Split-step balanced \(\theta \)-method for SDEs with non-globally Lipschitz continuous coefficients

DOI10.1016/j.amc.2021.126437OpenAlexW3201362699MaRDI QIDQ2246428

Yuelin Li, Wan-Rong Cao, Yu-Fen Liu

Publication date: 16 November 2021

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2021.126437

zbMATH Keywords

strong convergenceexponential stabilitynonlinear problemsmean-square contractionthe balanced method

Mathematics Subject Classification ID

Stability and convergence of numerical methods for ordinary differential equations (65L20) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (1)

Convergence Rates of Split-Step Theta Methods for SDEs with Non-Globally Lipschitz Diffusion Coefficients

Cites Work

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• Almost sure exponential stability of the backward Euler-Maruyama discretization for highly nonlinear stochastic functional differential equation
• Order-preserving strong schemes for SDEs with locally Lipschitz coefficients
• Divergence of the multilevel Monte Carlo Euler method for nonlinear stochastic differential equations
• Exponential mean square stability of numerical methods for systems of stochastic differential equations
• Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
• Theta schemes for SDDEs with non-globally Lipschitz continuous coefficients
• Convergence and stability of the semi-tamed Euler scheme for stochastic differential equations with non-Lipschitz continuous coefficients
• Compensated split-step balanced methods for nonlinear stiff SDEs with jump-diffusion and piecewise continuous arguments
• Convergence rates of the truncated Euler-Maruyama method for stochastic differential equations
• Numerical solution of stochastic differential equations with jumps in finance
• Split-step backward balanced Milstein methods for stiff stochastic systems
• Higher-order implicit strong numerical schemes for stochastic differential equations
• Mean-square stability of numerical schemes for stochastic differential systems
• Semi-implicit split-step numerical methods for a class of nonlinear stochastic differential equations with non-Lipschitz drift terms
• Strong convergence rates of modified truncated EM method for stochastic differential equations
• Convergence and stability of two classes of theta-Milstein schemes for stochastic differential equations
• The projected explicit Itô-Taylor methods for stochastic differential equations under locally Lipschitz conditions and polynomial growth conditions
• Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients
• Stability of the drift-implicit and double-implicit Milstein schemes for nonlinear SDEs
• On strong convergence of explicit numerical methods for stochastic delay differential equations under non-global Lipschitz conditions
• Almost sure exponential stability of implicit numerical solution for stochastic functional differential equation with extended polynomial growth condition
• Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients
• Balanced Implicit Methods for Stiff Stochastic Systems
• Mean-Square and Asymptotic Stability of the Stochastic Theta Method
• Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations
• Exponential Mean-Square Stability of Numerical Solutions to Stochastic Differential Equations
• The tamed Milstein method for commutative stochastic differential equations with non-globally Lipschitz continuous coefficients
• Split-step double balanced approximation methods for stiff stochastic differential equations
• Strong convergence of the tamed and the semi-tamed Euler schemes for stochastic differential equations with jumps under non-global Lipschitz condition
• Stability Analysis of Numerical Schemes for Stochastic Differential Equations
• A Fundamental Mean-Square Convergence Theorem for SDEs with Locally Lipschitz Coefficients and Its Applications
• A Note on the Rate of Convergence of the Euler–Maruyama Method for Stochastic Differential Equations

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