Issues in the software implementation of stochastic numerical Runge-Kutta

Abstract

This paper discusses stochastic numerical methods of Runge-Kutta type with weak and strong convergences for systems of stochastic differential equations in Itô form. At the beginning we give a brief overview of the stochastic numerical methods and information from the theory of stochastic differential equations. Then we motivate the approach to the implementation of these methods using source code generation. We discuss the implementation details and the used programming languages and libraries

in English, in Russian

References in corpus (2)

• Stochastic symplectic Runge-Kutta methods for the strong approximation of Hamiltonian systems with additive noise
• Stochastic Runge-Kutta Software Package for Stochastic Differential Equations

Cited by in corpus (2)

• An Analysis of Approximation Algorithms for Iterated Stochastic Integrals and a Julia and MATLAB Simulation Toolbox
• Using a template engine as a computer algebra tool