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Title: Families of Well Approximable Measures
Abstract We provide an algorithm to approximate a finitely supported discrete measureμby a measureνNcorresponding to a set ofNpoints so that the total variation betweenμandνNhas an upper bound. As a consequence ifμis a (finite or infinitely supported) discrete probability measure on [0, 1]dwith a sufficient decay rate on the weights of each point, thenμcan be approximated byνNwith total variation, and hence star-discrepancy, bounded above by (logN)N1. Our result improves, in the discrete case, recent work by Aistleitner, Bilyk, and Nikolov who show that for any normalized Borel measureμ, there exist finite sets whose star-discrepancy with respect toμis at most ( log N ) d 1 2 N 1 {\left( {\log \,N} \right)^{d - {1 \over 2}}}{N^{ - 1}}. Moreover, we close a gap in the literature for discrepancy in the cased=1 showing both that Lebesgue is indeed the hardest measure to approximate by finite sets and also that all measures without discrete components have the same order of discrepancy as the Lebesgue measure.  more » « less
Award ID(s):
1853993
PAR ID:
10571595
Author(s) / Creator(s):
; ;
Publisher / Repository:
Uniform Distribution Theory
Date Published:
Journal Name:
Uniform distribution theory
Volume:
16
Issue:
1
ISSN:
2309-5377
Page Range / eLocation ID:
53 to 70
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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