一类带乘性噪声随机分数阶微分方程Euler方法的弱收敛性与弱稳定性

本文主要在带加性噪声随机分数阶微分方程的基础上,研究了一类更为困难的带乘性噪声随机分数阶微分方程Euler方法的弱收敛性与弱稳定性,并得到了类似的结论.首先构造了数值求解带乘性噪声随机分数阶微分方程的Euler方法,然后证明当分数阶α满足0α1/2时,该方法是1/2-α阶弱收敛的和弱稳定的,文末数值试验的结果验证了理论结果的正确性.     

非线性随机分数阶微分方程Euler方法的弱收敛性

论文首先证明了非线性随机分数阶微分方程解的存在唯一性, 然后构造了数值求解该方程的Euler 方法, 并证明了当方程满足一定约束条件时, 该方法是弱收敛的. 特别地, 当分数阶α=0时, 该方程退化为非线性随机微分方程, 所获结论与现有文献中的相关结论是一致的; 当α ≠ 0, 且初值条件为齐次时, 所获结论可视为现有文献中线性随机分数阶微分方程情形的推广和改进. 随后, 文末的数值试验验证了所获理论结果的正确性.     

带有弱奇性核的多项分数阶非线性随机微分方程的改进Euler-Maruyama格式

针对一类带有弱奇性核的多项分数阶非线性随机微分方程构造了改进Euler-Maruyama (EM)格式,并证明了该格式的强收敛性.具体地,利用随机积分解的充分条件,将此多项分数阶随机微分方程等价地转化为随机Volterra 积分方程的形式,详细推导出对应的改进EM格式,并对该格式进行了强收敛性分析,其强收敛阶为α_m-α_m-1,其中α_i为分数阶导数的指标,且满足0<α₁<…<α_m-1<α_m<1.最后,通过数值实验验证了理论分析结果的正确性.     

一类具有弱奇异核的偏积分微分方程的Chebyshev小波数值方法（英文）

本文提出一种基于第四类Chebyshev小波配置法,求解了一类具有弱奇异核的偏积分微分方程数值解.利用第四类移位Chebyshev多项式,在Riemann-Liouville分数阶积分意义下,导出Chebyshev的分数次积分公式.通过利用分数次积分公式和二维的第四类Chebyshev小波结合配置法,将具有弱奇异核的偏积分微分方程转化为代数方程组求解.给出了第四类Chebyshev小波的收敛性分析.数值例子证明了本文方法的有效性.     

随机分数阶微分方程初值问题基于模拟方程法的数值求解

基于模拟方程法,提出了一种求解随机分数阶微分方程初值问题的数值方法.考虑含两个分数阶导数项的微分方程,引入两个线性的、非耦合的随机模拟方程,利用它们解构原方程,借助Laplace变换及逆变换,得到方程解的积分表达式,同时建立起两个模拟方程之间的联系,结合初始状态,得到求解随机微分方程初值问题的数值迭代算法.作为特例,对于含两个分数阶导数项线性常微分方程的初值问题,给出了基于模拟方程法的数值解法的显式结果.该方法是稳定的,它的误差仅存在于积分近似时的截断误差和计算软件的舍入误差.应用实例说明了数值方法在确定和随机情形的有效性和准确性.     

带有弱奇性核的多项分数阶非线性随机微分方程解的适定性

本文主要研究一类带有多项分数阶Caputo导数的非线性随机微分方程初值问题的解的适定性.具体地,首先把多项分数阶随机微分方程等价地转化为随机Volterra积分方程;然后,给出了该随机积分方程的Euler-Maruyama (EM)格式;最后,借助于该EM格式,证明了多项分数阶随机微分方程的解的适定性.     

变系数的分数阶非齐次线性微分方程

主要采用分数阶的幂级数展开的方法,研究α阶和2α阶非齐次线性微分方程解的形式.改进了原有的齐次变系数的分数阶微分方程关于数值解的结论.     

具有马尔可夫调制的随机微分方程数值解的收敛性和稳定性

研究了一类具有马尔可夫调制的线性随机微分方程Euler数值解的收敛性和稳定性,建立了Euler数值解MS稳定性的定义,确定了Euler数值解MS稳定的条件.     

