In this work, we study the computational complexity of reducing the squared gradient
magnitude for smooth minimax optimization problems. First, we present algorithms with …

In this paper we study the convex-concave saddle-point problem $\min_x\max_y f (x)+
y^\top\mathbf {A} xg (y) $, where $ f (x) $ and $ g (y) $ are smooth and convex functions. We …

We study structured convex optimization problems, with additive objective $ r:= p+ q $,
where $ r $ is ($\mu $-strongly) convex, $ q $ is $ L_q $-smooth and convex, and $ p $ is …

We study the bilinearly coupled minimax problem: $\min_ {x}\max_ {y} f (x)+ y^\top A xh (y) $,
where $ f $ and $ h $ are both strongly convex smooth functions and admit first-order …

We study finite-sum distributed optimization problems involving a master node and $ n-1$
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …

This paper is a survey of methods for solving smooth,(strongly) monotone stochastic
variational inequalities. To begin with, we present the deterministic foundation from which …

We consider strongly-convex-strongly-concave saddle-point problems with general non-
bilinear objective and different condition numbers with respect to the primal and the dual …

Several new accelerated methods in minimax optimization and fixed-point iterations have
recently been discovered, and, interestingly, they rely on a mechanism distinct from …

Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear
models with affine constraints. In this paper, we study distributed saddle-point problems with …

We introduce an inexact oracle model for variational inequalities with monotone operators,
propose a numerical method that solves such variational inequalities, and analyze its …