36 citations · 130 across the 23 of their papers we have counts for
10 papers · 1 filter
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
Laurent Condat, Artavazd Maranjyan, Peter Richtárik
In Distributed optimization and Learning, and even more in the modern framework of federated learning, communication, which is slow and costly, is critical. We introduce LoCoDL, a…
Tikhonov Regularization of Sphere-Valued Signals
Laurent Condat
It is common to have to process signals, whose values are points on the 3-D sphere. We consider a Tikhonov-type regularization model to smoothen or interpolate sphere-valued signal…
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates
Laurent Condat, Peter Richtárik
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…
Tikhonov Regularization of Circle-Valued Signals
Laurent Condat
It is common to have to process signals or images whose values are cyclic and can be represented as points on the complex circle, like wrapped phases, angles, orientations, or colo…
MURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
Laurent Condat, Peter Richtárik
We propose a generic variance-reduced algorithm, which we call MUltiple RANdomized Algorithm (MURANA), for minimizing a sum of several smooth functions plus a regularizer, in a seq…
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Adil Salim, Laurent Condat, Dmitry Kovalev +1
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 affi…
Distributed Proximal Splitting Algorithms with Rates and Acceleration
Laurent Condat, Grigory Malinovsky, Peter Richtárik
We analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rate…
Dualize, Split, Randomize: Toward Fast Nonsmooth Optimization Algorithms
Adil Salim, Laurent Condat, Konstantin Mishchenko +1
We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nons…
Proximal Splitting Algorithms for Convex Optimization: A Tour of Recent Advances, with New Twists
Laurent Condat, Daichi Kitahara, Andrés Contreras +1
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 pr…
A forward-backward view of some primal-dual optimization methods in image recovery
Patrick L. Combettes, Laurent Condat, Jean-Christophe Pesquet +1
A wide array of image recovery problems can be abstracted into the problem of minimizing a sum of composite convex functions in a Hilbert space. To solve such problems, primal-dual…