Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization

We introduce a new framework for the convergence analysis of a class of distributed constrained non-convex optimization algorithms in multi-agent systems. The aim is to search for local minimizers of a non-convex objective function which is supposed to be a sum of local utility functions of the agen...

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Bibliographic Details
Published in:	IEEE transactions on automatic control Vol. 58; no. 2; pp. 391 - 405
Main Authors:	Bianchi, P., Jakubowicz, J.
Format:	Journal Article
Language:	English
Published:	IEEE 01.02.2013 Institute of Electrical and Electronics Engineers
Subjects:	Algorithm design and analysis Computer Science Convergence Convergence of numerical methods distributed algorithms gradient methods multiagent systems Networking and Internet Architecture Nickel Noise Optimization Stochastic processes Vectors Gradient methods Convergence of numerical methods Multiagent systems Distributed algorithms
ISSN:	0018-9286, 1558-2523
Online Access:	Get full text
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