Asynchronous parallel algorithms for nonconvex optimization

We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of the sum of a smooth nonconvex function and a nonsmooth convex one, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a no...

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Vydáno v:Mathematical programming Ročník 184; číslo 1-2; s. 121 - 154
Hlavní autoři: Cannelli, Loris, Facchinei, Francisco, Kungurtsev, Vyacheslav, Scutari, Gesualdo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
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ISSN:0025-5610, 1436-4646
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Shrnutí:We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of the sum of a smooth nonconvex function and a nonsmooth convex one, subject to both convex and nonconvex constraints. The proposed framework hinges on successive convex approximation techniques and a novel probabilistic model that captures key elements of modern computational architectures and asynchronous implementations in a more faithful way than current state-of-the-art models. Other key features of the framework are: (1) it covers in a unified way several specific solution methods; (2) it accommodates a variety of possible parallel computing architectures; and (3) it can deal with nonconvex constraints. Almost sure convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup when the number of workers is not too large.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:0025-5610
1436-4646
DOI:10.1007/s10107-019-01408-w