An Algorithm-Driven Approach to Error Analysis for Multidimensional Integration

Abstract

Most error analyses for numerical integration algorithms specify the space of integrands and then determine the convergence rate for a particular algorithm or the optimal algorithm. This article takes a different perspective of specifying the convergence rate and then finding the largest space of integrands for which the algorithm gives that desired rate. Both worst-case and randomized error analyses are provided.