Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators

TL;DR

This paper analyzes the asymptotic complexity of multilevel best linear unbiased estimators, especially in PDE models, demonstrating their optimality and comparing their complexity to Multilevel Monte Carlo through theoretical and numerical results.

Contribution

It provides an asymptotic complexity analysis of SAOBs, showing their optimality and relation to multilevel Monte Carlo, using Richardson extrapolation for analysis.

Findings

SAOBs have asymptotically optimal complexity among linear unbiased estimators.

The complexity of SAOBs is not larger than that of Multilevel Monte Carlo.

Numerical experiments confirm the theoretical complexity bounds.

Abstract

We study the computational complexity and variance of multilevel best linear unbiased estimators introduced in [D. Schaden and E. Ullmann, SIAM/ASA J. Uncert. Quantif., (2020)]. We specialize the results in this work to PDE-based models that are parameterized by a discretization quantity, e.g., the finite element mesh size. In particular, we investigate the asymptotic complexity of the so-called sample allocation optimal best linear unbiased estimators (SAOBs). These estimators have the smallest variance given a fixed computational budget. However, SAOBs are defined implicitly by solving an optimization problem and are difficult to analyze. Alternatively, we study a class of auxiliary estimators based on the Richardson extrapolation of the parametric model family. This allows us to provide an upper bound for the complexity of the SAOBs, showing that their complexity is optimal within a certain class of linear unbiased estimators. Moreover, the complexity of the SAOBs is not larger than the complexity of Multilevel Monte Carlo. The theoretical results are illustrated by numerical experiments with an elliptic PDE.