Distributed Primal-Dual Subgradient Method for Multiagent Optimization via Consensus Algorithms

This paper studies the problem of optimizing the sum of multiple agents' local convex objective functions, subject to global convex inequality constraints and a convex state constraint set over a network. Through characterizing the primal and dual optimal solutions as the saddle points of the L...

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Veröffentlicht in:	IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Jg. 41; H. 6; S. 1715 - 1724
Hauptverfasser:	Deming Yuan, Shengyuan Xu, Huanyu Zhao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2011
Schlagworte:	Algorithm design and analysis Approximation algorithms Average consensus Convergence Convex functions convex optimization distributed optimization Lagrangian functions Optimization subgradient method Upper bound
ISSN:	1083-4419, 1941-0492
Online-Zugang:	Volltext
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