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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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 6; H. 1; S. 53 - 65
Hauptverfasser:	Li, Huaqing, Zheng, Lifeng, Wang, Zheng, Yan, Yu, Feng, Liping, Guo, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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