Well-Separation and Hyperplane Transversals in High Dimensions
Helena Bergold, Daniel Bertschinger, Nicolas Grelier and, Wolfgang Mulzer, Patrick Schnider

TL;DR
This paper investigates the computational complexity of well-separation and hyperplane transversals in high-dimensional point sets, establishing NP-hardness and coNP-completeness results, and providing approximation algorithms for related problems.
Contribution
It provides the first explicit proof linking well-separation to the absence of certain transversals, and characterizes the complexity of testing well-separation and finding optimal transversals in high dimensions.
Findings
Checking well-separation is in coNP.
Deciding hyperplane transversals is NP-hard.
Approximation algorithms exist for maximizing intersected sets.
Abstract
A family of point sets in dimensions is well-separated if the convex hulls of any two disjoint subfamilies can be separated by a hyperplane. Well-separation is a strong assumption that allows us to conclude that certain kinds of generalized ham-sandwich cuts for the point sets exist. But how hard is it to check if a given family of high-dimensional point sets has this property? Starting from this question, we study several algorithmic aspects of the existence of transversals and separations in high-dimensions. First, we give an explicit proof that point sets are well-separated if and only if their convex hulls admit no -transversal, i.e., if there exists no -dimensional flat that intersects the convex hulls of all sets. It follows that the task of checking well-separation lies in the complexity class coNP. Next, we show that it is NP-hard to decide…
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Digital Image Processing Techniques · Advanced Numerical Analysis Techniques
