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

TL;DR

This paper develops verifiable conditions for the existence of solutions to stochastic quasi-variational inequality and complementarity problems, extending previous work to more complex, practical scenarios without requiring explicit expectation calculations.

Contribution

It extends almost-sure sufficiency conditions to stochastic quasi-variational inequalities and complementarity problems, enabling practical existence verification without integration.

Findings

Conditions applicable to unbounded sets and multi-valued maps

Extensions to stochastic complementarity problems with co-coercive maps

Demonstrations on power markets and Nash-Cournot games

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 [44] 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 occurring instances of stochastic quasi-variational inequality problems and stochastic complementarity problems, arising as nonsmooth generalized Nash-Cournot games and power markets, respectively.