Title
An inexact proximal decomposition method for variational inequalities with separable structure
Date Issued
01 January 2021
Access level
open access
Resource Type
journal article
Author(s)
Publisher(s)
EDP Sciences
Abstract
This paper presents an inexact proximal method for solving monotone variational inequality problems with a given separable structure. The proposed algorithm is a natural extension of the Proximal Multiplier Algorithm with Proximal Distances (PMAPD) proposed by Sarmiento et al. [Optimization 65 (2016) 501-537], which unified the works of Chen and Teboulle (PCPM method), and Kyono and Fukushima (NPCPMM) developed for solving convex programs with a particular separable structure. The resulting method combines the recent proximal distances theory introduced by Auslender and Teboulle [SIAM J. Optim. 16 (2006) 697-725] with a decomposition method given by Chen and Teboulle for convex problems and extends the results of the Entropic Proximal Decomposition Method proposed by Auslender and Teboulle, which used to Logarithmic Quadratic proximal distances. Under some mild assumptions on the problem we prove a global convergence of the primal-dual sequences produced by the algorithm.
Start page
S873
End page
S884
Volume
55
Language
English
OCDE Knowledge area
Matemáticas aplicadas
Matemáticas puras
Subjects
Publication version
Version of Record
Scopus EID
2-s2.0-85102082543
Source
RAIRO - Operations Research
ISSN of the container
2804-7303
DOI of the container
10.1051/ro/2020018
Sponsor(s)
Acknowledgements. The first and third authors wish to thank at Callao National University and INNOVATE-PERÚ by supporting research through the CONVENIO 460-INNOVATEPERU-BRI-2015-Perú. The work of the second author was supported by the National Council for Scientific and Technological Development (CNPq) and by grant E-26/200.209/2017, Rio de Janeiro Research Foundation (FAPERJ).
Sources of information:
Directorio de Producción Científica
Scopus