Proximal nested primal-dual gradient algorithms for distributed constraint-coupled composite optimization

被引：2
|
作者：
Li, Jingwang [1 ]
An, Qing [2 ]
Su, Housheng [1 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Image Proc & Intelligent Control Key Lab, Educ Minist China, Luoyu Rd 1037, Wuhan 430074, Peoples R China
[2] Wuchang Univ Technol, Articial Intelligence Sch, Wuhan 430223, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2023年 / 444卷
基金：
中国国家自然科学基金;
关键词：
Constraint-coupled optimization; Non-smooth function; Proximal operator; Primal-dual gradient algorithm; RESOURCE-ALLOCATION; ECONOMIC-DISPATCH; CONVERGENCE;
D O I：
10.1016/j.amc.2022.127801
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we study a class of distributed constraint-coupled optimization problems, where each local function is composed of a smooth and strongly convex function and a convex but possibly non-smooth function. We design a novel proximal nested primal-dual gradient algorithm (Prox-NPGA), which is a generalized version of the exiting algorithm- NPGA. The convergence of Prox-NPGA is proved and the upper bounds of the step-sizes are given. Finally, numerical experiments are employed to verify the theoretical results and compare the convergence rates of different versions of Prox-NPGA.(c) 2022 Elsevier Inc. All rights reserved.
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页数：12
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