23 citations · 79 across the 6 of their papers we have counts for
3 papers · 1 filter
Lookback for Learning to Branch
Prateek Gupta, Elias B. Khalil, Didier Chetélat +4
The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Progra…
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…