Table of Contents
Fetching ...

Polynomial-order oscillations in geometric discrepancy

Thomas Beretti

TL;DR

This work shows that the optimal growth rate of the planar homothetic quadratic discrepancy $\mathcal D_2$ for convex bodies can exhibit prescribed oscillations between logarithmic and polynomial scales, challenging the notion of a single universal rate. It introduces two independent constructions: a geometric, implicit nested-convex-body method that realizes jumps among $\log N$, $N^{2/5}$, and $N^{1/2}$, and a Fourier-analytic method that directly builds boundary features to realize polynomial oscillations with exponents $\alpha\in(2/5,1/2)$. The results demonstrate that, for general convex bodies, the optimal h.q.d. need not fixate on a single order of growth and can be steered across a continuum of polynomial orders. This has implications for understanding irregularities of distribution in geometric contexts and highlights the interplay between boundary geometry, Fourier mass distribution, and discrepancy statistics.

Abstract

Let $C\subset\mathbb{R}^2$ be a convex body, and let $\mathcal{P}\subset[0,1)^2$ be a set of $N$ points. The discrepancy of $\mathcal{P}$ with respect to $C$ is defined as \begin{equation*} \mathcal{D}(\mathcal{P},\, C)=\sum_{\mathbf{p}\in\mathcal{P}}\sum_{\mathbf{n}\in\mathbb{Z}^2}\mathds{1}_C(\mathbf{p}+\mathbf{n})-N|C|. \end{equation*} A standard approach for estimating how a point distribution deviates from uniformity is to average the latter quantity over a family of sets; in particular, when considering quadratic averages over translations and dilations, one obtains \begin{equation*} \mathcal{D}_2(\mathcal{P},\, C)=\int_{0}^{1}\int_{[0,1)^2}\left|\mathcal{D}( \mathcal{P},\,\boldsymbolτ+δC)\right|^2\,{\rm d}\boldsymbolτ\,{\rm d} δ. \end{equation*} This paper concerns the behaviour of optimal \textit{homothetic quadratic discrepancy} \begin{equation*} \inf_{\# \mathcal{P}=N} \mathcal{D}_2(\mathcal{P},\, C)\quad\text{as}\quad N\to+\infty. \end{equation*} Beck and Chen \cite{MR1489133} showed that the optimal \textit{h.q.d.} of convex polygons has an order of growth of $\log N$. More recently, Brandolini and Travaglini \cite{MR4358540} showed that the optimal \textit{h.q.d.} of planar convex bodies has an order of growth of $N^{1/2}$ if their boundary is $\mathcal{C}^2$, and of $N^{2/5}$ if their boundary is only piecewise-$\mathcal{C}^2$ and not polygonal. We show that, in general, no order of growth is required. First, by an implicit geometric construction, we show that one can obtain prescribed oscillations from a logarithmic order of $\log N$ to polynomial orders of $N^{2/5}$ and $N^{1/2}$, and vice versa. Secondly, by using Fourier-analytic methods, we show that prescribed polynomial-order oscillations in the range $N^α$ with $α\in(2/5,1/2)$ are achievable.

Polynomial-order oscillations in geometric discrepancy

TL;DR

This work shows that the optimal growth rate of the planar homothetic quadratic discrepancy for convex bodies can exhibit prescribed oscillations between logarithmic and polynomial scales, challenging the notion of a single universal rate. It introduces two independent constructions: a geometric, implicit nested-convex-body method that realizes jumps among , , and , and a Fourier-analytic method that directly builds boundary features to realize polynomial oscillations with exponents . The results demonstrate that, for general convex bodies, the optimal h.q.d. need not fixate on a single order of growth and can be steered across a continuum of polynomial orders. This has implications for understanding irregularities of distribution in geometric contexts and highlights the interplay between boundary geometry, Fourier mass distribution, and discrepancy statistics.

Abstract

Let be a convex body, and let be a set of points. The discrepancy of with respect to is defined as \begin{equation*} \mathcal{D}(\mathcal{P},\, C)=\sum_{\mathbf{p}\in\mathcal{P}}\sum_{\mathbf{n}\in\mathbb{Z}^2}\mathds{1}_C(\mathbf{p}+\mathbf{n})-N|C|. \end{equation*} A standard approach for estimating how a point distribution deviates from uniformity is to average the latter quantity over a family of sets; in particular, when considering quadratic averages over translations and dilations, one obtains \begin{equation*} \mathcal{D}_2(\mathcal{P},\, C)=\int_{0}^{1}\int_{[0,1)^2}\left|\mathcal{D}( \mathcal{P},\,\boldsymbolτ+δC)\right|^2\,{\rm d}\boldsymbolτ\,{\rm d} δ. \end{equation*} This paper concerns the behaviour of optimal \textit{homothetic quadratic discrepancy} \begin{equation*} \inf_{\# \mathcal{P}=N} \mathcal{D}_2(\mathcal{P},\, C)\quad\text{as}\quad N\to+\infty. \end{equation*} Beck and Chen \cite{MR1489133} showed that the optimal \textit{h.q.d.} of convex polygons has an order of growth of . More recently, Brandolini and Travaglini \cite{MR4358540} showed that the optimal \textit{h.q.d.} of planar convex bodies has an order of growth of if their boundary is , and of if their boundary is only piecewise- and not polygonal. We show that, in general, no order of growth is required. First, by an implicit geometric construction, we show that one can obtain prescribed oscillations from a logarithmic order of to polynomial orders of and , and vice versa. Secondly, by using Fourier-analytic methods, we show that prescribed polynomial-order oscillations in the range with are achievable.
Paper Structure (7 sections, 10 theorems, 107 equations, 4 figures)

This paper contains 7 sections, 10 theorems, 107 equations, 4 figures.

Key Result

Theorem 1.2

Let $C$ be a convex polygon. Then, it holds

Figures (4)

  • Figure 1: The construction in the proof of Theorem \ref{['main1']}.
  • Figure 2: The chord in Definition \ref{['Corde1']}.
  • Figure 3: A close-up look at $P$, in which different auxiliary monomial curve segments are displayed in different colours.
  • Figure 4: The auxiliary tools we constructed.

Theorems & Definitions (16)

  • Definition 1.1
  • Theorem 1.3: Brandolini-Travaglini
  • Theorem 1.4
  • Theorem 1.5
  • Lemma 2.1
  • proof
  • Definition 3.1
  • Lemma 3.2
  • Proposition 3.3
  • Lemma 3.4
  • ...and 6 more