Distributed stochastic mirror descent algorithm for resource allocation problem

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...

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Veröffentlicht in:Control theory and technology Jg. 18; H. 4; S. 339 - 347
Hauptverfasser: Wang, Yinghui, Tu, Zhipeng, Qin, Huashu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Guangzhou South China University of Technology and Academy of Mathematics and Systems Science, CAS 01.12.2020
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
Schlagworte:
Complexity
Computational Intelligence
Control
Control and Systems Theory
Engineering
Mechatronics
Optimization
Research Article
Robotics
Systems Theory
Mirror descent
Resource allocation problem
Stochastic gradient
Distributed
ISSN:2095-6983, 2198-0942
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Zusammenfassung: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.
ISSN:2095-6983
2198-0942
DOI:10.1007/s11768-020-00018-8