16 citations · 27 across the 6 of their papers we have counts for
5 papers · 1 filter
Challenges in Developing Great Quasi-Monte Carlo Software
Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell +2
Quasi-Monte Carlo (QMC) methods have developed over several decades. With the explosion in computational science, there is a need for great software that implements QMC algorithms.…
Adaptive Approximation for Multivariate Linear Problems with Inputs Lying in a Cone
Yuhan Ding, Fred J. Hickernell, Peter Kritzer +1
We study adaptive approximation algorithms for general multivariate linear problems where the sets of input functions are non-convex cones. While it is known that adaptive algorith…
An Adaptive Algorithm Employing Continuous Linear Functionals
Yuhan Ding, Fred J. Hickernell, Lluís Antoni Jiménez Rugama
Automatic algorithms attempt to provide approximate solutions that differ from exact solutions by no more than a user-specified error tolerance. This paper describes an automatic,…
Local Adaption for Approximation and Minimization of Univariate Functions
Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell +1
Most commonly used \emph{adaptive} algorithms for univariate real-valued function approximation and global minimization lack theoretical guarantees. Our new locally adaptive algori…
The Cost of Deterministic, Adaptive, Automatic Algorithms: Cones, Not Balls
Nicholas Clancy, Yuhan Ding, Caleb Hamilton +2
Automatic numerical algorithms attempt to provide approximate solutions that differ from exact solutions by no more than a user-specified error tolerance. The computational cost is…