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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Vydané v:	IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Ročník 41; číslo 6; s. 1715 - 1724
Hlavní autori:	Deming Yuan, Shengyuan Xu, Huanyu Zhao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.12.2011
Predmet:	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
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