Sparse Linear Surrogates for Interpretable Budget Allocation
Optimization and Control
2026-08-03 v1
Abstract
To address the demand for inherently interpretable optimization methods, we introduce novel linear surrogates for budget allocation problems. These surrogates consist of sparse linear rules that map instances to feature-based representations of solutions. We present an exact approach based on mixed-integer programming as well as a heuristic for their computation. The performance of both approaches is analyzed through computational experiments.
Cite
@article{arxiv.2608.01858,
title = {Sparse Linear Surrogates for Interpretable Budget Allocation},
author = {Marc Goerigk and Michael Hartisch and Sebastian Merten},
journal= {arXiv preprint arXiv:2608.01858},
year = {2026}
}