Optimal order quadrature error bounds for infinite-dimensional higher order digital sequences
Takashi Goda, Kosuke Suzuki, Takehito Yoshiki

TL;DR
This paper develops explicit, easy-to-implement quasi-Monte Carlo rules using digital sequences that achieve near-optimal convergence rates for high-dimensional integration in Sobolev spaces, extending previous results to arbitrary fixed smoothness levels.
Contribution
It provides a general, explicit construction of optimal order QMC rules for any fixed smoothness, including the endpoint case, with extensibility in both number of points and dimension.
Findings
Achieves the best possible convergence rate of $N^{- ext{alpha}}( ext{log} N)^{(s-1)/2}$
Constructs digital sequences that are easy to implement and extend
Extends results to arbitrary fixed smoothness including alpha=1
Abstract
Quasi-Monte Carlo (QMC) quadrature rules using higher order digital nets and sequences have been shown to achieve the almost optimal rate of convergence of the worst-case error in Sobolev spaces of arbitrary fixed smoothness , . In a recent paper by the authors, it was proved that randomly-digitally-shifted order digital nets in prime base achieve the best possible rate of convergence of the root mean square worst-case error of order for , where and denote the number of points and the dimension, respectively, which implies the existence of an optimal order QMC rule. More recently, the authors provided an explicit construction of such an optimal order QMC rule by using Chen-Skriganov's digital nets in conjunction with Dick's digit interlacing composition. These results were for fixed number…
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