Stochastic Strongly Convex Optimization via Distributed Epoch Stochastic Gradient Algorithm

This article considers the problem of stochastic strongly convex optimization over a network of multiple interacting nodes. The optimization is under a global inequality constraint and the restriction that nodes have only access to the stochastic gradients of their objective functions. We propose an...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 6; s. 2344 - 2357
Hlavní autoři:	Yuan, Deming, Ho, Daniel W. C., Xu, Shengyuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Approximation algorithms Computational geometry Convergence Convergence rate Convex analysis Convex functions Convexity Distributed algorithms distributed stochastic strongly optimization epoch gradient descent inequality constraint Linear programming multiagent systems Nodes Optimization Probability theory Stochastic processes Stochasticity
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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