Second order interlaced polynomial lattice rules for integration over $\mathbb{R}^s$

Fuente: arXiv
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Auteurs principaux: Cui, Tiangang, Dick, Josef, Pillichshammer, Friedrich
Format: Preprint
Publié: 2025
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author Cui, Tiangang
Dick, Josef
Pillichshammer, Friedrich
author_facet Cui, Tiangang
Dick, Josef
Pillichshammer, Friedrich
contents We study numerical integration of functions $f: \mathbb{R}^{s} \to \mathbb{R}$ with respect to a probability measure. By applying the corresponding inverse cumulative distribution function, the problem is transformed into integrating an induced function over the unit cube $(0,1)^{s}$. We introduce a new orthonormal system: \emph{order~2 localized Walsh functions}. These basis functions retain the approximation power of classical Walsh functions for twice-differentiable integrands while inheriting the spatial localization of Haar wavelets. Localization is crucial because the transformed integrand is typically unbounded at the boundary. We show that the worst-case quasi-Monte Carlo integration error decays like $\mathcal{O}(N^{-1/λ})$ for every $λ\in (1/2,1]$. As an application, we consider elliptic partial differential equations with a finite number of log-normal random coefficients and show that our error estimates remain valid for their stochastic Galerkin discretizations by applying a suitable importance sampling density.
format Preprint
id arxiv_https___arxiv_org_abs_2509_26624
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Second order interlaced polynomial lattice rules for integration over $\mathbb{R}^s$
Cui, Tiangang
Dick, Josef
Pillichshammer, Friedrich
Numerical Analysis
We study numerical integration of functions $f: \mathbb{R}^{s} \to \mathbb{R}$ with respect to a probability measure. By applying the corresponding inverse cumulative distribution function, the problem is transformed into integrating an induced function over the unit cube $(0,1)^{s}$. We introduce a new orthonormal system: \emph{order~2 localized Walsh functions}. These basis functions retain the approximation power of classical Walsh functions for twice-differentiable integrands while inheriting the spatial localization of Haar wavelets. Localization is crucial because the transformed integrand is typically unbounded at the boundary. We show that the worst-case quasi-Monte Carlo integration error decays like $\mathcal{O}(N^{-1/λ})$ for every $λ\in (1/2,1]$. As an application, we consider elliptic partial differential equations with a finite number of log-normal random coefficients and show that our error estimates remain valid for their stochastic Galerkin discretizations by applying a suitable importance sampling density.
title Second order interlaced polynomial lattice rules for integration over $\mathbb{R}^s$
topic Numerical Analysis
url https://arxiv.org/abs/2509.26624