Generalized Discrepancy of Random Points

TL;DR

This paper investigates the $L_p$-discrepancy of random point sets in high dimensions, introducing generalized discrepancy measures with non-uniform sampling to improve bounds, and analyzing the persistence of the curse of dimensionality.

Contribution

It derives new upper bounds for generalized $L_p$-discrepancies using non-uniform sampling densities, revealing the persistent curse of dimensionality for random points.

Findings

Optimal densities improve upper bounds for $p=2$

Asymptotic estimates for $p eq 2$

Curse of dimensionality persists for $p eq 2$

Abstract

We study the $L_p$-discrepancy of random point sets in high dimensions, with emphasis on small values of $p$. Although the classical $L_p$-discrepancy suffers from the curse of dimensionality for all $p \in (1,\infty)$, the gap between known upper and lower bounds remains substantial, in particular for small $p \ge 1$. To clarify this picture, we review the existing results for i.i.d.\ uniformly distributed points and derive new upper bounds for \emph{generalized} $L_p$-discrepancies, obtained by allowing non-uniform sampling densities and corresponding non-negative quadrature weights. Using the probabilistic method, we show that random points drawn from optimally chosen product densities lead to significantly improved upper bounds. For $p=2$ these bounds are explicit and optimal; for general $p \in [1,\infty)$ we obtain sharp asymptotic estimates. The improvement can be interpreted as a form of importance sampling for the underlying Sobolev space $F_{d,q}$. Our results also reveal that, even with optimal densities, the curse of dimensionality persists for random points when $p\ge 1$, and it becomes most pronounced for small $p$. This suggests that the curse should also hold for the classical $L_1$-discrepancy for deterministic point sets.