Balanced-Euler approximation schemes for stiff systems of stochastic differential equations

Hassan Ranjbar, Leila Torkzadeh, Kazem Nouri


This paper aims to design new families of balanced-Euler approximation schemes for the solutions of stiff stochastic differential systems. To prove the strong convergence, we use some fundamental inequalities such as the global Lipschitz condition and linear growth bound. The mean-square stability properties of our new schemes are analyzed. Also, numerical examples illustrate the accuracy and efficiency of the proposed schemes.


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