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"Optimisation with Constraint Learning"

Event details of OPTIMAL Seminar: Ade Fajemisin and Donato Maragno (University of Amsterdam)
Date 13 April 2021
Time 10:00 -12:00


10:00-11:00 - Seminar, open to everyone
11:00-12:00 - Project meeting, by invitation only


Many real-life optimisation problems contain one or more constraints or objectives for which there are no explicit formulae. When there is data available however, one can use this data to learn these. This talk is divided into two parts. In the first part of the talk we will present a proposed framework for optimisation with constraint learning, as well as give an overview of the different approaches we have seen in the literature. We believe that our framework will help to formalise the process of constraint learning, and show the impact that each step of the framework has on the overall process.

In the second part of the talk we will also discuss two methods to learn constraints from data. The first method is based on data sets which contain information on only feasible data points (one-class constraint learning), while the second method contains information on both feasible and infeasible data points (two-class constraint learning). Both the approaches result in multiple linear constraints which do not increase the computational complexity of the final optimisation model.

OPTIMAL Research project

The research project Optimization for and with Machine Learning (OPTIMAL) is an ENW-GROOT research project funded by NWO. It is a collaboration between researchers from Centrum Wiskunde & Informatica (CWI), Delft University of Technology (TU Delft) and the University of Amsterdam (UvA). It started in 2020 and will end in 2025. Research from this project will be shared in various events. For more information about this research project visit the project site and the events calendar.

This seminar will be held via ZOOM. Attendance is possible invitation only. Please send an e-mail to if your are interested in attending this seminar.

A.O. (Ade) Fajemisin

Faculty of Economics and Business

Section Operations Management

Mr D. (Donato) Maragno

Faculty of Economics and Business

Section Operations Management