A Projection-Based Algorithm for Solving Stochastic Inverse Variational Inequality Problems

We consider a stochastic Inverse Variational Inequality (IVI) problem defined by a continuous and cocoercive map over a closed and convex set. Motivated by the absence of performance guarantees for stochastic IVI, we present a variance-reduced projection-based gradient method. Our proposed method ensures an almost sure convergence of the generated iterates to the solution, and we establish a convergence rate guarantee. To verify our results, we apply the proposed algorithm to a network equilibrium control problem.