3 papers
math.OC2026
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…
math.OC2026
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…
cs.LG2024
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…