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Adrian Thibault Etienne Jarret

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Related publications (4)

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A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO

Matthieu Martin Jean-André Simeoni, Adrian Thibault Etienne Jarret

We propose a fast and scalable Polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. The latter improves upon traditional Frank-Wolfe methods by considering generalized greedy steps with polyatomic (i.e. ...

IEEE Institute of Electrical and Electronics Engineers2022

PolyCLEAN: Atomic optimization for super-resolution imaging and uncertainty estimation in radio interferometry

Julien René Fageot, Matthieu Martin Jean-André Simeoni, Sepand Kashani, Adrian Thibault Etienne Jarret, Joan Rue Queralt

Context. Imaging in radio interferometry requires solving an ill-posed noisy inverse problem, for which the most adopted algorithm is the original CLEAN algorithm and its variants. Alternative explicit optimization methods have gained increasing attention, ...

EDP SCIENCES S A2025

A Decoupled Approach for Composite Sparse-plus-Smooth Penalized Optimization

Julien René Fageot, Adrian Thibault Etienne Jarret

We consider a linear inverse problem whose solution is expressed as a sum of two components: one smooth and the other sparse. This problem is addressed by minimizing an objective function with a least squares data-fidelity term and a different regularizati ...

2024

Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions

Julien René Fageot, Matthieu Martin Jean-André Simeoni, Adrian Thibault Etienne Jarret

Nous nous intéressons à la reconstruction parcimonieuse d’images à l’aide du problème d’optimisation régularisé LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets à reconstruire limitent, voire empêchent, l’utilisation des ...

2022