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Giulia Zarpellon

Alumni

Publications

Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning
Max B. Paulus
Giulia Zarpellon
Andreas Krause
Laurent Charlin
Chris J. Maddison
Cutting planes are essential for solving mixed-integer linear problems (MILPs), because they facilitate bound improvements on the optimal so… (voir plus)lution value. For selecting cuts, modern solvers rely on manually designed heuristics that are tuned to gauge the potential effectiveness of cuts. We show that a greedy selection rule explicitly looking ahead to select cuts that yield the best bound improvement delivers strong decisions for cut selection - but is too expensive to be deployed in practice. In response, we propose a new neural architecture (NeuralCut) for imitation learning on the lookahead expert. Our model outperforms standard baselines for cut selection on several synthetic MILP benchmarks. Experiments with a B&C solver for neural network verification further validate our approach, and exhibit the potential of learning methods in this setting.
2022-06-28
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
arxiv.org
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
Giulia Zarpellon
Jason Jo
Andrea Lodi
Yoshua Bengio
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learning b… (voir plus)ranching policies for MILP has become an active research area, with most works proposing to imitate the strong branching rule and specialize it to distinct classes of problems. We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization. We propose a novel imitation learning framework, and introduce new input features and architectures to represent branching. Experiments on MILP benchmark instances clearly show the advantages of incorporating an explicit parameterization of the state of the search tree to modulate the branching decisions, in terms of both higher accuracy and smaller B&B trees. The resulting policies significantly outperform the current state-of-the-art method for "learning to branch" by effectively allowing generalization to generic unseen instances.
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse
Simon Bowly
Quentin Cappart
Jonas Charfreitag
Laurent Charlin
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr Kazachkov
Elias Boutros Khalil
Paweł Lichocki
Andrea Lodi
Miles Lubin
Chris J. Maddison
Christopher Morris
D. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost … (voir 22 de plus)
Lara Scavuzzo
Giulia Zarpellon
Linxin Yangm
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Sheng Cheng Shao
Yuanming Zhu
Dong Dong Zhang
Tao Manh Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
C. Binbin
He Minggui
Haoren Ren Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
2021-01-01
NeurIPS (Competition and Demos) (publié)
doi.org
arxiv.org