activity
20072026
most citedA Proximal Decomposition Method for Solving Convex Variational Inverse Problems

208 citations · 1.7k across the 69 of their papers we have counts for

collaborators

79 papers

math.OC2026

Nonlinear Forward-Backward Algorithm for Solving Non-monotone+Lipschitz Inclusions with Applications to Adjoint Mismatch Problems

Jean-Christophe Pesquet, Fernando Roldán

This article presents a novel algorithmic framework for solving a broad class of nonlinear inclusion problems involving non-monotone and Lipschitz continuous operators. Our primary…

math.OC2026

Variable Bregman Majorization-Minimization algorithms for nonconvex nonsmooth optimization, with application to Poisson imaging

Maxence Adly, Alix Chazottes, Emilie Chouzenoux +2

In this work, we introduce a unifying Bregman-based majorization-minimization (MM) framework for solving nonconvex nonsmooth optimization problems. The proposed approach leverages…

math.OC2025

An adaptive forward-backward-forward splitting algorithm for solving pseudo-monotone inclusions

Flavia Chorobura, Ion Necoara, Jean-Christophe Pesquet

In this paper, we propose an adaptive forward-backward-forward splitting algorithm for finding a zero of a pseudo-monotone operator which is split as a sum of three operators: the…

cs.LG2025

Variable Bregman Majorization-Minimization Algorithm and its Application to Dirichlet Maximum Likelihood Estimation

Ségolène Martin, Jean-Christophe Pesquet, Gabriele Steidl +1

We propose a novel Bregman descent algorithm for minimizing a convex function that is expressed as the sum of a differentiable part (defined over an open set) and a possibly nonsmo…

math.OC2024★ 1 cited

Stability Bounds for the Unfolded Forward-Backward Algorithm

Emilie Chouzenoux, Cecile Della Valle, Jean-Christophe Pesquet

We consider a neural network architecture designed to solve inverse problems where the degradation operator is linear and known. This architecture is constructed by unrolling a for…

cs.CV2024

UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning

Long Zhou, Fereshteh Shakeri, Aymen Sadraoui +3

Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistic…