Random Coordinate Descent Algorithms for Multi-Agent Convex Optimization Over Networks

In this paper, we develop randomized block-coordinate descent methods for minimizing multi-agent convex optimization problems with linearly coupled constraints over networks and prove that they obtain in expectation an ε accurate solution in at most O(1/λ 2 (Q)ϵ) iterations, where λ 2 (Q) is the sec...

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Veröffentlicht in:	IEEE transactions on automatic control Jg. 58; H. 8; S. 2001 - 2012
1. Verfasser:	Necoara, Ion
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.08.2013
Schlagworte:	Algorithm design and analysis Convergence Convex functions Distributed control Eigenvalues and eigenfunctions Linear programming linearly coupled constraints Optimization random coordinate descent rate of convergence resource allocation problems smooth convex optimization Vectors
ISSN:	0018-9286, 1558-2523
Online-Zugang:	Volltext
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