5 papers
Decision-Tree Surrogates for Weak Counterfactual Explanations in Integer Optimization
Marc Goerigk, Jannis Kurtz, Sebastian Merten
The concept of counterfactual explanations (CEs) has recently gained increasing attention for deriving explanations of mathematical optimization problems. For a given problem, a co…
Sparse Linear Surrogates for Interpretable Budget Allocation
Marc Goerigk, Michael Hartisch, Sebastian Merten
To address the demand for inherently interpretable optimization methods, we introduce novel linear surrogates for budget allocation problems. These surrogates consist of sparse lin…
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…
Towards Robust Interpretable Surrogates for Optimization
Marc Goerigk, Michael Hartisch, Sebastian Merten
An important factor in the practical implementation of optimization models is the acceptance by the intended users. This is influenced among other factors by the interpretability o…
Feature-Based Interpretable Surrogates for Optimization
Marc Goerigk, Michael Hartisch, Sebastian Merten +1
For optimization models to be used in practice, it is crucial that users trust the results. A key factor in this aspect is the interpretability of the solution process. A previous…