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Mathematical Programming

, Volume 165, Issue 1, pp 291–330 | Cite as

On the existence of solutions to stochastic quasi-variational inequality and complementarity problems

  • Uma Ravat
  • Uday V. ShanbhagEmail author
Full Length Paper Series B

Abstract

Variational inequality problems allow for capturing an expansive class of problems, including convex optimization problems, convex Nash games and economic equilibrium problems, amongst others. Yet in most practical settings, such problems are complicated by uncertainty, motivating the examination of a stochastic generalization of the variational inequality problem and its extensions in which the components of the mapping contain expectations. When the associated sets are unbounded, ascertaining existence requires having access to analytical forms of the expectations. Naturally, in practical settings, such expressions are often difficult to derive, severely limiting the applicability of such an approach. Consequently, our goal lies in developing techniques that obviate the need for integration and our emphasis lies in developing tractable and verifiable sufficiency conditions for claiming existence. We begin by recapping almost-sure sufficiency conditions for stochastic variational inequality problems with single-valued maps provided in our prior work Ravat and Shanbhag (in: Proceedings of the American Control Conference (ACC), 2010), Ravat and Shanbhag (SIAM J Optim 21: 1168–1199, 2011) and provide extensions to multi-valued mappings. Next, we extend these statements to quasi-variational regimes where maps can be either single or set-valued. Finally, we refine the obtained results to accommodate stochastic complementarity problems where the maps are either general or co-coercive. The applicability of our results is demonstrated on practically occuring instances of stochastic quasi-variational inequality problems and stochastic complementarity problems, arising as nonsmooth generalized Nash-Cournot games and power markets, respectively.

Mathematics Subject Classification

90C15 90C33 

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Copyright information

© Springer-Verlag GmbH Germany and Mathematical Optimization Society 2017

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Industrial and Manufacturing EngineeringPennsylvania State UniversityUniversity ParkUSA

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