Stochastic Proximal Gradient Consensus Over Random Networks

We consider solving a convex optimization problem with possibly stochastic gradient, and over a randomly time-varying multiagent network. Each agent has access to some local objective function, and it only has unbiased estimates of the gradients of the smooth component. We develop a dynamic stochast...

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Vydané v:	IEEE transactions on signal processing Ročník 65; číslo 11; s. 2933 - 2948
Hlavní autori:	Hong, Mingyi, Chang, Tsung-Hui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2017
Predmet:	ADMM Algorithm design and analysis Convergence Convex functions Distributed optimization fast algorithms Heuristic algorithms Linear programming Optimization rate analysis Signal processing algorithms
ISSN:	1053-587X, 1941-0476
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