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Distributed stochastic mirror descent algorithm for resource allocation problem
YinghuiWang,ZhipengTu,HuashuQin
0
(Key Lab of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China;University of Chinese Academy of Sciences, Beijing, 100190, China)
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 eff ectiveness of the proposed algorithm.
关键词:  Distributed · Resource allocation problem · Stochastic gradient · Mirror descent
DOI:https://doi.org/10.1007/s11768-020-00018-8
基金项目:This work was supported by the National Key Research and Development Program of China (No. 2016YFB0901900), the National Natural Science Foundation of China (No. 61733018) and the China Special Postdoctoral Science Foundation Funded Project (No. Y990075G21).
Distributed stochastic mirror descent algorithm for resource allocation problem
Yinghui Wang,Zhipeng Tu,Huashu Qin
(Key Lab of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China;University of Chinese Academy of Sciences, Beijing, 100190, China)
Abstract:
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 eff ectiveness of the proposed algorithm.
Key words:  Distributed · Resource allocation problem · Stochastic gradient · Mirror descent