We introduce ProxSkip—a surprisingly simple and provably efficient method for minimizing
the sum of a smooth ($ f $) and an expensive nonsmooth proximable ($\psi $) function. The …

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 design accelerated algorithms with improved rates for several fundamental classes of
optimization problems. Our algorithms all build upon techniques related to the analysis of …

Variational inequalities are a formalism that includes games, minimization, saddle point, and
equilibrium problems as special cases. Methods for variational inequalities are therefore …

While numerous effective decentralized algorithms have been proposed with theoretical
guarantees and empirical successes, the performance limits in decentralized optimization …

We consider the task of minimizing the sum of smooth and strongly convex functions stored
in a decentralized manner across the nodes of a communication network whose links are …

This paper considers the decentralized convex optimization problem, which has a wide
range of applications in large-scale machine learning, sensor networks, and control theory …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …

In this paper, we focus on solving the decentralized optimization problem of minimizing the
sum of n objective functions over a multi-agent network. The agents are embedded in an …

Inspired by a recent breakthrough of Mishchenko et al.[2022], who for the first time showed
that local gradient steps can lead to provable communication acceleration, we propose an …