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Multilevel Monte Carlo methods

Published online by Cambridge University Press:  27 April 2015

Michael B. Giles
Michael B. Giles*
Affiliation:
Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK E-mail: mike.giles@maths.ox.ac.uk
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Abstract

Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.

In this article, we review the ideas behind the multilevel Monte Carlo method, and various recent generalizations and extensions, and discuss a number of applications which illustrate the flexibility and generality of the approach and the challenges in developing more efficient implementations with a faster rate of convergence of the multilevel correction variance.

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Type
Research Article
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Acta Numerica , Volume 24 , 01 May 2015 , pp. 259 - 328
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© Cambridge University Press, 2015 

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