In this paper, we consider nonconvex minimax optimization, which is gaining prominence in
many modern machine learning applications, such as GANs. Large-scale edge-based …

In this paper, we study the nonconvex-strongly-concave minimax optimization problem on
decentralized setting. The minimax problems are attracting increasing attentions because of …

This paper deals with distributed reinforcement learning problems with safety constraints. In
particular, we consider that a team of agents cooperate in a shared environment, where …

We study solution methods for (strongly-) convex-(strongly)-concave Saddle-Point Problems
(SPPs) over networks of two type--master/workers (thus centralized) architectures and mesh …

Abstract Generative Adversarial Networks (GANs) are a powerful class of generative models
in the deep learning community. Current practice on large-scale GAN training utilizes large …

Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in
recent years and has found many scientific and engineering applications. However, a key …

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 …

Variational inequalities are a broad and flexible class of problems that includes
minimization, saddle point, and fixed point problems as special cases. Therefore, variational …

We consider distributed convex-concave saddle point problems over arbitrary connected
undirected networks and propose a decentralized distributed algorithm for their solution. The …

Minimax optimization has seen a surge in interest with the advent of modern applications
such as GANs, and it is inherently more challenging than simple minimization. The difficulty …