Constructing Optimal $L_{\infty}$ Star Discrepancy Sets

TL;DR

This paper introduces mathematical programming models to construct optimal point sets minimizing the $L_{ abla}$ star discrepancy, achieving new records for dimensions 2 and 3 and revealing novel structures in these sets.

Contribution

The paper develops new mathematical programming formulations for constructing optimal $L_{ abla}$ star discrepancy sets, extending known results to larger point sets in low dimensions.

Findings

Optimal sets computed for up to 21 points in 2D and 8 points in 3D.

New sets exhibit about 50% lower discrepancy than previous bests.

Optimal sets have no two points sharing a coordinate.

Abstract

The $L_{\infty}$ star discrepancy is a very well-studied measure used to quantify the uniformity of a point set distribution. Constructing optimal point sets for this measure is seen as a very hard problem in the discrepancy community. Indeed, optimal point sets are, up to now, known only for $n\leq 6$ in dimension 2 and $n \leq 2$ for higher dimensions. We introduce in this paper mathematical programming formulations to construct point sets with as low $L_{\infty}$ star discrepancy as possible. Firstly, we present two models to construct optimal sets and show that there always exist optimal sets with the property that no two points share a coordinate. Then, we provide possible extensions of our models to other measures, such as the extreme and periodic discrepancies. For the $L_{\infty}$ star discrepancy, we are able to compute optimal point sets for up to 21 points in dimension 2 and for up to 8 points in dimension 3. For $d=2$ and $n\ge 7$ points, these point sets have around a 50% lower discrepancy than the current best point sets, and show a very different structure.