Adaptive Multidimensional Integration Based on Rank-1 Lattices

TL;DR

This paper introduces an adaptive quasi-Monte Carlo integration method using rank-1 lattices, with error bounds based on Fourier coefficients, enabling guaranteed accuracy depending on the integrand's spectral decay.

Contribution

It develops a new error bound based on Fourier coefficients and proposes an adaptive algorithm for multidimensional integration using rank-1 lattices.

Findings

Error bounds depend on Fourier coefficient decay

The adaptive algorithm guarantees accuracy based on spectral properties

Computational cost scales as O(mb^m) with data points

Abstract

Quasi-Monte Carlo methods are used for numerically integrating multivariate functions. However, the error bounds for these methods typically rely on a priori knowledge of some semi-norm of the integrand, not on the sampled function values. In this article, we propose an error bound based on the discrete Fourier coefficients of the integrand. If these Fourier coefficients decay more quickly, the integrand has less fine scale structure, and the accuracy is higher. We focus on rank-1 lattices because they are a commonly used quasi-Monte Carlo design and because their algebraic structure facilitates an error analysis based on a Fourier decomposition of the integrand. This leads to a guaranteed adaptive cubature algorithm with computational cost $O(mb^m)$, where $b$ is some fixed prime number and $b^m$ is the number of data points.