Improved Euler-Maruyama method for numerical solution of the Itô stochastic differential systems by composite previous-current-step idea

DOI10.1007/s00009-018-1187-8zbMath1393.60063OpenAlexW2805545784MaRDI QIDQ723870

Leila Torkzadeh, Hassan Ranjbar, Kazem Nouri

Publication date: 24 July 2018

Published in: Mediterranean Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00009-018-1187-8

zbMATH Keywords

strong solutionmean-square convergencestochastic differential systemsEuler-Maruyama methodcomposite previous-current-step idea

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Rate of convergence, degree of approximation (41A25) Strong solutions to PDEs (35D35)

Related Items (5)

Improving split-step forward methods by ODE solver for stiff stochastic differential equations ⋮ \(\mathrm{L}^p\)-calculus approach to the random autonomous linear differential equation with discrete delay ⋮ Mean-square convergence analysis of the semi-implicit scheme for stochastic differential equations driven by the Wiener processes ⋮ Solving the stochastic differential systems with modified split-step Euler-Maruyama method ⋮ Modified stochastic theta methods by ODEs solvers for stochastic differential equations

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Solving random homogeneous linear second-order differential equations: a full probabilistic description
• Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
• The truncated Euler-Maruyama method for stochastic differential equations
• Mean square convergence of the numerical solution of random differential equations
• Numerical solution based on hat functions for solving nonlinear stochastic Itô Volterra integral equations driven by fractional Brownian motion
• A mean square chain rule and its application in solving the random Chebyshev differential equation
• Convergence and stability of the split-step \(\theta \)-method for stochastic differential equations
• Economical Runge-Kutta methods with strong global order one for stochastic differential equations
• Split-step forward methods for stochastic differential equations
• Convergence rates of the truncated Euler-Maruyama method for stochastic differential equations
• Economical Runge-Kutta methods for numerical solution of stochastic differential equations
• Nordsieck representation of two-step Runge-Kutta methods for ordinary differential equations
• Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients
• Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations
• Weak Second Order Conditions for Stochastic Runge--Kutta Methods
• Split-step Adams–Moulton Milstein methods for systems of stiff stochastic differential equations
• The composite Euler method for stiff stochastic differential equations
• Implicit Taylor methods for stiff stochastic differential equations

This page was built for publication: Improved Euler-Maruyama method for numerical solution of the Itô stochastic differential systems by composite previous-current-step idea