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Approximation of Discrete Measures by Finite Point Sets Cover

Approximation of Discrete Measures by Finite Point Sets

By: Christian Weiss  
Open Access
|Aug 2023

Abstract

For a probability measure μ on [0, 1] without discrete component, the best possible order of approximation by a finite point set in terms of the star-discrepancy is &inline as has been proven relatively recently. However, if μ contains a discrete component no non-trivial lower bound holds in general because it is straightforward to construct examples without any approximation error in this case. This might explain, why the approximation of discrete measures on [0, 1] by finite point sets has so far not been completely covered in the existing literature. In this note, we close the gap by giving a complete description for discrete measures. Most importantly, we prove that for any discrete measures (not supported on one point only) the best possible order of approximation is for infinitely many N bounded from below by &inline for some constant 6 ≥ c> 2 which depends on the measure. This implies, that for a finitely supported discrete measure on [0, 1]d the known possible order of approximation &inline is indeed the optimal one.

DOI: https://doi.org/10.2478/udt-2023-0003 | Journal eISSN: 2309-5377 | Journal ISSN: 1336-913X
Language: English
Page range: 31 - 38
Submitted on: Jul 7, 2022
Accepted on: Feb 9, 2023
Published on: Aug 10, 2023
Published by: Slovak Academy of Sciences, Mathematical Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 Christian Weiss, published by Slovak Academy of Sciences, Mathematical Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.