Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agent...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 9; s. 5310 - 5321
Hlavní autori:	Jiang, Xia, Zeng, Xianlin, Sun, Jian, Chen, Jie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Complexity Complexity analysis Convergence Convexity distributed algorithm Distributed algorithms Distributed databases Machine learning Multiagent systems non-convex finite-sum optimization Optimization Random variables Reduction Signal processing Signal processing algorithms stochastic gradient Stochasticity Sums Tracking variance reduction
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line prístup:	Získať plný text
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