54 citations · 99 across the 6 of their papers we have counts for
6 papers
Learning optimal objective values for MILP
Lara Scavuzzo, Karen Aardal, Neil Yorke-Smith
Modern Mixed Integer Linear Programming (MILP) solvers use the Branch-and-Bound algorithm together with a plethora of auxiliary components that speed up the search. In recent years…
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
Lara Scavuzzo, Karen Aardal, Andrea Lodi +1
Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. During the past decades,…
Optimal entanglement distribution policies in homogeneous repeater chains with cutoffs
Álvaro G. Iñesta, Gayane Vardoyan, Lara Scavuzzo +1
We study the limits of bipartite entanglement distribution using a chain of quantum repeaters that have quantum memories. To generate end-to-end entanglement, each node can attempt…
Learning to branch with Tree MDPs
Lara Scavuzzo, Feng Yang Chen, Didier Chételat +4
State-of-the-art Mixed Integer Linear Program (MILP) solvers combine systematic tree search with a plethora of hard-coded heuristics, such as the branching rule. The idea of learni…
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse, Quentin Cappart, Jonas Charfreitag +38
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolatio…
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo +3
We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinator…