Event-Triggered Distributed Stochastic Mirror Descent for Convex Optimization

This article is concerned with the distributed convex constrained optimization over a time-varying multiagent network in the non-Euclidean sense, where the bandwidth limitation of the network is considered. To save the network resources so as to reduce the communication costs, we apply an event-trig...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 9; s. 6480 - 6491
Hlavní autori:	Xiong, Menghui, Zhang, Baoyong, Ho, Daniel W. C., Yuan, Deming, Xu, Shengyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bandwidth Bregman divergence Constraints Convergence Convex functions Convexity Distance measurement distributed convex optimization Divergence event-triggered communication strategy Machine learning algorithms Mirrors Multiagent systems Optimization stochastic mirror descent algorithm Stochasticity Upper bound Upper bounds
ISSN:	2162-237X, 2162-2388, 2162-2388
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