [1 ]
Pesquet, Jean-Christophe [1 ]
机构
[1] Univ Paris 06, UPMC, CNRS, Lab Jacques Louis Lions,UMR 7598, F-75005 Paris, France
来源
FIXED-POINT ALGORITHMS FOR INVERSE PROBLEMS IN SCIENCE AND ENGINEERING | 2011年 / 49卷
关键词
Alternating-direction method of multipliers; Backward-backward algorithm; Convex optimization; Denoising; Douglas-Rachford algorithm; Forward-backward algorithm; Frame; Landweber method; Iterative thresholding; Parallel computing; Peaceman-Rachford algorithm; Proximal algorithm; Restoration and reconstruction; Sparsity; Splitting; TOTAL VARIATION MINIMIZATION; LINEAR INVERSE PROBLEMS; THRESHOLDING ALGORITHM; IMAGE RECOVERY; VARIATIONAL FORMULATION; CONVERGENCE ANALYSIS; MONOTONE-OPERATORS; NOISE REMOVAL; FIXED-POINTS; DECOMPOSITION;
D O I
10.1007/978-1-4419-9569-8_10
中图分类号
O29 [应用数学];
学科分类号
070104 [应用数学];
摘要
The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex optimization problems, has recently been introduced in the arena of inverse problems and, especially, in signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. These proximal splitting methods are shown to capture and extend several well-known algorithms in a unifying framework. Applications of proximal methods in signal recovery and synthesis are discussed.
引用
收藏
页码:185 / +
页数:8
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