Monte Carlo integration of $C^r$ functions with adaptive variance reduction: an asymptotic analysis

TL;DR

This paper analyzes adaptive variance reduction techniques for Monte Carlo integration of smooth functions, showing that adaptivity can significantly improve asymptotic convergence rates especially for functions with rapidly changing derivatives.

Contribution

It introduces adaptive variance reduction methods that improve asymptotic convergence in Monte Carlo integration of $C^r$ functions, surpassing nonadaptive approaches.

Findings

Adaptive methods substantially lower asymptotic error factors.

Adaptive techniques outperform uniform partitioning for functions with rapid derivative variation.

Proposed methods are easy to implement and suitable for automatic integration.

Abstract

The theme of the present paper is numerical integration of $C^r$ functions using randomized methods. We consider variance reduction methods that consist in two steps. First the initial interval is partitioned into subintervals and the integrand is approximated by a piecewise polynomial interpolant that is based on the obtained partition. Then a randomized approximation is applied on the difference of the integrand and its interpolant. The final approximation of the integral is the sum of both. The optimal convergence rate is already achieved by uniform (nonadaptive) partition plus the crude Monte Carlo; however, special adaptive techniques can substantially lower the asymptotic factor depending on the integrand. The improvement can be huge in comparison to the nonadaptive method, especially for functions with rapidly varying $r$th derivatives, which has serious implications for practical computations. In addition, the proposed adaptive methods are easily implementable and can be well used for automatic integration.