Split S-ROCK Methods for High-Dimensional Stochastic Differential Equations

We propose explicit stochastic Runge–Kutta (RK) methods for high-dimensional Itô stochastic differential equations. By providing a linear error analysis and utilizing a Strang splitting-type approach, we construct them on the basis of orthogonal Runge–Kutta–Chebyshev methods of order 2. Our methods...

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Bibliographic Details
Published in:	Journal of scientific computing Vol. 97; no. 3; p. 62
Main Authors:	Komori, Yoshio, Burrage, Kevin
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2023 Springer Nature B.V
Subjects:	Accuracy Algorithms Approximation Chebyshev approximation Computational Mathematics and Numerical Analysis Differential equations Eigenvalues Error analysis Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical functions Mathematics Mathematics and Statistics Methods Numerical analysis Ordinary differential equations Random variables Runge-Kutta method Theoretical 65L05 65L06 60H10 Explicit method Orthogonal Runge–Kutta–Chebyshev method Itô stochastic differential equation Stiffness Weak second order approximation Noncommutative noise
ISSN:	0885-7474, 1573-7691
Online Access:	Get full text
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