An explicit two-stage truncated Runge–Kutta method for nonlinear stochastic differential equations
- Department of Mathematics, Faculty of Science, Razi University, Kermanshah, 67149, IR
Published in Issue 2023-01-12
How to Cite
Haghighi, A. (2023). An explicit two-stage truncated Runge–Kutta method for nonlinear stochastic differential equations. Mathematical Sciences, 18(3 (September 2024). https://doi.org/10.1007/s40096-023-00508-1
Abstract
Abstract
In this paper, we construct a two-stage truncated Runge–Kutta (TSRK2) method for highly nonlinear stochastic differential equations (SDEs) with non-global Lipschitz coefficients. TSRK2 is an explicit method and includes some free parameters that can extend the accuracy of the results and stability regions. We show that this method can achieve a strong convergence rate arbitrarily close to half under local Lipschitz and Khasiminskii conditions. We study the mean square stability properties (MS-stability) of the method based on a scalar linear test equation with multiplicative noise, and the advantages of our results are highlighted by comparing them with those of the truncated Euler–Maruyama method. We also analyze the asymptotic stability properties of the method. We show that the proposed method can preserve the asymptotic stability of the original system under mild conditions. Finally, we report some numerical experiments to illustrate the effectiveness of the proposed method. As a result, we show that the new method has good properties not only in terms of practical errors but also in terms of stability.
Keywords
- Nonlinear stochastic differential equations,
- Truncated methods,
- Runge–Kutta method,
- Asymptotic stability
References
- Buckwar et al. (2022) A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model (pp. 191-220) https://doi.org/10.1016/j.apnum.2022.04.018
- Burrage et al. (2004) Numerical methods for strong solutions of stochastic differential equations: an overview (pp. 373-402) https://doi.org/10.1098/rspa.2003.1247
- Gan et al. (2020) Tamed Runge–Kutta methods for SDEs with super-linearly growing drift and diffusion coefficients (pp. 379-402) https://doi.org/10.1016/j.apnum.2019.11.014
- Gillespie (2000) The chemical Langevin equation 113(1) (pp. 297-306) https://doi.org/10.1063/1.481811
- Guo et al. (2018) A note on the partially truncated Euler–Maruyama method (pp. 157-170) https://doi.org/10.1016/j.apnum.2018.04.004
- Guo et al. (2017) The partially truncated Euler–Maruyama method and its stability and boundedness (pp. 235-251) https://doi.org/10.1016/j.apnum.2017.01.010
- Guo et al. (2018) The truncated Milstein method for stochastic differential equations with commutative noise (pp. 298-310) https://doi.org/10.1016/j.cam.2018.01.014
- Haghighi et al. (2016) Diagonally drift-implicit Runge–Kutta methods of strong order one for stiff stochastic differential systems (pp. 82-93) https://doi.org/10.1016/j.cam.2015.02.036
- Higham et al. (2002) Strong convergence of Euler-type methods for nonlinear stochastic differential equations 40(3) (pp. 1041-1063) https://doi.org/10.1137/S0036142901389530
- Hu et al. (2018) Convergence rate and stability of the truncated Euler–Maruyama method for stochastic differential equations (pp. 274-289) https://doi.org/10.1016/j.cam.2018.01.017
- Hutzenthaler et al. (2011) Strong and weak divergence in finite time of Euler’s method for stochastic differential equations with non-globally Lipschitz continuous coefficients (pp. 1563-1576)
- Hutzenthaler et al. (2012) Strong convergence of an explicit numerical method for SDEs with non-globally Lipschitz continuous coefficients 22(4) (pp. 1611-1641) https://doi.org/10.1214/11-AAP803
- Kloeden and Platen (1992) Springer https://doi.org/10.1007/978-3-662-12616-5
- Komori and Burrage (2013) Strong first order S-ROCK methods for stochastic differential equations (pp. 261-274) https://doi.org/10.1016/j.cam.2012.10.026
- Lewis (2000) Finance Press
- Li and Yin (2020) Explicit Milstein schemes with truncation for nonlinear stochastic differential equations: convergence and its rate https://doi.org/10.1016/j.cam.2020.112771
- Liu and Mao (2013) Strong convergence of the stopped Euler–Maruyama method for nonlinear stochastic differential equations (pp. 389-400)
- Mao (2002) A note on the LaSalle-type theorems for stochastic differential delay equations 268(1) (pp. 125-142) https://doi.org/10.1006/jmaa.2001.7803
- Mao (2007) Horwood
- Mao (2015) The truncated Euler–Maruyama method for stochastic differential equations (pp. 370-384) https://doi.org/10.1016/j.cam.2015.06.002
- Mao (2016) Convergence rates of the truncated Euler–Maruyama method for stochastic differential equations (pp. 362-375) https://doi.org/10.1016/j.cam.2015.09.035
- Mao and Szpruch (2013) Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients (pp. 14-28) https://doi.org/10.1016/j.cam.2012.08.015
- Mao and Yuan (2006) Imperial College Press https://doi.org/10.1142/p473
- Milstein (1995) Kluwer Academic https://doi.org/10.1007/978-94-015-8455-5
- Milstein and Tretyakov (2004) Springer https://doi.org/10.1007/978-3-662-10063-9
- Nouri (2022) Improving split-step forward methods by ODE solver for stiff stochastic differential equations 16(1) (pp. 51-57) https://doi.org/10.1007/s40096-021-00392-7
- Nouri et al. (2018) Improved Euler–Maruyama method for numerical solution of the Itô stochastic differential systems by composite previous-current-step idea 15(3) https://doi.org/10.1007/s00009-018-1187-8
- Øksendal, B.: Stochastic Differential Equations. An Introduction With Applications. In: Universitext, 6th edn. Springer-Verlag, Berlin (2003)
- Rößler (2010) Runge–Kutta methods for the strong approximation of solutions of stochastic differential equations 48(3) (pp. 922-952)
- Sabanis (2013) A note on tamed Euler approximations (pp. 1-10) https://doi.org/10.1214/ECP.v18-2824
- Tian and Burrage (2001) Implicit Taylor methods for stiff stochastic differential equations 38(1) (pp. 167-185) https://doi.org/10.1016/S0168-9274(01)00034-4
- Torkzadeh (2021) Mean-square convergence analysis of the semi-implicit scheme for stochastic differential equations driven by the Wiener processes https://doi.org/10.1007/s40096-021-00440-2
- Tretyakov and Zhang (2013) A fundamental mean-square convergence theorem for SDEs with locally Lipschitz coefficients and its applications 51(6) (pp. 3135-3162) https://doi.org/10.1137/120902318
- Wang and Gan (2013) The tamed Milstein method for commutative stochastic differential equations with non-globally Lipschitz continuous coefficients 19(3) (pp. 466-490) https://doi.org/10.1080/10236198.2012.656617
- Yang and Huang (2022) Convergence and stability of modified partially truncated Euler–Maruyama method for nonlinear stochastic differential equations with hölder continuous diffusion coefficient https://doi.org/10.1016/j.cam.2021.113895
- Yang et al. (2020) The truncated Euler–Maruyama method for stochastic differential equations with hölder diffusion coefficients https://doi.org/10.1016/j.cam.2019.112379
10.1007/s40096-023-00508-1