Search Results for author: Quentin Cappart

Found 13 papers, 10 papers with code

Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches

1 code implementation9 Mar 2024 Léo Boisvert, Hélène Verhaeghe, Quentin Cappart

In response to this challenge, this paper advocates for progress toward a fully generic representation of combinatorial problems for learning-based approaches.

Learning Lagrangian Multipliers for the Travelling Salesman Problem

no code implementations22 Dec 2023 Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron Ferber, Louis-Martin Rousseau

Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

1 code implementation14 Dec 2023 Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian Schiffer

We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit.

counterfactual

Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver

1 code implementation5 Jan 2023 Tom Marty, Tristan François, Pierre Tessier, Louis Gauthier, Louis-Martin Rousseau, Quentin Cappart

Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time.

Q-Learning Variable Selection

SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning

1 code implementation18 Feb 2021 Félix Chalumeau, Ilan Coulon, Quentin Cappart, Louis-Martin Rousseau

This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning.

BIG-bench Machine Learning Combinatorial Optimization +2

Learning the Travelling Salesperson Problem Requires Rethinking Generalization

4 code implementations12 Jun 2020 Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.

Combinatorial Optimization Transfer Learning +1

How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem

1 code implementation28 Sep 2019 Antoine François, Quentin Cappart, Louis-Martin Rousseau

In this paper, we address the limitations of ML approaches for solving the TSP and investigate two fundamental questions: (1) how can we measure the level of accuracy of the pure ML component of such methods; and (2) what is the impact of a search procedure plugged inside a ML model on the performances?

BIG-bench Machine Learning Combinatorial Optimization

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