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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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 3; s. 1 - 15
Hlavní autori:	Jiang, Xia, Zeng, Xianlin, Sun, Jian, Chen, Jie, Xie, Lihua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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