S-DIGing: A Stochastic Gradient Tracking Algorithm for Distributed Optimization

In this article, we study convex optimization problems where agents of a network cooperatively minimize the global objective function which consists of multiple local objective functions. The intention of this work is to solve large-scale optimization problems where the local objective function is c...

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Vydané v:	IEEE transactions on emerging topics in computational intelligence Ročník 6; číslo 1; s. 53 - 65
Hlavní autori:	Li, Huaqing, Zheng, Lifeng, Wang, Zheng, Yan, Yu, Feng, Liping, Guo, Jing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Computational geometry Convergence Convex functions Convexity Distributed optimization gradient tracking linear convergence Linear programming Machine learning algorithms multi-agent systems Optimization stochastic averaging gradient Stochastic processes Tracking Upper bounds
ISSN:	2471-285X, 2471-285X
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