Distributed stochastic mirror descent algorithm for resource allocation problem

被引：3
|
作者：
Wang, Yinghui [1 ,2 ]
Tu, Zhipeng [1 ,2 ]
Qin, Huashu [1 ,2 ]
机构：
[1] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100190, Peoples R China
来源：
CONTROL THEORY AND TECHNOLOGY | 2020年 / 18卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Distributed; Resource allocation problem; Stochastic gradient; Mirror descent; ECONOMIC-DISPATCH; INITIALIZATION; OPTIMIZATION; NETWORKS;
D O I：
10.1007/s11768-020-00018-8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we consider a distributed resource allocation problem of minimizing a global convex function formed by a sum of local convex functions with coupling constraints. Based on neighbor communication and stochastic gradient, a distributed stochastic mirror descent algorithm is designed for the distributed resource allocation problem. Sublinear convergence to an optimal solution of the proposed algorithm is given when the second moments of the gradient noises are summable. A numerical example is also given to illustrate the effectiveness of the proposed algorithm.
引用
收藏
页码：339 / 347
页数：9
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