28 citations · 51 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Swann Bessa, Darius Dabert, Max Bourgeat +2
Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bo…
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks
Léo Boisvert, Megh Thakkar, Maxime Gasse +6
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recent LLMs seem capable of planning and reas…
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis, Quentin Cappart, Bistra Dilkina +2
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…
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau +2
Combinatorial optimization has found applications in numerous fields, from aerospace to transportation planning and economics. The goal is to find an optimal solution among a finit…
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem
Antoine François, Quentin Cappart, Louis-Martin Rousseau
Combinatorial optimization is the field devoted to the study and practice of algorithms that solve NP-hard problems. As Machine Learning (ML) and deep learning have popularized, se…
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Quentin Cappart, Emmanuel Goutierre, David Bergman +1
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) hav…