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Related papers: On the optimization of discrepancy measures

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The $L_{\infty}$ star discrepancy is a measure for the regularity of a finite set of points taken from $[0,1)^d$. Low discrepancy point sets are highly relevant for Quasi-Monte Carlo methods in numerical integration and several other…

Neural and Evolutionary Computing · Computer Science 2023-06-30 François Clément , Diederick Vermetten , Jacob de Nobel , Alexandre D. Jesus , Luís Paquete , Carola Doerr

We study the extreme and the periodic $L_p$ discrepancy of point sets in the $d$-dimensional unit cube. The extreme discrepancy uses arbitrary sub-intervals of the unit cube as test sets, whereas the periodic discrepancy is based on…

Number Theory · Mathematics 2021-09-14 Ralph Kritzinger , Friedrich Pillichshammer

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.…

Computational Geometry · Computer Science 2024-02-28 François Clément , Carola Doerr , Kathrin Klamroth , Luís Paquete

The star discrepancy $D_N^*(\mathcal{P})$ is a quantitative measure for the irregularity of distribution of a finite point set $\mathcal{P}$ in the multi-dimensional unit cube which is intimately related to the integration error of…

Number Theory · Mathematics 2018-03-22 Mario Neumüller , Friedrich Pillichshammer

In this book chapter we survey known approaches and algorithms to compute discrepancy measures of point sets. After providing an introduction which puts the calculation of discrepancy measures in a more general context, we focus on the…

Numerical Analysis · Mathematics 2021-09-21 Carola Doerr , Michael Gnewuch , Magnus Wahlström

The L infinity star discrepancy is a measure for how uniformly a point set is distributed in a given space. Point sets of low star discrepancy are used as designs of experiments, as initial designs for Bayesian optimization algorithms, for…

Neural and Evolutionary Computing · Computer Science 2026-04-02 Imène Ait Abderrahim , Carola Doerr , Martin Durand

We study the periodic $L_2$-discrepancy of point sets in the $d$-dimensional torus. This discrepancy is intimately connected with the root-mean-square $L_2$-discrepancy of shifted point sets, with the notion of diaphony, and with the worst…

Number Theory · Mathematics 2020-01-08 Josef Dick , Aicke Hinrichs , Friedrich Pillichshammer

Kernel discrepancies are a powerful tool for analyzing worst-case errors in quasi-Monte Carlo (QMC) methods. Building on recent advances in optimizing such discrepancy measures, we extend the subset selection problem to the setting of…

Machine Learning · Statistics 2025-11-05 Deyao Chen , François Clément , Carola Doerr , Nathan Kirk

Two popular and often applied methods to obtain two-dimensional point sets with the optimal order of $L_p$ discrepancy are digit scrambling and symmetrization. In this paper we combine these two techniques and symmetrize $b$-adic Hammersley…

Number Theory · Mathematics 2016-04-13 Ralph Kritzinger , Lisa M. Kritzinger

In the present paper we prove several results concerning the existence of low-discrepancy point sets with respect to an arbitrary non-uniform measure $\mu$ on the $d$-dimensional unit cube. We improve a theorem of Beck, by showing that for…

Number Theory · Mathematics 2013-08-26 Christoph Aistleitner , Josef Dick

Geometric discrepancies are standard measures to quantify the irregularity of distributions. They are an important notion in numerical integration. One of the most important discrepancy notions is the so-called \emph{star discrepancy}.…

Neural and Evolutionary Computing · Computer Science 2013-10-08 Carola Doerr , Francois-Michel De Rainville

Discrepancy measures how uniformly distributed a point set is with respect to a given set of ranges. There are two notions of discrepancy, namely continuous discrepancy and combinatorial discrepancy. Depending on the ranges, several…

Computational Geometry · Computer Science 2011-03-24 Panos Giannopoulos , Christian Knauer , Magnus Wahlström , Daniel Werner

Low discrepancy point sets have been widely used as a tool to approximate continuous objects by discrete ones in numerical processes, for example in numerical integration. Following a century of research on the topic, it is still unclear…

Computational Geometry · Computer Science 2024-07-17 François Clément , Carola Doerr , Kathrin Klamroth , Luís Paquete

We present a new algorithm for estimating the star discrepancy of arbitrary point sets. Similar to the algorithm for discrepancy approximation of Winker and Fang [SIAM J. Numer. Anal. 34 (1997), 2028--2042] it is based on the optimization…

Data Structures and Algorithms · Computer Science 2021-09-21 Michael Gnewuch , Magnus Wahlström , Carola Winzen

We investigate $L_2$-discrepancies of what we call weak Latin hypercubes. In this case it turns out that there is a precise equivalence between the extreme and periodic $L_2$-discrepancy which follows from a much broader result about…

Numerical Analysis · Mathematics 2025-03-03 Nicolas Nagel

The star discrepancy is a quantitative measure of the uniformity of a point set in the unit cube. A central quantity of interest is the inverse of the star discrepancy, $N(\varepsilon, s)$, defined as the minimum number of points required…

Numerical Analysis · Mathematics 2026-03-06 Josef Dick , Friedrich Pillichshammer

We consider a linear ill-posed equation in the Hilbert space setting. Multiple independent unbiased measurements of the right hand side are available. A natural approach is to take the average of the measurements as an approximation of the…

Numerical Analysis · Mathematics 2021-09-01 Tim Jahn

We start by providing a very simple and elementary new proof of the classical bound due to J. Beck which states that the spherical cap $\mathbb{L}_2$-discrepancy of any $N$ points on the unit sphere $\mathbb S^d$ in $\mathbb{R}^{d+1}$,…

Classical Analysis and ODEs · Mathematics 2025-02-25 Dmitriy Bilyk , Johann S. Brauchart

We consider the problem of approximating the unknown density $u\in L^2(\Omega,\lambda)$ of a measure $\mu$ on $\Omega\subset\R^n$, absolutely continuous with respect to some given reference measure $\lambda$, from the only knowledge of…

Optimization and Control · Mathematics 2012-09-03 Didier Henrion , Jean-Bernard Bernard Lasserre , Martin Mevissen

By a result of Heinrich, Novak, Wasilkowski and Wo\'zniakowski the inverse of the star discrepancy $n(d,\varepsilon)$ satisfies $n(d,\varepsilon)\leq c_{\abs}d\varepsilon^{-2}$. Equivalently for any $N$ and $d$ there exists a set of $N$…

Probability · Mathematics 2014-08-12 Thomas Löbbe
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