Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities

We develop a new stochastic algorithm for solving pseudomonotone variational inequalities. Our method builds on Tseng’s forward-backward-forward algorithm, which is known in the deterministic literature to be valuable alternative Korpelevich’s extragradient when inequalities over convex and closed set governed by Lipschitz continuous operators. The main computational advantage of that it relies only single projection step two independent queries oracle. incorporates minibatch sampling mechanism leads almost sure convergence an optimal solution. To best our knowledge, this first look-ahead achieving using at each iteration.