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arXiv · 2605.02305

A computational comparison of handling distance constraints in MINLP

Christopher Hojny·Leo Liberti
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Abstract

Minimum distance constraints (minDCs) appear in many geometric optimization problems. They pose major challenges for mixed-integer nonlinear programming (MINLP) due to their reverse-convexity. We develop new algorithms for tightening variable bounds in general MINLPs with minDCs. Because many such problems exhibit substantial symmetry, we further discuss an approach for handling rotation symmetries. In a computational study, we examine the performance of the various methods and determine the scenarios in which each approach demonstrates superiority.

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Christopher Hojny, Leo Liberti. 2026-05-04. A computational comparison of handling distance constraints in MINLP. https://arxiv.org/abs/2605.02305

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