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A class of Benders decomposition methods for variational inequalities

  • Juan Pablo Luna
  • Claudia Sagastizábal
  • Mikhail SolodovEmail author
Article
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Abstract

We develop new variants of Benders decomposition methods for variational inequality problems. The construction is done by applying the general class of Dantzig–Wolfe decomposition of Luna et al. (Math Program 143(1–2):177–209, 2014) to an appropriately defined dual of the given variational inequality, and then passing back to the primal space. As compared to previous decomposition techniques of the Benders kind for variational inequalities, the following improvements are obtained. Instead of rather specific single-valued monotone mappings, the framework includes a rather broad class of multi-valued maximally monotone ones, and single-valued nonmonotone. Subproblems’ solvability is guaranteed instead of assumed, and approximations of the subproblems’ mapping are allowed (which may lead, in particular, to further decomposition of subproblems, which may otherwise be not possible). In addition, with a certain suitably chosen approximation, variational inequality subproblems become simple bound-constrained optimization problems, thus easier to solve.

Keywords

Variational inequalities Benders decomposition Dantzig–Wolfe decomposition Stochastic Nash games 

Mathematics Subject Classification

90C33 65K10 49J53 

Notes

Acknowledgements

The authors thank the two referees for their useful comments. All the three authors are supported by the FAPERJ Grant 203.052/2016 and by FAPERJ Grant E-26/210.908/2016 (PRONEX–Optimization). The second author’s research is also supported by CNPq Grant 303905/2015-8, by Gaspard Monge Visiting Professor Program, and by EPSRC Grant No. EP/ R014604/1 (Isaac Newton Institute for Mathematical Sciences, programme Mathematics for Energy Systems). The third author is also supported by CNPq Grant 303724/2015-3, and by Russian Foundation for Basic Research Grant 19-51-12003 NNIOa.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Juan Pablo Luna
    • 1
  • Claudia Sagastizábal
    • 2
  • Mikhail Solodov
    • 3
    Email author
  1. 1.COPPE-UFRJ, Engenharia de ProduçãoRio de JaneiroBrazil
  2. 2.IMECC - UNICAMPCampinasBrazil
  3. 3.IMPA – Instituto de Matemática Pura e AplicadaRio de JaneiroBrazil

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