  Variational inequalities are a universal optimization paradigm that is
interesting in itself, but also incorporates classical minimization and saddle
point problems. Modern realities encourage to consider stochastic formulations
of optimization problems. In this paper, we present an analysis of a method
that gives optimal convergence estimates for monotone stochastic finite-sum
variational inequalities. In contrast to the previous works, our method
supports batching and does not lose the oracle complexity optimality. The
effectiveness of the algorithm, especially in the case of small but not single
batches is confirmed experimentally.
