Explicit constructions of quasi-Monte Carlo rules for the numerical integration of high dimensional periodic functions

TL;DR

This paper presents explicit constructions of quasi-Monte Carlo point sets that achieve near-optimal convergence rates for high-dimensional periodic function integration, applicable in both deterministic and randomized settings.

Contribution

The paper introduces a novel extension of digital nets to construct point sets with optimal convergence rates for high-dimensional periodic functions.

Findings

Achieves convergence rate of approximately N^{- ext{min}( ext{alpha},d)} with logarithmic factors.

Works for both deterministic and randomized quasi-Monte Carlo algorithms.

Based on an extended digital net construction over finite fields.

Abstract

In this paper we give explicit constructions of point sets in the $s$ dimensional unit cube yielding quasi-Monte Carlo algorithms which achieve the optimal rate of convergence of the worst-case error for numerically integrating high dimensional periodic functions. In the classical measure $P_{\alpha}$ of the worst-case error introduced by Korobov the convergence is of $\landau(N^{-\min(\alpha,d)} (\log N)^{s\alpha-2})$ for every even integer $\alpha \ge 1$, where $d$ is a parameter of the construction which can be chosen arbitrarily large and $N$ is the number of quadrature points. This convergence rate is known to be best possible up to some $\log N$ factors. We prove the result for the deterministic and also a randomized setting. The construction is based on a suitable extension of digital $(t,m,s)$-nets over the finite field $\integer_b$.