Multilevel Monte Carlo metamodeling

Imry Rosenbaum, Jeremy Staum

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Approximating the function that maps the input parameters of the simulation model to the expectation of the simulation output is an important and challenging problem in stochastic simulation metamodeling. Because an expectation is an integral, this function approximation problem can be seen as parametric integration - approximating the function that maps a parameter vector to the integral of an integrand that depends on the parameter vector. S. Heinrich and coauthors have proved that the multilevel Monte Carlo (MLMC) method improves the computational complexity of parametric integration, under some conditions. We prove similar results under different conditions that are more applicable to stochastic simulation metamodeling problems in operations research. We also propose a practical MLMC procedure for stochastic simulation metamodeling with user-driven error tolerance. In our simulation experiments, this procedure was up to tens of thousands of times faster than standard Monte Carlo.

Original languageEnglish (US)
Pages (from-to)1062-1077
Number of pages16
JournalOperations Research
Volume65
Issue number4
DOIs
StatePublished - Jul 1 2017

Keywords

  • Design of experiments
  • Efficiency

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

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