Predict and Optimize: combinatorial optimisation with machine-learning based input
Combinatorial optimisation is used to solve large scale routing, planning and scheduling problems. But how do these techniques work? And what if not all data is known, and want to integrate machine learning predictions?
Praktische info:
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- Inschrijvingen: tot 16 mrt. 2023
- Prijs: Free
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Leertrajecten
In this course, we review different combinatorial optimisation problems (Vehicle Routing, Scheduling and combinatorial puzzles) as well as discrete optimisation techniques for solving them, including Branch and Bound, Integer Programming, Dynamic Programming and Multi-Valued Decision Diagrams, Constraint Programming, Local Search and Hybridization of the aforementioned techniques. A focus will be on the reusability and genericity of the techniques for solving large classes of constrained optimisation problems.
With a better understanding of the solving techniques, we then move to the second part of the course which focuses on ‘Predict and Optimize’ problems, where part of the input to the combinatorial optimisation problem has to be inferred from data. We will review issues such as perception-based solving (e.g. visual sudoku), learning preferences in vehicle routing and end-to-end learning for energy-aware scheduling. We will discuss how to improve either the learning or the solving, as well as the integration of the two to obtain the best results.
Part 1 will be taught by Pierre Schaus, UC Louvain, using material borrowed from his new MOOC on constraint programming.
Part 2 will be taught by Tias Guns, KU Leuven. Links will be provided to example notebooks in the CPMpy constraint modeling library (Github).
Lesgevers / sprekers
Pierre Schaus
Pierre Schaus is a professor of computer science at UCLouvain since 2012 where he teaches the constraint programming course since 2017. He has worked on several constraint programming solvers, used CP to solve real-life applications, and published scientific papers in the major conferences of the CP field (CP, CPAIOR, etc).
Tias Guns
Tias Guns is hoofddocent in de Declaratieve Talen en AI (DTAI) sectie van het Departement Computerwetenschappen. Zijn onderzoek richt zich op de integratie van machine learning technieken met technieken van constraint solving en combinatorische optimisatie.
Hij is secretaris van de associatie voor constraint programming en lid van het executive committee van de Europese machine learning conferentie. Hij ontving in 2021 een prestigieuze ERC grant voor het ontwikkelen van conversationele mensgerichte software voor combinatorische optimisatie.
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