Distributed stochastic mirror descent algorithm for resource allocation problem
Yinghui Wang
Zhipeng Tu
Huashu Qin
1.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,China2.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,China3.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 effectiveness of the proposed algorithm.
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论文发表日期：2020-11-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 339-347 )
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