Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framework that unifies many existing state-of-the-ar...

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Vydané v:	IEEE transactions on automatic control Ročník 66; číslo 6; s. 2787 - 2794
Hlavní autori:	Alghunaim, Sulaiman A., Ryu, Ernest K., Yuan, Kun, Sayed, Ali H.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Convergence Convex functions Cost function Decentralized optimization diffusion Electronic mail gradient tracking linear convergence Multiagent systems Optimization proximal gradient algorithms Symmetric matrices unified decentralized algorithm
ISSN:	0018-9286, 1558-2523
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