# Explicit error bounds for randomized Smolyak algorithms and an application to infinite-dimensional integration

## TL;DR

This paper analyzes randomized Smolyak algorithms, providing explicit error bounds and demonstrating their effectiveness in high-dimensional and infinite-dimensional integration problems, with applications to weighted reproducing kernel Hilbert spaces.

## Contribution

It introduces explicit error bounds for randomized Smolyak algorithms and applies these results to infinite-dimensional integration, highlighting when randomized methods outperform deterministic ones.

## Key findings

- Derived upper and lower error bounds with explicit dependence on variables and evaluations.
- Established convergence rates for N-th minimal errors in infinite-dimensional integration.
- Characterized spaces where randomized algorithms outperform deterministic counterparts.

## Abstract

Smolyak's method, also known as hyperbolic cross approximation or sparse grid method, is a powerful tool to tackle multivariate tensor product problems solely with the help of efficient algorithms for the corresponding univariate problem. In this paper we study the randomized setting, i.e., we randomize Smolyak's method. We provide upper and lower error bounds for randomized Smolyak algorithms with explicitly given dependence on the number of variables and the number of information evaluations used. The error criteria we consider are the worst-case root mean square error (the typical error criterion for randomized algorithms, often referred to as "randomized error") and the root mean square worst-case error (often referred to as "worst-case error"). Randomized Smolyak algorithms can be used as building blocks for efficient methods such as multilevel algorithms, multivariate decomposition methods or dimension-wise quadrature methods to tackle successfully high-dimensional or even infnite-dimensional problems. As an example, we provide a very general and sharp result on the convergence rate of N-th minimal errors of infnite-dimensional integration on weighted reproducing kernel Hilbert spaces. Moreover, we are able to characterize the spaces for which randomized algorithms for infnte-dimensional integration are superior to deterministic ones. We illustrate our fndings for the special instance of weighted Korobov spaces. We indicate how these results can be extended, e.g., to spaces of functions whose smooth dependence on successive variables increases ("spaces of increasing smoothness") and to the problem of L2-approximation (function recovery).