Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory

In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints, and also consider extensions to some structured, nonsmooth problems. The algorithm solves a sequence of (separable) strongly convex problems...

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Vydané v:	IEEE transactions on signal processing Ročník 65; číslo 8; s. 1929 - 1944
Hlavní autori:	Scutari, Gesualdo, Facchinei, Francisco, Lampariello, Lorenzo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 15.04.2017
Predmet:	Ad hoc networks Approximation algorithms Convergence Distributed algorithms Linear programming nonconvex optimization Optimization Signal processing algorithms successive convex approximation
ISSN:	1053-587X, 1941-0476
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