一类修正的BDF方法

1.引言 在常微分方程初值问题的数值方法中,线性多步法是最简单、使用最广泛的方法之一.但在刚性(Stiff)微分方程中,由于数值稳定性问题,线性多步法的应用受到很大限制.G.Dahlquist指出,A-稳定线性多步法的最高可达阶是2,而梯形公式是2阶A-稳定线性多步法中误差常数最小的方法.因此,人们不再致力于探索高阶A-稳定线性多     

线性Volterra多延迟积分微分方程线性多步法的GPm稳定性

讨论了多步法求解线性Volterra多延迟积分微分方程数值方法的GPm稳定.证明了对任给的步长h>0,A-稳定的线性多步法保持原线性系统的渐近稳定性,从而是GPm稳定.     

多时滞微分方程数值稳定性

考虑了时滞微分方程的初值问题,分析了用线性多步法求解一类滞后型微分系统数值解的稳定性,在一定的Lagrange插值条件下,给出并证明了求解滞后型微分系统的线性多步法数值稳定的充分必要条件.     

非线性延迟积分微分方程线性多步法的渐近稳定性

本文研究求解非线性延迟积分微分方程的线性多步法的渐近稳定性,其中积分部分采用复化梯形公式计算,结果表明:在问题真解渐近稳定的条件下,A-稳定的线性多步法也是渐近稳定的.     

广义时滞微分方程的渐近稳定性和数值分析

考虑了广义时滞微分方程的初值问题，分析了用线性多步法求解一类广义滞后型微分系统数值解的稳定性，在一定的Lagrange插值条件下，给出并证明了求解广义滞后型微分系统的线性多步法数值稳定的充分必要条件。     

中立型微分方程组连续线性多步法

我们曾对中立型微分方程组给出了任意阶的单步解法.但是,就计算量来说,多步法比较优越.本文将给出连续线性多步法,它类似于常微分方程组Adams线性多步法.当方程是常微且取离散解时,它就是Adams法;当方程不含导数时,它是某类Volterra泛函微分方程组的线性多步法(Volterra微分积分方程与迟延方程为其特例).本文的算法在     

预估-校正方法的绝对稳定性讨论

预估-校正方法,即PECE方法,常被用于求解常微分方程的初值问题.而一般文献中常只讨论了单个线性多步法公式的稳定性问题,很少涉及由一个显式公式和一个隐式公式组合而成的PECE方法的稳定性.本文应用根轨迹法和对分法讨论了常用的PECE方法的稳定性,求出了一些常用PECE方法的组合公式的绝对稳定区间和绝对稳定区域,并用数值...     

一类A(α)-稳定降阶的Gear方法

<正>1引言线性多步法是求解常微分方程(组)的初值问题:{y'=f(x,y),a≤x≤b y(a)=y0的重要经典算法之一~([1-29]),其中具有A(α)-稳定性,可用于解刚性常微分方程的线性多步法的文献有:Gear于1968年在[9]中给出了一类Gear(BDF)方法;杨大地等于2008年在[17]中给出了一类YL方法,它的绝对稳定域比同阶Gear方法略大一些;Cash于1980年在[15]中给出了一种广义的向后差分方法;孙耿等于1981年在[20]中给出了一类变更的     

解Stiff常微分方程组初值问题的线性隐式方法

对于Stiff常微分方程组初值问题的数值解,人们为了保证数值解过程误差传播的有界性,经常使用的方法之一是隐式的线性多步法.而在解由隐式线性多步法所产生的非线性方程组时,总是采用Newton-Raphson迭代方法.为此就要给出适当的预估式和计算     

中立型随机比例微分方程的数值解的指数稳定性（英文）

本文主要利用半鞅收敛定理,研究中立型随机比例微分方程的数值稳定性.该文建立了线性的和非线性的中立型随机比例微分方程新的细则,我们将证明,在线性增长条件下,欧拉方法可以保留中立型随机比例微分方程的几乎处处指数稳定性,并且反向的欧拉方法能保留非线性的中立型随机比例微分方程的几乎处处指数稳定性.     

随机延迟微分方程平衡方法的均方收敛性与稳定性

本文讨论求解刚性随机延迟微分方程的平衡方法.证明了随机延迟微分方程平衡方法的均方收敛阶为1/2.给出了线性随机延迟微分方程平衡方法均方稳定的条件.     

Exponential stability of impulsive stochastic functional differential equations

In this paper, we investigate the pth moment and almost sure exponential stability of impulsive stochastic functional differential equations with finite delay by using Lyapunov method. Several stability theorems of impulsive stochastic functional differential equations with finite delay are derived. These new results are employed to impulsive stochastic equations with bounded time-varying delays and stochastically perturbed equations. Meanwhile, an example and simulations are given to show that impulses play an important role in pth moment and almost sure exponential stability of stochastic functional differential equations with finite delay.     

