Distributed stochastic mirror descent algorithm for resource allocation problem

• 作者: Yinghui Wang Zhipeng Tu Huashu Qin
• 来源期刊: 控制理论与技术（英文版）
• 年，卷（期）: 2020,（4）
• 页码范围: 339-347
• 页数: 9页
• 语种: 英文
• DOI: 10.1007/s11768-020-00018-8

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.