Laurence Wilkes

most citedRandom-prime--fixed-vector randomised lattice-based algorithm for high-dimensional integration

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

math.NA2025

Error bounds for function approximation using generated sets

Ronald Cools, Dirk Nuyens, Laurence Wilkes

This paper explores the use of "generated sets" ${{k ζ} : k = 1, \dots, n}$ for function approximation in reproducing kernel Hilbert spaces which consist of mul…

math.NA2023

A randomised lattice rule algorithm with pre-determined generating vector and random number of points for Korobov spaces with $0 < α \leq 1/2$

Dirk Nuyens, Laurence Wilkes

In previous work (Kuo, Nuyens, Wilkes, 2023), we showed that a lattice rule with a pre-determined generating vector but random number of points can achieve the near optimal converg…

math.NA2023★ 1 cited

Random-prime--fixed-vector randomised lattice-based algorithm for high-dimensional integration

Frances Y. Kuo, Dirk Nuyens, Laurence Wilkes

We show that a very simple randomised algorithm for numerical integration can produce a near optimal rate of convergence for integrals of functions in the $d$ -dimensional weighted…

Error bounds for function approximation using generated sets

A randomised lattice rule algorithm with pre-determined generating vector and random number of points for Korobov spaces with 0<α≤1/2

Random-prime--fixed-vector randomised lattice-based algorithm for high-dimensional integration

A randomised lattice rule algorithm with pre-determined generating vector and random number of points for Korobov spaces with $0 < α \leq 1/2$