Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions

被引：6
|
作者：
Cevher, Volkan [1 ]
Vu, Bang Cong [1 ]
Yurtsever, Alp [1 ]
机构：
[1] Ecole Polytech Fed Lausanne, Lausanne, Switzerland
来源：
LARGE-SCALE AND DISTRIBUTED OPTIMIZATION | 2018年 / 2227卷
关键词：
Monotone inclusion; Monotone operator; Operator splitting; Cocoercive; Forward backward algorithm; Composite operator; Duality; Primal-dual algorithm; SURE CONVERGENCE; ALGORITHM;
D O I：
10.1007/978-3-319-97478-1_7
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We propose a stochastic Forward-Douglas-Rachford Splitting framework for finding a zero point of the sum of three maximally monotone operators, one of which is cocoercive, in a real separable Hilbert space. We characterize the rate of convergence in expectation for strongly monotone operators. We further provide guidance on step-size sequence selection that achieve this rate, even when the strong convexity parameter is unknown.
引用
收藏
页码：149 / 179
页数：31
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