In this work, we consider the optimization formulation of personalized federated learning
recently introduced by Hanzely & Richtarik (2020) which was shown to give an alternative …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

This article presents a powerful algorithmic framework for big data optimization, called the
block successive upper-bound minimization (BSUM). The BSUM includes as special cases …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

This paper establishes global convergence and provides global bounds of the rate of
convergence for the Heavy-ball method for convex optimization. When the objective function …

Decentralized optimization to minimize a finite sum of functions, distributed over a network of
nodes, has been a significant area within control and signal-processing research due to its …

We propose a new randomized coordinate descent method for minimizing the sum of
convex functions each of which depends on a small number of coordinates only. Our method …

We consider a generic convex optimization problem associated with regularized empirical
risk minimization of linear predictors. The problem structure allows us to reformulate it as a …

In this paper, we consider a class of finite-sum convex optimization problems whose
objective function is given by the average of m\,(≥ 1) m (≥ 1) smooth components together …

Decentralized methods to solve finite-sum minimization problems are important in many
signal processing and machine learning tasks where the data samples are distributed …