Search Results for author: Louis-Martin Rousseau

Found 10 papers, 6 papers with code

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).

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

A prediction-based approach for online dynamic patient scheduling: a case study in radiotherapy treatment

no code implementations16 Dec 2021 Tu-San Pham, Antoine Legrain, Patrick De Causmaecker, Louis-Martin Rousseau

When our prediction-based approach is compared to flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients, while also maintaining comparable waiting times for the other patients.

regression Scheduling

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

Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services

no code implementations3 Feb 2021 Léa Ricard, Guy Desaulniers, Andrea Lodi, Louis-Martin Rousseau

Two types of probabilistic models, namely similarity-based density estimation models and a smoothed Logistic Regression for probabilistic classification model, are compared on a dataset of more than 41, 000 trips and 50 bus routes of the city of Montr\'eal.

Density Estimation Scheduling

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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