Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming

no code implementations • 8 Feb 2024 • Lara Scavuzzo, Karen Aardal, Andrea Lodi, Neil Yorke-Smith

We also address how to represent MILPs in the context of applying learning algorithms, MILP benchmarks and software.

Learning to branch with Tree MDPs

1 code implementation • 23 May 2022 • Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen Aardal

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.

Reinforcement Learning (RL)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

2 code implementations • 4 Mar 2022 • Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun

Combinatorial optimization is a well-established area in operations research and computer science.

BIG-bench Machine Learning Combinatorial Optimization

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

2 code implementations • NeurIPS Workshop LMCA 2020 • Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi

We present Ecole, a new library to simplify machine learning research for combinatorial optimization.

BIG-bench Machine Learning Combinatorial Optimization +1