## Abstract

In order to approximate solutions of stochastic partial differential equations (SPDEs) that do not possess commutative noise, one has to simulate the involved iterated stochastic integrals. Recently, two approximation methods for iterated stochastic integrals in infinite dimensions were introduced in [8]. As a result of this, it is now possible to apply the Milstein scheme by Jentzen and Röckner [2] to equations that need not fulfill the commutativity condition. We prove that the order of convergence of the Milstein scheme can be maintained when combined with one of the two approximation methods for iterated stochastic integrals. However, we also have to consider the computational cost and the corresponding effective order of convergence for a meaningful comparison with other schemes. An analysis of the computational cost shows that, in dependence on the equation, a combination of the Milstein scheme with any of the two methods may be the preferred choice. Further, the Milstein scheme is compared to the exponential Euler scheme and we show for different SPDEs depending on the parameters describing, e.g., the regularity of the equation, which of the schemes achieves the highest effective order of convergence.

Original language | English |
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Title of host publication | MCQMC 2018: Monte Carlo and Quasi-Monte Carlo Methods |

Editors | Bruno Tuffin, Pierre L'Ecuyer |

Number of pages | 19 |

Volume | 324 |

Publisher | Springer, Cham |

Publication date | 20.05.2020 |

Pages | 503-521 |

ISBN (Print) | 978-3-030-43464-9 |

ISBN (Electronic) | 978-3-030-43465-6 |

DOIs | |

Publication status | Published - 20.05.2020 |

Event | 13th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing - Rennes, France Duration: 01.07.2018 → 06.07.2018 Conference number: 239389 |