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This paper is focused on a stochastic quasi-variational inequality (SQVI)problem with a continuous and strongly-monotone mapping over a closed andconvex set where the projection onto the constraint set may not be easy tocompute. We present an inexact variance reduced stochastic scheme to solve SQVIproblems and analyzed its convergence rate and oracle complexity. A linear rateof convergence is obtained by progressively increasing sample-size andapproximating the projection operator. Moreover, we show how a competitionamong blood donation organizations can be modeled as an SQVI and we providesome preliminary simulation results to validate our findings.

An Inexact Variance-Reduced Method For Stochastic Quasi-Variational Inequality Problems With An Application In Healthcare