Walk on spheres and Array-RQMC
Provides a highly effective variance reduction method for Monte Carlo simulation of Dirichlet boundary value problems, with substantial practical gains.
Array-RQMC sampling in a walk on spheres algorithm reduces Monte Carlo variance by factors of 57 to 2290 at n=2^17 trajectories for Dirichlet problems, with empirical rates between n^{-1.4} and n^{-1.8}, significantly outperforming simpler RQMC-WOS (1.8-10.7 fold reduction).
We use Array-RQMC sampling in a walk on spheres (WOS) algorithm for Dirichlet boundary value problems. On a collection of problems, we find that Array-RQMC-WOS reduces the Monte Carlo variance by factors ranging from $57$-fold to $2290$-fold at $n=2^{17}$ trajectories. The variance is known to be $o(1/n)$ but attains empirical rates between $n^{-1.4}$ and $n^{-1.8}$ in our examples. A simpler RQMC-WOS algorithm studied in Ho and Owen (2026) has more theoretical support but only reduced variance by 1.8 to 10.7-fold on the same set of examples. In order to explain this improvement, we introduce a column-wise mean dimension of the RQMC error based on Sobol' indices. It matches the usual mean dimension for Monte Carlo and the mean dimension of a dual lattice error for randomized lattices. We find for a gasket example from Crane et al.\ (2025) that the mean dimension of Array-RQMC-WOS errors is much higher than an analogous Array-MC-WOS algorithm has.