Distributed Stochastic Proximal Algorithm With Random Reshuffling for Nonsmooth Finite-Sum Optimization

The nonsmooth finite-sum minimization is a fundamental problem in machine learning. This article develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multiagent networks. The objective function is a sum of differ...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 3; S. 1 - 15
Hauptverfasser:	Jiang, Xia, Zeng, Xianlin, Sun, Jian, Chen, Jie, Xie, Lihua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Convergence Convex functions Costs Distributed optimization Machine learning Machine learning algorithms Minimization Multiagent systems Objective function Optimization proximal operator random reshuffling (RR) Regularization stochastic algorithm Stochasticity Sums time-varying graphs
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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