A computational comparison of handling distance constraints in MINLP
Optimization and Control
2026-05-05 v1
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 introduce a practical approach for handling rotation symmetries via separation of lexicographic constraints induced by Givens rotations. In a computational study, we examine the performance of the various methods and determine the scenarios in which each approach demonstrates superiority.
Keywords
Cite
@article{arxiv.2605.02305,
title = {A computational comparison of handling distance constraints in MINLP},
author = {Christopher Hojny and Leo Liberti},
journal= {arXiv preprint arXiv:2605.02305},
year = {2026}
}