We propose an extragradient method with stepsizes bounded away from zero for stochastic
variational inequalities requiring only pseudomonotonicity. We provide convergence and …

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 …

In this paper we first present a novel operator extrapolation (OE) method for solving
deterministic variational inequality (VI) problems. Similar to the gradient (operator) projection …

In this paper, we propose dynamic sampled stochastic approximated (DS-SA) extragradient
methods for stochastic variational inequalities (SVIs) that are robust with respect to an …

We consider a class of optimization problems with Cartesian variational inequality (CVI)
constraints, where the objective function is convex and the CVI is associated with a …

We consider the stochastic variational inequality problem in which the map is expectation-
valued in a component-wise sense. Much of the available convergence theory and rate …

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 develop a new stochastic algorithm for solving pseudomonotone stochastic variational
inequalities. Our method builds on Tseng's forward-backward-forward algorithm, which is …

In this paper, we consider stochastic monotone Nash games where each player's strategy
set is characterized by possibly a large number of explicit convex constraint inequalities …

We propose a new framework for solving the convex bilevel optimization problem, where
one optimizes a convex objective over the optimal solutions of another convex optimization …