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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…
math.OC2025
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…