线性随机时滞微分方程裂步Milstein方法的收敛性(英文)

给出一个新的求解线性随机时滞微分方程的显式分裂步长Milstein格式.运用ItoTaylor展开式证明该格式相对于已有的求解随机时滞微分方程的分裂步长方法而言具有更好的收敛性.数值实验验证了理论分析的正确性.     

中立型随机延迟微分方程Milstein方法的均方稳定性

本文讨论Milstein方法用于求解线性中立型随机延迟微分方程初值问题时数值解的稳定性,给出了Milstein方法均方稳定的一个充分条件.文末的数值试验证实了本文所获理论结果的正确性.     

随机采样下随机微分方程Milstein方法的渐近误差

随机微分方程数值解的误差问题在度量离散化对冲的金融风险方面有着重要的应用.众所周知,等距采样下Ito型随机微分方程的Euler方法的收敛速度为1/n~(1/2),而Milstein方法是Euler方法的一种修正,可以将收敛速度提高到1/n,非等距、随机采样在一定程度上也能提高收敛速度.本文给出在非等距、随机采样下由一列连续局部鞅驱动的随机微分方程的Milstein方法的误差过程的渐近(弱收敛)结果.     

随机延迟微分方程Milstein方法的均方稳定性

本文以线性随机延迟微分方程为试验方程研究了随机延迟微分方程的Milstein方法的稳定性,给出了均方稳定的充分条件,所得结果表明Milstein方法能保持试验方程解的稳定性.完成了相关的数值试验以验证所得结论的正确性.     

随机延迟微分方程的Milstein方法的非线性均方稳定性

本文针对一般的非线性随机延迟微分方程,证明了当系统理论解满足均方稳定性条件时,则当方程的漂移和扩散项满足一定的条件时,Milstein方法也是均方稳定的.数学实验进一步验证了我们的结论.     

imaNon-Lipschitz条件的随机微分方程的逼近定理(英文)

本文给出了Non Lipschitz条件下的随机微分方程的一个逼近定理 .     

求解比例延迟微分方程的Rosenbrock方法的渐近稳定性(英文)

本文讨论了一类Rosenbrock方法求解比例延迟微分方程,y′(t)=λy(t) μy(qt),λ,μ∈C,0     

有随机投资回报的随机保费模型的渐近破产概率(英文)

本文研究了随机投资回报环境下扰动的随机保费模型的破产问题.利用鞅方法和随机分析的理论讨论了盈余过程的一些基本性质,得到了一个可以用来求解破产时刻的Laplace变换的积分微分方程,结果推广了已有的随机投资问报风险模型的结论.     

具有C~1-扩散系统的Stratonovich随机微分方程(英文)

本文研究了具有C1扩散系数的Stratonovich随机微分方程的强解的存在唯一性.     

Three-stage Stiffly Accurate Runge-Kutta Methods for Stiff Stochastic Differential Equations

In this paper we discuss diagonally implicit and semi-implicit methods based on the three-stage stiffly accurate Runge-Kutta methods for solving Stratonovich stochastic differential equations（SDEs）.Two methods,a three-stage stiffly accurate semi-implicit（SASI3） method and a three-stage stiffly accurate diagonally implicit （SADI3） method,are constructed in this paper.In particular,the truncated random variable is used in the implicit method.The stability properties and numerical results show the effectiveness of these methods in the pathwise approximation of stiff SDEs.     

一类随机延迟微分代数系统的Euler-Maruyama方法

我们主要构造了数值求解一类1指标随机延迟微分代数系统的Euler-Maruyama方法,并且证明用该方法求解此类问题可达到1/2阶均方收敛.最后的效值试验验证了方法的有效性及所获结论的正确性.     

非线性中立型随机延迟微分方程随机θ方法的稳定性

本文研究非线性中立型随机延迟微分方程随机θ方法的均方稳定性.在方程解析解均方稳定的条件下,证明了如下结论:当θ∈[0,1/2)时,随机θ方法对于适当小的时间步长是均方稳定的;当θ∈[1/2,1]时,随机θ方法对于任意步长都是均方稳定的.数值结果验证了所获结论的正确性.     

Implicit Stochastic Runge–Kutta Methods for Stochastic Differential Equations

In this paper we construct implicit stochastic Runge–Kutta (SRK) methods for solving stochastic differential equations of Stratonovich type. Instead of using the increment of a Wiener process, modified random variables are used. We give convergence conditions of the SRK methods with these modified random variables. In particular, the truncated random variable is used. We present a two-stage stiffly accurate diagonal implicit SRK (SADISRK2) method with strong order 1.0 which has better numerical behaviour than extant methods. We also construct a five-stage diagonal implicit SRK method and a six-stage stiffly accurate diagonal implicit SRK method with strong order 1.5. The mean-square and asymptotic stability properties of the trapezoidal method and the SADISRK2 method are analysed and compared with an explicit method and a semi-implicit method. Numerical results are reported for confirming convergence properties and for comparing the numerical behaviour of these methods. This revised version was published online in July 2006 with corrections to the Cover Date.     

随机微分方程欧拉格式算法分析

首先给出了线性随机微分方程的欧拉格式算法,然后给出了非线性随机微分方程变步长的欧拉格式算法,接着讨论了其对初值的连续依赖性和收敛性.     

Two-Stage Stochastic Runge-Kutta Methods for Stochastic Differential Equations

In this paper we discuss two-stage diagonally implicit stochastic Runge-Kutta methods with strong order 1.0 for strong solutions of Stratonovich stochastic differential equations. Five stochastic Runge-Kutta methods are presented in this paper. They are an explicit method with a large MS-stability region, a semi-implicit method with minimum principal error coefficients, a semi-implicit method with a large MS-stability region, an implicit method with minimum principal error coefficients and another implicit method. We also consider composite stochastic Runge-Kutta methods which are the combination of semi-implicit Runge-Kutta methods and implicit Runge-Kutta methods. Two composite methods are presented in this paper. Numerical results are reported to compare the convergence properties and stability properties of these stochastic Runge-Kutta methods.     

Runge–Kutta Methods for Itô Stochastic Differential Equations with Scalar Noise

A general class of stochastic Runge–Kutta methods for Itô stochastic differential equation systems w.r.t. a one-dimensional Wiener process is introduced. The colored rooted tree analysis is applied to derive conditions for the coefficients of the stochastic Runge–Kutta method assuring convergence in the weak sense with a prescribed order. Some coefficients for new stochastic Runge–Kutta schemes of order two are calculated explicitly and a simulation study reveals their good performance.     

Convergence and Stability of the Truncated Euler-Maruyama Method for Stochastic Differential Equations with Piecewise Continuous Arguments

In this paper, we develop the truncated Euler-Maruyama (EM) method for stochastic differential equations with piecewise continuous arguments (SDEPCAs), and consider the strong convergence theory under the local Lipschitz condition plus the Khasminskii-type condition. The order of convergence is obtained. Moreover, we show that the truncated EM method can preserve the exponential mean square stability of SDEPCAs. Numerical examples are provided to support our conclusions.     

带Caputo导数的变分数阶随机微分方程的Euler-Maruyama方法

该文构造了Euler-Maruyama(EM)方法求解一类带Caputo导数的变分数阶随机微分方程. 首先, 证明了该方程的适定性; 然后, 详细推导出对应的EM方法, 并对该方法进行了强收敛性的分析, 通过使用EM方法的连续形式证明了其强收敛阶为β-0.5, 其中β是Caputo导数的阶数,且满足0.5 < β < 1. 最后, 通过数值实验验证了理论分析结果的正确性.