A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization

Based on the idea of randomized coordinate descent of α-averaged operators, a randomized primal-dual optimization algorithm is introduced, where a random subset of coordinates is updated at each iteration. The algorithm builds upon a variant of a recent (deterministic) algorithm proposed by Vũ and...

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Veröffentlicht in:	IEEE transactions on automatic control Jg. 61; H. 10; S. 2947 - 2957
Hauptverfasser:	Bianchi, Pascal, Hachem, Walid, Iutzeler, Franck
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Consensus algorithms Convergence Convex functions coordinate descent Cost function distributed optimization Mathematics Minimization Optimization algorithms Optimization and Control primal-dual algorithm Statistics Yttrium Distributed Optimization Primal-Dual Algorithm Coordinate Descent Consensus algorithms
ISSN:	0018-9286, 1558-2523
Online-Zugang:	Volltext
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