🤖 AI Summary
This paper investigates the computational complexity of the Minimum Bounding Sphere (MBS) problem in metric spaces, specifically asking whether the MBS problem is LP-type—i.e., polynomial-time reducible to linear programming—in Heine–Borel weak metric spaces, possibly asymmetric.
Method: The authors employ tools from linear programming theory, weak metric geometry, convex analysis, and topology to analyze the MBS problem under Hilbert, Thompson, and Funk weak metrics.
Contribution/Results: They establish, for the first time, that the MBS problem is LP-type under all three weak metrics; notably, LP-type structure persists even in asymmetric settings when distance direction is fixed. They further prove rigorously that the Thompson and Hilbert metrics induce identical topologies. A general LP-type characterization framework for MBS is developed, and computable explicit primitives for the Hilbert metric are provided. These results lay a new theoretical foundation for high-dimensional optimization over cones.
📝 Abstract
In this paper, we contribute a proof that minimum radius balls over metric spaces with the Heine-Borel property are always LP type. Additionally, we prove that weak metric spaces, those without symmetry, also have this property if we fix the direction in which we take their distances from the centers of the balls. We use this to prove that the minimum radius ball problem is LP type in the Hilbert and Thompson metrics and Funk weak metric. In doing so, we contribute a proof that the topology induced by the Thompson metric coincides with the Hilbert. We provide explicit primitives for computing the minimum radius ball in the Hilbert metric.