Probabilistic discrepancy bound for Monte Carlo point sets

Author:

Aistleitner Christoph,Hofer Markus

Abstract

By a profound result of Heinrich, Novak, Wasilkowski, and Woźniakowski the inverse of the star-discrepancy n ( s , ε ) n^*(s,\varepsilon ) satisfies the upper bound n ( s , ε ) c a b s s ε 2 n^*(s,\varepsilon ) \leq c_{\mathrm {abs}} s \varepsilon ^{-2} . This is equivalent to the fact that for any N N and s s there exists a set of N N points in [ 0 , 1 ] s [0,1]^s whose star-discrepancy is bounded by c a b s s 1 / 2 N 1 / 2 c_{\mathrm {abs}} s^{1/2} N^{-1/2} . The proof is based on the observation that a random point set satisfies the desired discrepancy bound with positive probability. In the present paper we prove an applied version of this result, making it applicable for computational purposes: for any given number q ( 0 , 1 ) q \in (0,1) there exists an (explicitly stated) number c ( q ) c(q) such that the star-discrepancy of a random set of N N points in [ 0 , 1 ] s [0,1]^s is bounded by c ( q ) s 1 / 2 N 1 / 2 c(q) s^{1/2} N^{-1/2} with probability at least q q , uniformly in N N and s s .

Publisher

American Mathematical Society (AMS)

Subject

Applied Mathematics,Computational Mathematics,Algebra and Number Theory

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