DISTRIBUTED PROXIMAL-GRADIENT METHOD FOR CONVEX OPTIMIZATION WITH INEQUALITY CONSTRAINTS

We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penal...

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Vydané v:	The ANZIAM journal Ročník 56; číslo 2; s. 160 - 178
Hlavní autori:	LI, JUEYOU, WU, CHANGZHI, WU, ZHIYOU, LONG, QIANG, WANG, XIANGYU
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cambridge, UK Cambridge University Press 01.10.2014
Predmet:	Algorithms Computer simulation Constraints Convergence Inequalities Networks Optimization State estimation proximal-gradient method distributed algorithm convex optimization 90C25 exact penalty function method
ISSN:	1446-1811, 1446-8735
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