一个新的分数阶比较定理

研究了Caputo和Riemann-Liouville两型分数阶微分方程的比较定理.首先,讨论了一类线性分数阶微分不等式解得非负性.其次,引入单边Lipschitz条件,将微分方程解的比较问题化为线性微分不等式非负解问题,通过线性分数阶微分方程的求解,得到分数阶比较定理.最后,为进一步说明结论,给出了两个数值仿真例子.     

线性随机变时滞微分方程指数Euler方法的收敛性和稳定性

文应用指数Euler方法研究了线性随机变时滞微分方程的收敛性和稳定性;首先,证明了指数Euler方法是$\frac{1}{2}$阶均方收敛的;其次,在解析解均方稳定的前提下,通过跟Euler-Maruyama方法比较发现指数Euler方法在大步长下依然保持解析解的均方稳定性;最后,用数值试验验证了收敛和稳定的结果.     

Convergence and stability of the semi-implicit Euler method for a linear stochastic differential delay equation

The paper deals with convergence and stability of the semi-implicit Euler method for a linear stochastic differential delay equation. It is proved that the semi-implicit Euler method is convergent with strong order . The conditions under which the method is MS-stable and GMS-stable are determined and the numerical experiments are given.     

倒向随机方程期权定价模型的一类随机算法

期权定价问题可以转化为对倒向随机微分方程的求解,进而转化为对相应抛物型偏微分方程的求解.为了求解与倒向随机微分方程相应的二阶拟线性抛物型微分方程初值问题,引入一类新的随机算法-分层方法取代传统的确定性数值算法.这种数值方法理论上是通过弱显式欧拉法,离散其相应随机系统解的概率表示而得到.该随机算法的收敛性在文中得到证明,其稳定性是自然的.并构造了易于数值实现的基于插值的算法,实证研究说明这种算法能很好地提供期权定价模型的数值模拟.     

The th moment stability for the stochastic pantograph equation

In this paper, we investigate the αth moment asymptotical stability of the analytic solution and the numerical methods for the stochastic pantograph equation by using the Razumikhin technique. Especially the linear stochastic pantograph equations and the semi-implicit Euler method applying them are considered. The convergence result of the semi-implicit Euler method is obtained. The stability conditions of the analytic solution of those equations and the numerical method are given. Finally, some experiments are given.     

The composite Milstein methods for the numerical solution of Ito stochastic differential equations

In this paper, we present the composite Milstein methods for the strong solution of Ito stochastic differential equations. These methods are a combination of semi-implicit and implicit Milstein methods. We give a criterion for choosing either the implicit or the semi-implicit scheme at each step of our numerical solution. The stability and convergence properties are investigated and discussed for the linear test equation. The convergence properties for the nonlinear case are shown numerically to be the same as the linear case. The stability properties of the composite Milstein methods are found to be more superior compared to those of the Milstein, the Euler and even better than the composite Euler method. This superiority in stability makes the methods a better candidate for the solution of stiff SDEs.     

Strong convergence of a stochastic Rosenbrock-type scheme for the finite element discretization of semilinear SPDEs driven by multiplicative and additive noise

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part, usually called stochastic dominated transport equations. Most standard numerical schemes lose their good stability properties on such equations, including the current linear implicit Euler method. We discretize the SPDE in space by the finite element method and propose a novel scheme called stochastic Rosenbrock-type scheme for temporal discretization. Our scheme is based on the local linearization of the semi-discrete problem obtained after space discretization and is more appropriate for such equations. We provide a strong convergence of the new fully discrete scheme toward the exact solution for multiplicative and additive noise and obtain optimal rates of convergence. Numerical experiments to sustain our theoretical results are provided.     

Convergence of the Stochastic Euler Scheme for Locally Lipschitz Coefficients

Stochastic differential equations are often simulated with the Monte Carlo Euler method. Convergence of this method is well understood in the case of globally Lipschitz continuous coefficients of the stochastic differential equation. However, the important case of superlinearly growing coefficients has remained an open question. The main difficulty is that numerically weak convergence fails to hold in many cases of superlinearly growing coefficients. In this paper we overcome this difficulty and establish convergence of the Monte Carlo Euler method for a large class of one-dimensional stochastic differential equations whose drift functions have at most polynomial growth.