In game-theoretic learning, several agents are simultaneously following their individual
interests, so the environment is non-stationary from each player's perspective. In this context …

We investigate a structured class of nonconvex-nonconcave min-max problems exhibiting
so-called\emph {weak Minty} solutions, a notion which was only recently introduced, but is …

We study the problem of minimizing a relatively-smooth convex function using stochastic
Bregman gradient methods. We first prove the convergence of Bregman Stochastic Gradient …

We present a new family of min-max optimization algorithms that automatically exploit the
geometry of the gradient data observed at earlier iterations to perform more informative extra …

Adaptive algorithms like AdaGrad and AMSGrad are successful in nonconvex optimization
owing to their parameter-agnostic ability–requiring no a priori knowledge about problem …

We consider distributed stochastic variational inequalities (VIs) on unbounded domains with
the problem data that is heterogeneous (non-IID) and distributed across many devices. We …

This article provides a novel projection neural network (PNN) for a category of monotone
variational inequality (MVI). For simplifying calculation, the feasible region of MVI is …

In this work, we propose a universal and adaptive second-order method for minimization of
second-order smooth, convex functions. Precisely, our algorithm achieves $ O (\sigma/\sqrt …

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 …

In this paper, we propose a general algorithmic framework for the first-order methods in
optimization in a broad sense, including minimization problems, saddle-point problems and …