4 citations · 5 across the 12 of their papers we have counts for
13 papers
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…
An Expansion-Based Approach for Quantified Integer Programming
Michael Hartisch, Leroy Chew
Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while a…
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…
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…
A general model-and-run solver for multistage robust discrete linear optimization
Michael Hartisch, Ulf Lorenz
The necessity to deal with uncertain data is a major challenge in decision making. Robust optimization emerged as one of the predominant paradigms to produce solutions that hedge a…
A Framework for Inherently Interpretable Optimization Models
Marc Goerigk, Michael Hartisch
With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world…
A Preference Elicitation Approach for the Ordered Weighted Averaging Criterion using Solution Choice Observations
Werner Baak, Marc Goerigk, Michael Hartisch
Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the…
Combinatorial Optimization Problems with Balanced Regret
Marc Goerigk, Michael Hartisch
For decision making under uncertainty, min-max regret has been established as a popular methodology to find robust solutions. In this approach, we compare the performance of our so…
Adaptive Relaxations for Multistage Robust Optimization
Michael Hartisch
Multistage robust optimization problems can be interpreted as two-person zero-sum games between two players. We exploit this game-like nature and utilize a game tree search in orde…
Multistage Robust Discrete Optimization via Quantified Integer Programming
Marc Goerigk, Michael Hartisch
Decision making needs to take an uncertain environment into account. Over the last decades, robust optimization has emerged as a preeminent method to produce solutions that are imm…
Game Tree Search in a Robust Multistage Optimization Framework: Exploiting Pruning Mechanisms
Michael Hartisch, Ulf Lorenz
We investigate pruning in search trees of so-called quantified integer linear programs (QIPs). QIPs consist of a set of linear inequalities and a minimax objective function, where…