4 papers
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Stéphane Eilles-Chan Way, Hugo Percot, Quentin Cappart +2
Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models. However, its practical depl…
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…
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…
An Exact Framework for Solving the Space-Time Dependent TSP
Isaac Rudich, Quentin Cappart, Manuel López-Ibáñez +2
Many real-world scenarios involve solving bi-level optimization problems in which there is an outer discrete optimization problem, and an inner problem involving expensive or black…