Stable strong order 1.0 schemes for solving stochastic ordinary differential equations

The Balanced method was introduced as a class of quasi-implicit methods, based upon the Euler-Maruyama scheme, for solving stiff stochastic differential equations. We extend the Balanced method to introduce a class of stable strong order 1.0 numerical schemes for solving stochastic ordinary differential equations. We derive convergence results for this class of numerical schemes. We illustrate the asymptotic stability of this class of schemes is illustrated and is compared with contemporary schemes of strong order 1.0. We present some evidence on parametric selection with respect to minimising the error convergence terms. Furthermore we provide a convergence result for general Balanced style schemes of higher orders.     

Numeric solution of advection–diffusion equations by a discrete time random walk scheme

Explicit numerical finite difference schemes for partial differential equations are well known to be easy to implement but they are particularly problematic for solving equations whose solutions admit shocks, blowups, and discontinuities. Here we present an explicit numerical scheme for solving nonlinear advection–diffusion equations admitting shock solutions that is both easy to implement and stable. The numerical scheme is obtained by considering the continuum limit of a discrete time and space stochastic process for nonlinear advection–diffusion. The stochastic process is well posed and this guarantees the stability of the scheme. Several examples are provided to highlight the importance of the formulation of the stochastic process in obtaining a stable and accurate numerical scheme.     

Stability in distribution of neutral stochastic functional differential equations with Markovian switching

Stability in distribution of stochastic differential equations with Markovian switching and stochastic differential delay equations with Markovian switching have been studied by several authors and this kind of stability is an important property for stochastic systems. There are several papers which study this stability for stochastic differential equations with Markovian switching and stochastic differential delay equations with Markovian switching technically. In our paper, we are concerned with the general neutral stochastic functional differential equations with Markovian switching and we derive the sufficient conditions for stability in distribution. At the end of our paper, one example is established to illustrate the theory of our work.     

Approximation of stochastic advection diffusion equations with Stochastic Alternating Direction Explicit methods

The numerical solutions of stochastic partial differential equations of Itô type with time white noise process, using stable stochastic explicit finite difference methods are considered in the paper. Basically, Stochastic Alternating Direction Explicit (SADE) finite difference schemes for solving stochastic time dependent advection-diffusion and diffusion equations are represented and the main properties of these stochastic numerical methods, e.g. stability, consistency and convergence are analyzed. In particular, it is proved that when stable alternating direction explicit schemes for solving linear parabolic PDEs are developed to the stochastic case, they retain their unconditional stability properties applying to stochastic advection-diffusion and diffusion SPDEs. Numerically, unconditional stable SADE techniques are significant for approximating the solutions of the proposed SPDEs because they do not impose any restrictions for refining the computational domains. The performance of the proposed methods is tested for stochastic diffusion and advection-diffusion problems, and the accuracy and efficiency of the numerical methods are demonstrated.     

Numerical solutions of backward stochastic differential equations: A finite transposition method

In this Note, we present a new numerical method for solving backward stochastic differential equations. Our method can be viewed as an analogue of the classical finite element method solving deterministic partial differential equations.     

滞后型分段连续随机微分方程的稳定性

本文研究了滞后型分段连续随机微分方程的解析稳定性和数值稳定性问题.首先,利用伊藤公式等方法获得了解析解均方稳定的条件,其次,对于包括均方稳定和T-稳定在内的Euler-Maruyama方法的数值稳定性问题,运用不等式技术和随机分析方法获得了一些新的结果,证明了在一定条件下,Euler-Maruyama方法既是均方稳定又是T-稳定的,推广了随机延迟微分方程的数值稳定性结论.     

Pathwise Stability of Degenerate Stochastic Evolutions

For linear stochastic evolution equations with linear multiplicative noise, a new method is presented for estimating the pathwise Lyapunov exponent. The method consists of finding a suitable (quadratic) Lyapunov function by means of solving an operator inequality. One of the appealing features of this approach is the possibility to show stabilizing effects of degenerate noise. The results are illustrated by applying them to the examples of a stochastic partial differential equation and a stochastic differential equation with delay. In the case of a stochastic delay differential equation our results improve upon earlier results.     

Implicit Milstein method for stochastic differential equations via the Wong-Zakai approximation

The aim of this paper is to derive a numerical scheme for solving stochastic differential equations (SDEs) via Wong-Zakai approximation. One of the most important methods for solving SDEs is Milstein method, but this method is not so popular because the cost of simulating the double stochastic integrals is high. For overcoming this complexity, we present an implicit Milstein scheme based on Wong-Zakai approximation by approximating the Brownian motion with its truncated Haar expansion. The main advantages of this method lie in the fact that it preserves the convergence order and also stability region of the Milstein method while its simulation is much easier than Milstein scheme. We show the convergence rate of the method by some numerical examples.