Robust Protection against Uncertainties in Discrete-Continuous Optimization
Speaker: Prof. Dr. Frauke Liers
Affiliation: FAU MoD Research Center for Mathematics of Data. Department of Data Science (DDS) Professorship of Optimization under Uncertainty & Data Analysis at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Erlangen-Nürnberg (Germany)
Zoom meeting link
Meeting ID: 530 867 8850 | PIN: 014005
Abstract. Edging optimization problems against uncertainties is an exciting research area where new methods and algorithms are developing rapidly. One approach consists in determining best possible robust decisions that are feasible regardless of how uncertainties manifest themselves within predefined uncertainty sets.
In this talk, we will review some of the recent developments in the field, in particular for discrete-continuous robust optimization problems that often apply reformulation, decomposition as well as approximation approaches. Data-driven methods are on the rise as well. Discrete decisions add another difficulty in algorithmic tractability, both in theory as well as in practice. Together with presenting some approaches, we will also discuss experimental results in realistic application settings.