×

Continuous stage stochastic Runge-Kutta methods. (English) Zbl 1487.65014

Summary: In this work, a version of continuous stage stochastic Runge-Kutta (CSSRK) methods is developed for stochastic differential equations (SDEs). First, a general order theory of these methods is established by the theory of stochastic B-series and multicolored rooted tree. Then the proposed CSSRK methods are applied to three special kinds of SDEs and the corresponding order conditions are derived. In particular, for the single integrand SDEs and SDEs with additive noise, we construct some specific CSSRK methods of high order. Moreover, it is proved that with the help of different numerical quadrature formulas, CSSRK methods can generate corresponding stochastic Runge-Kutta (SRK) methods which have the same order. Thus, some efficient SRK methods are induced. Finally, some numerical experiments are presented to demonstrate those theoretical results.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
65L06 Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations

References:

[1] Mao, X., Stochastic Differential Equations and Applications (1997), New York: Horwood, New York · Zbl 0892.60057
[2] Liu, M.; Zhu, Y., Stability of a budworm growth model with random perturbations, Appl. Math. Lett., 79, 13-19 (2018) · Zbl 1459.92088 · doi:10.1016/j.aml.2017.11.020
[3] Liu, M.; Yu, L., Stability of a stochastic logistic model under regime switching, Adv. Differ. Equ. (2015) · Zbl 1422.60100 · doi:10.1186/s13662-015-0666-5
[4] Li, X.; Ma, Q.; Yang, H.; Yuan, C., The numerical invariant measure of stochastic differential equations with Markovian switching, SIAM J. Numer. Anal., 56, 3, 1435-1455 (2018) · Zbl 1388.60098 · doi:10.1137/17M1143927
[5] Huang, C., Exponential mean square stability of numerical methods for systems of stochastic differential equations, J. Comput. Appl. Math., 236, 16, 4016-4026 (2012) · Zbl 1251.65003 · doi:10.1016/j.cam.2012.03.005
[6] Li, X.; Zhang, C.; Ma, Q.; Ding, X., Discrete gradient methods and linear projection methos for preserving a conserved quantity of stochastic differential equations, Int. J. Comput. Math., 95, 12, 2511-2524 (2018) · Zbl 1499.60196 · doi:10.1080/00207160.2017.1408803
[7] Zong, X.; Wu, F.; Huang, C., Preserving exponential mean square stability and decay rates in two classes of theta approximations of stochastic differential equations, J. Differ. Equ. Appl., 20, 7, 1091-1111 (2014) · Zbl 1291.60143 · doi:10.1080/10236198.2014.892934
[8] Mao, W.; Hu, L.; Mao, X., Approximate solutions for a class of doubly perturbed stochastic differential equations, Adv. Differ. Equ. (2018) · Zbl 1445.60042 · doi:10.1186/s13662-018-1490-5
[9] Yin, Z.; Gan, S., Chebyshev spectral collocation method for stochastic delay differential equations, Adv. Differ. Equ. (2015) · Zbl 1422.60109 · doi:10.1186/s13662-015-0447-1
[10] Wang, X.; Gan, S.; Wang, D., A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise, BIT Numer. Math., 52, 3, 741-771 (2012) · Zbl 1259.65007 · doi:10.1007/s10543-012-0370-8
[11] Tan, J.; Yang, H.; Men, W.; Guo, Y., Construction of positivity preserving numerical method for jump-diffusion option pricing models, J. Comput. Appl. Math., 320, 96-100 (2017) · Zbl 1371.60123 · doi:10.1016/j.cam.2017.02.006
[12] Hu, L.; Li, X.; Mao, X., Convergence rate and stability of the truncated Euler-Maruyama method for stochastic differential equations, J. Comput. Appl. Math., 337, 274-289 (2018) · Zbl 1458.65008 · doi:10.1016/j.cam.2018.01.017
[13] Tan, J.; Mu, Z.; Guo, Y., Convergence and stability of the compensated split-step θ-method for stochastic differential equations with jumps, Adv. Differ. Equ. (2014) · Zbl 1417.34137 · doi:10.1186/1687-1847-2014-209
[14] Zhou, W.; Zhang, J.; Hong, J.; Song, S., Stochastic symplectic Runge-Kutta methods for the strong approximation of Hamiltonian systems with additive noise, J. Comput. Appl. Math., 325, 134-148 (2017) · Zbl 1367.65015 · doi:10.1016/j.cam.2017.04.050
[15] Butcher, J. C., An algebraic theory of integration methods, Math. Comput., 26, 79-106 (1972) · Zbl 0258.65070 · doi:10.1090/S0025-5718-1972-0305608-0
[16] Tang, W.; Lang, G.; Luo, X., Construction of symplectic (partitioned) Runge-Kutta methods type methods with continuous stage, Appl. Math. Comput., 286, 279-287 (2016) · Zbl 1410.65264
[17] Tang, W., A note on continuous-stage Runge-Kutta methodsconstruction of Runge-Kutta type methods for solving ordinary differential equations, Appl. Math. Comput., 339, 231-241 (2018) · Zbl 1429.65154 · doi:10.1016/j.cam.2017.10.019
[18] Kloeden, P. E.; Platen, E., Numerical Solution of Stochastic Differential Equations (1992), Berlin: Springer, Berlin · Zbl 0752.60043 · doi:10.1007/978-3-662-12616-5
[19] Tian, T.; Burrage, K., Implicit Taylor methods for stiff stochastic differential equations, Appl. Numer. Math., 38, 167-185 (2001) · Zbl 0983.65007 · doi:10.1016/S0168-9274(01)00034-4
[20] Debrabant, K., Cheap arbitrary high order methods for single integrand sdes, BIT Numer. Math., 57, 153-168 (2017) · Zbl 1364.65012 · doi:10.1007/s10543-016-0619-8
[21] Debrabant, K., B-series analysis of stochastic Runge-Kutta methods that use an iterative scheme to compute their integral stage values, SIAM J. Numer. Anal., 47, 181-203 (2008) · Zbl 1188.65006 · doi:10.1137/070704307
[22] Miyatake, Y.; Butcher, J. C., A characterization of energy-preserving methods and the construction of parallel integrators for Hamiltonian systems, SIAM J. Numer. Anal., 54, 1993-2013 (2016) · Zbl 1342.65232 · doi:10.1137/15M1020861
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.