QMC integration based on arbitrary (t,m,s)-nets yields optimal convergence rates on several scales of function spaces
Michael Gnewuch, Josef Dick, Lev Markhasin, Winfried Sickel

TL;DR
This paper demonstrates that quasi-Monte Carlo methods using arbitrary (t,m,s)-nets achieve optimal convergence rates for integration over various function spaces, including Haar wavelet, Sobolev, and Besov spaces, on the unit cube.
Contribution
It establishes the optimality of (t,m,s)-net-based QMC rules across multiple function spaces, providing both upper and lower error bounds and connecting different smoothness spaces through embeddings.
Findings
QMC with (t,m,s)-nets achieves optimal convergence rates.
Error bounds transfer across Haar, Sobolev, and Besov spaces.
Lower bounds confirm the optimality of the proposed QMC approach.
Abstract
We study the integration problem over the -dimensional unit cube on four types of Banach spaces of integrands. First we consider Haar wavelet spaces, consisting of functions whose Haar wavelet coefficients exhibit a certain decay behavior measured by a parameter . We study the worst case error of integration over the norm unit ball and provide upper error bounds for quasi-Monte Carlo (QMC) cubature rules based on arbitrary -nets as well as matching lower error bounds for arbitrary cubature rules. These results show that using arbitrary -nets as sample points yields the best possible rate of convergence. Afterwards we study spaces of integrands of fractional smoothness and state a sharp Koksma-Hlawka-type inequality. More precisely, we show that on those spaces the worst case error of integration is equal to the corresponding fractional…
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Taxonomy
TopicsNumerical Methods and Algorithms · Digital Filter Design and Implementation · Optimization and Packing Problems
