Parallel computing proximal method for nonsmooth convex optimization with fixed point constraints of quasi-nonexpansive mappings

被引：0
|
作者：
Shimizu K. [1 ]
Hishinuma K. [1 ]
Iiduka H. [2 ]
机构：
[1] Computer Science Course, Graduate School of Science and Technology, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa
[2] Department of Computer Science, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki-shi, Kanagawa
来源：
Applied Set-Valued Analysis and Optimization | 2020年 / 2卷 / 01期
基金：
日本学术振兴会;
关键词：
Fixed point; Nonsmooth convex optimization; Parallel computing; Proximal method; Quasi-nonexpansive mapping;
D O I：
10.23952/asvao.2.2020.1.01
中图分类号：
学科分类号：
摘要：
We present a parallel computing proximal method for solving the problem of minimizing the sum of convex functions over the intersection of fixed point sets of quasi-nonexpansive mappings in a real Hilbert space. We also provide a convergence analysis of the method for constant and diminishing step sizes under certain assumptions as well as a convergence-rate analysis for a diminishing step size. Numerical comparisons show that the performance of the algorithm is comparable with existing subgradient methods. ©2020 Applied Set-Valued Analysis and Optimization
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页码：1 / 17
页数：16
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