Adaptive step size stochastic runge-kutta method of order 1.5(1.0) for stochastic differential equations (SDEs)

Noor Julailah, Abd Mutalib and Norhayati, Rosli and Noor Amalina Nisa, Ariffin (2023) Adaptive step size stochastic runge-kutta method of order 1.5(1.0) for stochastic differential equations (SDEs). Mathematics and Statistics, 11 (1). pp. 183-190. ISSN 2332-2071. (Published)

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The stiff stochastic differential equations (SDEs) involve the solution with sharp turning points that permit us to use a very small step size to comprehend its behavior. Since the step size must be set up to be as small as possible, the implementation of the fixed step size method will result in high computational cost. Therefore, the application of variable step size method is needed where in the implementation of variable step size methods, the step size used can be considered more flexible. This paper devotes to the development of an embedded stochastic Runge-Kutta (SRK) pair method for SDEs. The proposed method is an adaptive step size SRK method. The method is constructed by embedding a SRK method of 1.0 order into a SRK method of 1.5 order of convergence. The technique of embedding is applicable for adaptive step size implementation, henceforth an estimate error at each step can be obtained. Numerical experiments are performed to demonstrate the efficiency of the method. The results show that the solution for adaptive step size SRK method of order 1.5(1.0) gives the smallest global error compared to the global error for fix step size SRK4, Euler and Milstein methods. Hence, this method is reliable in approximating the solution of SDEs.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Adaptive step size; Embedded stochastic runge-kutta; Stochastic differential equations
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Faculty/Division: Institute of Postgraduate Studies
Center for Mathematical Science
College of Engineering
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 05 Sep 2023 07:29
Last Modified: 05 Sep 2023 07:29
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