A Note on the Importance of Weak Convergence Rates for SPDE Approximations in Multilevel Monte Carlo Schemes

  • Annika LangEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 163)


It is a well-known rule of thumb that approximations of stochastic partial differential equations have essentially twice the order of weak convergence compared to the corresponding order of strong convergence. This is already known for many approximations of stochastic (ordinary) differential equations while it is recent research for stochastic partial differential equations. In this note it is shown how the availability of weak convergence results influences the number of samples in multilevel Monte Carlo schemes and therefore reduces the computational complexity of these schemes for a given accuracy of the approximations.


Stochastic partial differential equations Multilevel Monte Carlo methods Finite element approximations Weak error analysis Stochastic heat equation 



This research was supported in part by the Knut and Alice Wallenberg foundation as well as the Swedish Research Council under Reg. No. 621-2014-3995. The author thanks Lukas Herrmann, Andreas Petersson, and two anonymous referees for helpful comments.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematical SciencesChalmers University of Technology and University of GothenburgGothenburgSweden

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