Scrambled geometric net integration over general product spaces
For researchers in numerical integration and quasi-Monte Carlo methods, this work extends scrambled net quadrature to non-cubic domains, offering improved convergence rates over Monte Carlo.
This paper presents a construction of randomized point sets for numerical integration over Cartesian products of general spaces (e.g., triangles, spheres, disks) using scrambled geometric nets. The method achieves unbiased estimates with variance o(1/n) for any L^2 integrand, and under smoothness assumptions, variance O(n^{-1-2/d} (log n)^{s-1}), outperforming Monte Carlo's O(n^{-1}).
Quasi-Monte Carlo (QMC) sampling has been developed for integration over $[0,1]^s$ where it has superior accuracy to Monte Carlo (MC) for integrands of bounded variation. Scrambled net quadrature gives allows replication based error estimation for QMC with at least the same accuracy and for smooth enough integrands even better accuracy than plain QMC. Integration over triangles, spheres, disks and Cartesian products of such spaces is more difficult for QMC because the induced integrand on a unit cube may fail to have the desired regularity. In this paper, we present a construction of point sets for numerical integration over Cartesian products of $s$ spaces of dimension $d$, with triangles ($d=2$) being of special interest. The point sets are transformations of randomized $(t,m,s)$-nets using recursive geometric partitions. The resulting integral estimates are unbiased and their variance is $o(1/n)$ for any integrand in $L^2$ of the product space. Under smoothness assumptions on the integrand, our randomized QMC algorithm has variance $O(n^{-1 - 2/d} (\log n)^{s-1})$, for integration over $s$-fold Cartesian products of $d$-dimensional domains, compared to $O(n^{-1})$ for ordinary Monte Carlo.