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Bibliographic Details
Main Authors: Hudson, Thomas, Helfert, Sarah, Li, Xingjie Helen
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2503.19203
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author Hudson, Thomas
Helfert, Sarah
Li, Xingjie Helen
author_facet Hudson, Thomas
Helfert, Sarah
Li, Xingjie Helen
contents We revisit the numerical stability of four well-established explicit stochastic integration schemes through a new generic benchmark stochastic differential equation designed to assess asymptotic statistical accuracy and stability properties. This one-parameter benchmark equation is derived from a general one-dimensional first-order SDE using spatio-temporal nondimensionalization and is employed to evaluate the performance of the (1) Euler-Maruyama, (2) Milstein, (3) Stochastic Heun, and (4) three-stage Runge-Kutta schemes. Our findings reveal that lower-order schemes can outperform higher-order ones over a range of time step sizes, depending on the benchmark parameters and application context. The theoretical results are validated through a series of numerical experiments, and we discuss their implications for more general applications, including a nonlinear example. Our results suggest that the insights obtained from the linear benchmark problem provide reliable guidance for time-stepping strategies when simulating nonlinear SDEs.
format Preprint
id arxiv_https___arxiv_org_abs_2503_19203
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Numerical stability revisited: A family of benchmark problems for the analysis of explicit stochastic differential equation integrators
Hudson, Thomas
Helfert, Sarah
Li, Xingjie Helen
Numerical Analysis
60H35, 65L20
We revisit the numerical stability of four well-established explicit stochastic integration schemes through a new generic benchmark stochastic differential equation designed to assess asymptotic statistical accuracy and stability properties. This one-parameter benchmark equation is derived from a general one-dimensional first-order SDE using spatio-temporal nondimensionalization and is employed to evaluate the performance of the (1) Euler-Maruyama, (2) Milstein, (3) Stochastic Heun, and (4) three-stage Runge-Kutta schemes. Our findings reveal that lower-order schemes can outperform higher-order ones over a range of time step sizes, depending on the benchmark parameters and application context. The theoretical results are validated through a series of numerical experiments, and we discuss their implications for more general applications, including a nonlinear example. Our results suggest that the insights obtained from the linear benchmark problem provide reliable guidance for time-stepping strategies when simulating nonlinear SDEs.
title Numerical stability revisited: A family of benchmark problems for the analysis of explicit stochastic differential equation integrators
topic Numerical Analysis
60H35, 65L20
url https://arxiv.org/abs/2503.19203