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 …

Starting from where a first course in convex optimization leaves off, this text presents a
unified analysis of first-order optimization methods–including parallel-distributed algorithms …

Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …

We consider the task of decentralized minimization of the sum of smooth strongly convex
functions stored across the nodes of a network. For this problem, lower bounds on the …

The celebrated FedAvg algorithm of McMahan et al.(2017) is based on three components:
client sampling (CS), data sampling (DS) and local training (LT). While the first two are …

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 …

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 …

This work introduces DADAO: the first decentralized, accelerated, asynchronous, primal, first-
order algorithm to minimize a sum of $ L $-smooth and $\mu $-strongly convex functions …

Optimization problems under affine constraints appear in various areas of machine learning.
We consider the task of minimizing a smooth strongly convex function F (x) under the affine …

In this article, we propose a novel dual inexact splitting algorithm (DISA) for distributed
convex composite optimization problems, where the local loss function consists of a smooth …