On the Randomization of Frolov's Algorithm for Multivariate Integration

TL;DR

This paper reviews the randomized Frolov's algorithm for multivariate integration, highlighting its optimal error bounds and extensions to different Sobolev spaces, with implications for numerical analysis.

Contribution

It summarizes recent advances in randomized Frolov's algorithm, including error bounds, unbiased variants, and extensions to isotropic Sobolev spaces.

Findings

Error bounds of the randomized algorithm are optimal for Sobolev spaces.

Adding a random shift makes the algorithm unbiased.

The algorithm's effectiveness extends to isotropic Sobolev spaces.

Abstract

We are concerned with the numerical integration of functions from the Sobolev space $H^{r,\text{mix}}([0,1]^d)$ of dominating mixed smoothness $r\in\mathbb{N}$ over the $d$-dimensional unit cube. In 1976, K. K. Frolov introduced a deterministic quadrature rule whose worst case error has the order $n^{-r} \, (\log n)^{(d-1)/2}$ with respect to the number $n$ of function evaluations. This is known to be optimal. 39 years later, Erich Novak and me introduced a randomized version of this algorithm using $d$ random dilations. We showed that its error is bounded above by a constant multiple of $n^{-r-1/2} \, (\log n)^{(d-1)/2}$ in expectation and by $n^{-r} \, (\log n)^{(d-1)/2}$ almost surely. The main term $n^{-r-1/2}$ is again optimal and it turns out that the very same algorithm is also optimal for the isotropic Sobolev space $H^s([0,1]^d)$ of smoothness $s>d/2$. We also added a random shift to this algorithm to make it unbiased. Just recently, Mario Ullrich proved that the expected error of the resulting algorithm on $H^{r,\text{mix}}([0,1]^d)$ is even bounded above by $n^{-r-1/2}$. This thesis is a review of the mentioned upper bounds and their proofs.