We propose stochastic variance reduced algorithms for solving convex-concave saddle
point problems, monotone variational inequalities, and monotone inclusions. Our framework …

Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …

This paper introduces a family of stochastic extragradient-type algorithms for a class of
nonconvex-nonconcave problems characterized by the weak Minty variational inequality …

This paper studies the decentralized stochastic optimization problem over an undirected
network, where each agent owns its local private functions made up of two non-smooth …

We examine the problem of regret minimization when the learner is involved in a continuous
game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is …

Motivated by the training of generative adversarial networks (GANs), we study methods for
solving minimax problems with additional nonsmooth regularizers. We do so by employing …

We solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone
games with expected value cost functions. Specifically, we present the first distributed SGNE …

In this paper, we propose a variable sample-size optimistic mirror descent algorithm under
the Bregman distance for a class of stochastic mixed variational inequalities. Different from …

We develop two novel stochastic variance-reduction methods to approximate a solution of
root-finding problems applicable to both equations and inclusions. Our algorithms leverage …

In this paper, we propose a fast stochastic approximation-based subgradient extragradient
algorithm with variance reduction for solving the stochastic variational inequality, where the …