Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization

This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for solving convex composite optimization problems over undirected and connected networks. The local loss function in these problems contains both smooth and nonsmooth terms. The proposed algorithm uses uncoordinated...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 71; S. 1 - 16
Hauptverfasser:	Guo, Luyao, Shi, Xinli, Cao, Jinde, Wang, Zihao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Convergence Decentralized optimization Exact solutions Gradient methods inexact proximal gradient method linear convergence Measurement network independent Network topology Optimization Signal processing algorithms
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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