72 citations
- CentraleSupélecFR26 papers
- Institut Camille JordanFR10 papers
- Université Paris-SaclayFR9 papers
- Laboratoire Génie IndustrielFR6 papers
- Centre de Vision NumériqueFR4 papers
- Lyon 1 UniversitéFR4 papers
- OPIS: OPtimisation Imagerie et SantéFR4 papers
- Centre Inria de SaclayFR3 papers
- McGill UniversityCA3 papers
- Assistance Publique – Hôpitaux de ParisFR2 papers
- Bicêtre HospitalFR2 papers
- École Centrale ParisFR2 papers
40 papers
New Formulations and Discretization Insights for the Electric Autonomous Dial-a-Ride Problem
Boshuai Zhao, Adam Abdin, Jakob Puchinger
The Electric Autonomous Dial-a-Ride Problem (E-ADARP) involves routing and scheduling electric autonomous vehicles under battery capacity and partial recharging constraints, aiming…
The Bi-objective Electric Autonomous Dial-a-Ride Problem
Yue Su, Sophie N. Parragh, Nicolas Dupin +1
The electric autonomous dial-a-ride problem (E-ADARP) introduces electric, autonomously driving vehicles and their unique requirements into the classic dial-a-ride problem, where p…
Training Neural Networks at Any Scale
Thomas Pethick, Kimon Antonakopoulos, Antonio Silveti-Falls +2
This article reviews modern optimization methods for training neural networks with an emphasis on efficiency and scale. We present state-of-the-art optimization algorithms under a…
Analyse comparative d'algorithmes de restauration en architecture dépliée pour des signaux chromatographiques parcimonieux
Mouna Gharbi, Silvia Villa, Emilie Chouzenoux +2
Data restoration from degraded observations, of sparsity hypotheses, is an active field of study. Traditional iterative optimization methods are now complemented by deep learning t…
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…
Training Deep Learning Models with Norm-Constrained LMOs
Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos +3
In this work, we study optimization methods that leverage the linear minimization oracle (LMO) over a norm-ball. We propose a new stochastic family of algorithms that uses the LMO…