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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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 48; číslo 1; s. 1 - 12
Hlavní autori:	Ye, Haishan, Xiong, Wei, Zhang, Tong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 05.09.2025
Predmet:	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
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