PMGT-VR: a Decentralized Proximal-gradient Algorithmic Framework with Variance Reduction

This paper considers the decentralized composite optimization problem. We propose a novel decentralized variance-reduction proximal-gradient algorithmic framework, called PMGT-VR, which combines several techniques, including multi-consensus, gradient tracking, and variance reduction. The proposed fr...

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Bibliographic Details

Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 48; no. 1; pp. 1 - 12
Main Authors:	Ye, Haishan, Xiong, Wei, Zhang, Tong
Format:	Journal Article
Language:	English
Published:	United States IEEE 05.09.2025
Subjects:	Complexity theory Convergence Decentralized optimization Loss measurement Network topology Optimization proximal-gradient Servers Symmetric matrices Topology Training variance reduction Vectors
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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