This paper aims to investigate the tamed Euler method for the random periodic solution of semilinear SDEs with one-sided Lipschitz coefficient.We introduce a novel approach to analyze mean-square error bounds of the n...This paper aims to investigate the tamed Euler method for the random periodic solution of semilinear SDEs with one-sided Lipschitz coefficient.We introduce a novel approach to analyze mean-square error bounds of the novel schemes,without relying on a priori high-order moment bound of the numerical approximation.The expected order-one mean square convergence is attained for the proposed scheme.Moreover,a numerical example is presented to verify our theoretical analysis.展开更多
In this paper,we introduce a new class of explicit numerical methods called the tamed stochastic Runge-Kutta-Chebyshev(t-SRKC)methods,which apply the idea of taming to the stochastic Runge-Kutta-Chebyshev(SRKC)methods...In this paper,we introduce a new class of explicit numerical methods called the tamed stochastic Runge-Kutta-Chebyshev(t-SRKC)methods,which apply the idea of taming to the stochastic Runge-Kutta-Chebyshev(SRKC)methods.The key advantage of our explicit methods is that they can be suitable for stochastic differential equations with non-globally Lipschitz coefficients and stiffness.Under certain non-globally Lipschitz conditions,we study the strong convergence of our methods and prove that the order of strong convergence is 1/2.To show the advantages of our methods,we compare them with some existing explicit methods(including the Euler-Maruyama method,balanced Euler-Maruyama method and two types of SRKC methods)through several numerical examples.The numerical results show that our t-SRKC methods are efficient,especially for stiff stochastic differential equations.展开更多
We present an adaptive control scheme of accumulative error to stabilize the unstable fixed point for chaotic systems which only satisfies local Lipschitz condition, and discuss how the convergence factor affects the ...We present an adaptive control scheme of accumulative error to stabilize the unstable fixed point for chaotic systems which only satisfies local Lipschitz condition, and discuss how the convergence factor affects the convergence and the characteristics of the final control strength. We define a minimal local Lipschitz coefficient, which can enlarge the condition of chaos control. Compared with other adaptive methods, this control scheme is simple and easy to implement by integral circuits in practice. It is also robust against the effect of noise. These are illustrated with numerical examples.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12471394,12371417)Natural Science Foundation of Changsha(No.kq2502101)。
文摘This paper aims to investigate the tamed Euler method for the random periodic solution of semilinear SDEs with one-sided Lipschitz coefficient.We introduce a novel approach to analyze mean-square error bounds of the novel schemes,without relying on a priori high-order moment bound of the numerical approximation.The expected order-one mean square convergence is attained for the proposed scheme.Moreover,a numerical example is presented to verify our theoretical analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.12101525,12071403)by the Natural Science Foundation of Hunan Province of China(Grant No.2023JJ40615)+1 种基金by the Research Foundation of Education Department of Hunan Province of China(Grant No.21A0108)by the Research Initiation Fund Project of Xiangtan University(Grant No.21QDZ16).
文摘In this paper,we introduce a new class of explicit numerical methods called the tamed stochastic Runge-Kutta-Chebyshev(t-SRKC)methods,which apply the idea of taming to the stochastic Runge-Kutta-Chebyshev(SRKC)methods.The key advantage of our explicit methods is that they can be suitable for stochastic differential equations with non-globally Lipschitz coefficients and stiffness.Under certain non-globally Lipschitz conditions,we study the strong convergence of our methods and prove that the order of strong convergence is 1/2.To show the advantages of our methods,we compare them with some existing explicit methods(including the Euler-Maruyama method,balanced Euler-Maruyama method and two types of SRKC methods)through several numerical examples.The numerical results show that our t-SRKC methods are efficient,especially for stiff stochastic differential equations.
基金supported by National Nature Science Foundation of China(Nos.61273088,10971120 and 61001099)Nature Science Foundation of Shandong Province(No.ZR2010FM010)
文摘We present an adaptive control scheme of accumulative error to stabilize the unstable fixed point for chaotic systems which only satisfies local Lipschitz condition, and discuss how the convergence factor affects the convergence and the characteristics of the final control strength. We define a minimal local Lipschitz coefficient, which can enlarge the condition of chaos control. Compared with other adaptive methods, this control scheme is simple and easy to implement by integral circuits in practice. It is also robust against the effect of noise. These are illustrated with numerical examples.
