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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Veröffentlicht in:	IEEE transactions on automatic control 2021-06, Vol.66 (6), p.2787-2794
Hauptverfasser:	Alghunaim, Sulaiman A., Ryu, Ernest K., Yuan, Kun, Sayed, Ali H.
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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