Master Class

Recent Advances in Optimisation Paradigms and Solving Technology

The CPAIOR Master Class is an annual one-day event (which will be held on September 21 this year) to disseminate cutting-edge research in the areas of constraint programming, artificial intelligence, operations research, and the integration of these fields.

Abstract: Tools for automating decision-making, commonly referred to as solvers, have seen tremendous improvement over the past decades. Nevertheless, as the problems of today are becoming increasingly complex, the limits of solving technology is constantly being challenged, warranting further advancement of the state-of-the-art. The master class gathers the leading experts to present and discuss the latest techniques incorporated in modern solving technology and to explain their remarkable efficiency in solving large and complex problems. A series of talks is planned, each addressing a particular decision/optimisation paradigm.

List of confirmed speakers

Laurent Perron and Frédéric Didier (Google Paris, France): Constraint Programming
Armin Biere (Johannes Kepler University, Linz, Austria): Satisfiability (SAT)
Günther Raidl (TU Wien, Austria) and Andrea Schaerf (University of Udine, Italy): (Meta)Heuristics and Hybridisation
Inês Lynce (University of Lisbon, Portugal): MaxSAT, Multi-Objective Optimisation, and Parallelism
Timo Berthold (Fair Isaac Germany GmbH): Mixed-Integer Programming
Marie Pelleau (l'Université Nice Sophia-Antipolis, France): Numerical Constraint Programming

Master class organisers

Emir Demirović, University of Melbourne
Andrea Rendl, Satalia
Mohamed Siala, INSA Toulouse and LAAS-CNRS

Please refer your questions about the master class to the dedicated email address rather than directly emailing individual organisers:

Registration is temporarily closed

Registration is temporarily closed, due to uncertainties regarding the coronavirus. We will inform you once registration can be reopened and will adjust the timelines for early and late registration accordingly. We apologize for any inconvenience.