4 papers
Feature Selection for Data-driven Explainable Optimization
Kevin-Martin Aigner, Marc Goerigk, Michael Hartisch +3
Mathematical optimization, although often leading to NP-hard models, is now capable of solving even large-scale instances within reasonable time. However, the primary focus is ofte…
Scenario Reduction for Distributionally Robust Optimization
Kevin-Martin Aigner, Sebastian Denzler, Frauke Liers +2
Stochastic and (distributionally) robust optimization problems often become computationally challenging as the number of scenarios or data points increases. Scenario reduction is t…
A Framework for Data-Driven Explainability in Mathematical Optimization
Kevin-Martin Aigner, Marc Goerigk, Michael Hartisch +2
Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, p…
Data-driven Distributionally Robust Optimization over Time
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun +5
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters…