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 …

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

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

This paper discusses the efficiency of Hybrid Primal-Dual (HPD) type algorithms to
approximately solve discrete Optimal Transport (OT) and Wasserstein Barycenter (WB) …

Alternating minimization (AM) procedures are practically efficient in many applications for
solving convex and non-convex optimization problems. On the other hand, Nesterov's …

We consider smooth convex-concave saddle point problems in the decentralized distributed
setting, where a finite-sum objective is distributed among the nodes of a computational …

Variational inequalities in general and saddle point problems in particular are increasingly
relevant in machine learning applications, including adversarial learning, GANs, transport …

By choosing a suitable function space as the dual to the non-negative measure cone, we
study in a unified framework a class of functional saddle-point optimization problems, which …

The Wasserstein barycenter has been widely studied in various fields, including natural
language processing, and computer vision. However, it requires a high computational cost …

Computing Wasserstein barycenters of discrete measures has recently attracted
considerable attention due to its wide variety of applications in data science. In general, this …