Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators

We study the computational complexity and variance of multilevel best linear unbiased estimators introduced in [D. Schaden and E. Ullmann, SIAM/ASA J. Uncertain. Quantif., 8 (2020), pp. 601- 635]. 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.

Automated summary

The study focuses on the computational complexity and variance of best linear unbiased estimators for PDE-based models, with a particular interest in sample allocation optimal best linear unbiased estimators (SAOBs). Results show that SAOBs have optimal complexity within a certain class of linear unbiased estimators, and their complexity is not higher than that of multilevel Monte Carlo methods.