A Primal-Dual Partial Inverse Algorithm for Constrained Monotone Inclusions: Applications to Stochastic Programming and Mean Field Games

In this work, we study a constrained monotone inclusion involving the normal cone to a closed vector subspace and a priori information on primal solutions. We model this information by imposing that solutions belong to the fixed point set of an averaged nonexpansive mapping. We characterize the solu...

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Vydané v:Applied mathematics & optimization Ročník 87; číslo 2; s. 21
Hlavní autori: Briceño-Arias, Luis, Deride, Julio, López-Rivera, Sergio, Silva, Francisco J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.04.2023
Springer Nature B.V
Springer Verlag (Germany)
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ISSN:0095-4616, 1432-0606
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Shrnutí:In this work, we study a constrained monotone inclusion involving the normal cone to a closed vector subspace and a priori information on primal solutions. We model this information by imposing that solutions belong to the fixed point set of an averaged nonexpansive mapping. We characterize the solutions using an auxiliary inclusion that involves the partial inverse operator. Then, we propose the primal-dual partial inverse splitting and we prove its weak convergence to a solution of the inclusion, generalizing several methods in the literature. The efficiency of the proposed method is illustrated in multiple applications including constrained LASSO, stochastic arc capacity expansion problems in transport networks, and variational mean field games with non-local couplings.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0095-4616
1432-0606
DOI:10.1007/s00245-022-09921-9