Online gradient descent (OGD) is well-known to be doubly optimal under strong convexity or
monotonicity assumptions:(1) in the single-agent setting, it achieves an optimal regret of Θ …

In this paper, we develop stochastic variance reduced algorithms for solving a class of finite-
sum monotone VI, where the operator consists of the sum of finitely many monotone VI …

This paper considers smooth strongly convex and strongly concave stochastic saddle point
(SSP) problems. Suppose there is an arbitrary oracle that in expectation returns an ϵ …

This paper studies the distributed stochastic Nash equilibrium seeking problem under heavy-
tailed noises. Unlike the traditional stochastic Nash equilibrium algorithms, where the …

In this paper, we consider a Cartesian stochastic variational inequality with a high
dimensional solution space. This mathematical formulation captures a wide range of …

Lower-bound analyses for nonconvex strongly-concave minimax optimization problems
have shown that stochastic first-order algorithms require at least $\mathcal {O}(\varepsilon …

This paper presents a stochastic approximation proximal subgradient (SAPS) method for
stochastic convex-concave minimax optimization. By accessing unbiased and variance …

Stochastic approximation (SA) type methods have been well studied for solving single-stage
stochastic variational inequalities (SVIs). This paper proposes a dynamic stochastic …

The monotone Variational Inequality (VI) is a general model with important applications in
various engineering and scientific domains. In numerous instances, the VI problems are …

In this paper, we delve into the utilization of the negative momentum technique in
constrained minimax games. From an intuitive mechanical standpoint, we introduce a novel …