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An Antithetic Multilevel Monte Carlo-Milstein Scheme for Stochastic Partial Differential Equations
by A. Haji-Ali and A. Stein
(Report number 2023-33)
Abstract
We present a novel multilevel Monte Carlo approach for estimating quantities
of interest for stochastic partial differential equations (SPDEs). Drawing
inspiration from [Giles and Szpruch: Antithetic multilevel Monte Carlo
estimation for multi-dimensional SDEs without Lévy area simulation, Annals of
Appl. Prob., 2014], we extend the antithetic Milstein scheme for
finite-dimensional stochastic differential equations to Hilbert space-valued
SPDEs. Our method has the advantages of both Euler and Milstein
discretizations, as it is easy to implement and does not involve intractable
Lévy area terms. Moreover, the antithetic correction in our method leads to
the same variance decay in a MLMC algorithm as the standard Milstein method,
resulting in significantly lower computational complexity than a corresponding
MLMC Euler scheme. Our approach is applicable to a broader range of non-linear
diffusion coefficients and does not require any commutative properties. The key
component of our MLMC algorithm is a truncated Milstein-type time stepping
scheme for SPDEs, which accelerates the rate of variance decay in the MLMC
method when combined with an antithetic coupling on the fine scales. We combine
the truncated Milstein scheme with appropriate spatial discretizations and
noise approximations on all scales to obtain a fully discrete scheme and show
that the antithetic coupling does not introduce an additional bias.
Keywords: Stochastic Partial Differential Equations, Multilevel Monte Carlo, Milstein Scheme, Variance Reduction, Antithetic Variates.
BibTeX@Techreport{HS23_1070, author = {A. Haji-Ali and A. Stein}, title = {An Antithetic Multilevel Monte Carlo-Milstein Scheme for Stochastic Partial Differential Equations}, institution = {Seminar for Applied Mathematics, ETH Z{\"u}rich}, number = {2023-33}, address = {Switzerland}, url = {https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2023/2023-33.pdf }, year = {2023} }
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