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Journal ArticleDOI

Strong convergence of split-step theta methods for non-autonomous stochastic differential equations

TLDR
The strong convergence of the split-step theta methods for non-autonomous stochastic differential equations under a linear growth condition on the diffusion coefficient and a one-sided Lipschitz condition is proved.
Abstract
In this paper, we first prove the strong convergence of the split-step theta methods for non-autonomous stochastic differential equations under a linear growth condition on the diffusion coefficient and a one-sided Lipschitz condition on the drift coefficient. Then, if the drift coefficient satisfies a polynomial growth condition, we further get the rate of convergence. Finally, the obtained results are supported by numerical experiments.

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Citations
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Dissertation

On the theory of superconductivity

TL;DR: In this article, it was shown that a metal should be superconductive if a set of corners of a Brillouin zone is lying very near the Fermi surface, considered as a sphere, which limits the region in the momentum space completely filled with electrons.
Journal ArticleDOI

The truncated Milstein method for stochastic differential equations with commutative noise

TL;DR: Inspired by the truncated EulerMaruyama method developed in Mao (2015), this paper proposed a truncated Milstein method with strong convergence rate close to 1 for a class of highly non-linear stochastic differential equations with commutative noise.
Posted Content

Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities

TL;DR: This work proves that full- Discrete exponential Euler and full-discrete linear-implicit Euler approximations diverge strongly and numerically weakly in the case of stochastic Allen-Cahn equations.

Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities

TL;DR: In this article, it was shown that the divergence phenomenon also holds for stochastic partial differential equations with superlinearly growing nonlinearities, such as the Allen-Cahn equations.
Journal ArticleDOI

Discrete gradient methods and linear projection methods for preserving a conserved quantity of stochastic differential equations

TL;DR: The relationship of the two classes of methods for preserving a conserved quantity is proved, which is, the constructed linear projection methods can be considered as a subset of the constructed discrete gradient methods.
References
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Book

Numerical Solution of Stochastic Differential Equations

TL;DR: In this article, a time-discrete approximation of deterministic Differential Equations is proposed for the stochastic calculus, based on Strong Taylor Expansions and Strong Taylor Approximations.
Journal ArticleDOI

An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations

Desmond J. Higham
- 01 Mar 2001 - 
TL;DR: The article is built around $10$ MATLAB programs, and the topics covered include stochastic integration, the Euler--Maruyama method, Milstein's method, strong and weak convergence, linear stability, andThe stochastics chain rule.
Dissertation

On the theory of superconductivity

TL;DR: In this article, it was shown that a metal should be superconductive if a set of corners of a Brillouin zone is lying very near the Fermi surface, considered as a sphere, which limits the region in the momentum space completely filled with electrons.

Numerical Solution of Stochastic Differential Equations

Faniran T
TL;DR: In this article, the main concepts and techniques necessary for someone who wishes to carry out numerical experiments involving stochastic differential equations (SDEs) are described and compared. And the convergence of Euler-Maruyama and Milstein and Taylor approximate solutions are compared.
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