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...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 34; no. 9; pp. 5310 - 5321
Main Authors:	Jiang, Xia, Zeng, Xianlin, Sun, Jian, Chen, Jie
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!