Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2011, Article ID 217672, 11 pages
http://dx.doi.org/10.1155/2011/217672
Research Article

Almost Surely Asymptotic Stability of Numerical Solutions for Neutral Stochastic Delay Differential Equations

1Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai 264209, China
2Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China

Received 7 March 2011; Accepted 11 April 2011

Academic Editor: Her-Terng Yau

Copyright © 2011 Zhanhua Yu and Mingzhu Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We investigate the almost surely asymptotic stability of Euler-type methods for neutral stochastic delay differential equations (NSDDEs) using the discrete semimartingale convergence theorem. It is shown that the Euler method and the backward Euler method can reproduce the almost surely asymptotic stability of exact solutions to NSDDEs under additional conditions. Numerical examples are demonstrated to illustrate the effectiveness of our theoretical results.

1. Introduction

The neutral stochastic delay differential equation (NSDDE) has attracted much more attention, and much work (see [14]) has been done. For example, Mao [2] studied the existence and uniqueness, moment and pathwise estimates, and the exponential stability of the solution to the NSDDE. Moreover, Mao et al. [4] studied the almost surely asymptotic stability of the NEDDE with Markovian switching:

Since most NSDDEs cannot be solved explicitly, numerical solutions have become an important issue in the study of NSDDEs. Convergence analysis of numerical methods for NSDDEs can be found in [57]. On the other hand, stability theory of numerical solutions is one of the fundamental research topics in the numerical analysis. For stochastic differential equations (SDEs) as well as stochastic delay differential equations (SDDEs), moment stability and asymptotic stability of numerical solutions have received much more attention (e.g., [813] for moment stability and [1214] for asymptotic stability). Recently, Wang and Chen [15] studied the mean-square stability of the semi-implicit Euler method for NSDDEs. We aim in this paper to study the almost surely asymptotic stability of Euler-type methods for NSDDEs using the discrete semimartingale convergence theorem. The discrete semimartingale convergence theorem (cf. [16, 17]) plays an important role in the almost surely asymptotic stability analysis of numerical solutions to SDEs and SDDEs [1719]. Using the discrete semimartingale convergence theorem, we show that Euler-type methods for NSDDEs can preserve the almost surely asymptotic stability of exact solutions under additional conditions.

In Section 2, we introduce some necessary notations and state the discrete semimartingale convergence theorem as a lemma. In Section 3, we study the almost surely asymptotic stability of exact solutions to NSDDEs. Section 4 gives the almost surely asymptotic stability of the Euler method. In Section 5, we discuss the almost surely asymptotic stability of the backward Euler method. Numerical experiments are presented in Section 6.

2. Preliminaries

Throughout this paper, unless otherwise specified, we use the following notations. Let be a complete probability space with filtration satisfying the usual conditions (i.e., it is right continuous and contains all -null sets). is a scalar Brownian motion defined on the probability space. denotes the Euclidean norm in . The inner product of in is denoted by or . If is a vector or matrix, its transpose is denoted by . If is a matrix, its trace norm is denoted by . Let and denote the family of all continuous -valued functions on . Let be the family of all -measurable bounded -valued random variables .

Consider an -dimensional NSDDE on with initial data . Here , , and .

Let denote the family of all nonnegative functions on which are continuously once differentiable in and twice differentiable in . For each , define an operator from to by where

As a standing hypothesis, we impose the following assumption on the coefficients , and .

Assumption 2.1. Assume that both and satisfy the local Lipschitz condition. That is, for each integer , there exists a positive constant such that for with and . Assume also that there is a constant such that Assume moreover that for all ,

The following discrete semimartingale convergence theorem (cf. [16, 17]) will play an important role in this paper.

Lemma 2.2. Let and be two sequences of nonnegative random variables such that both and are -measurable for , and a.s. Let be a real-valued local martingale with a.s. Let be a nonnegative -measurable random variable. Assume that is a nonnegative semimartingale with the Doob-Mayer decomposition If a.s., then for almost all that is, both and converge to finite random variables.

3. Almost Surely Asymptotic Stability of the Exact Solution

In this section, we will study the almost surely asymptotic stability of exact solutions to (2.1). To be precise, let us give the definition on the almost surely asymptotic stability of exact solutions.

Definition 3.1. The solution to (2.1) is said to be almost surely asymptotically stable if for any initial data .

Theorem 3.2. Let Assumption 2.1 hold. Assume that there are four positive constants such that for and . If then, for any initial data , there exists a unique global solution to (2.1) and the solution is almost surely asymptotically stable.

Proof. Let . Using (3.2) and (3.3), we have Then, from Theorem 3.1 in [4], we conclude that there exists a unique global solution to (2.1) for any initial data . According to (3.3), there is a constant such that Let . Here . Then where . By Theorem 4.1 in [4], we can obtain that the solution is almost surely asymptotically stable. The proof is completed.

Theorem 3.2 gives sufficient conditions of the almost surely asymptotic stability of the NSDDE (2.1). Based on these sufficient conditions, we will investigate the almost surely asymptotic stability of Euler-type methods in the following sections.

4. Stability of the Euler Method

Applying the Euler method (EM) to (2.1) yields Here ( is an positive integer) is the stepsize, and represents the Browian motion increment. To be precise, let us introduce the definition on the almost surely asymptotic stability of numerical solutions.

Definition 4.1. The numerical solution to (2.1) is said to be almost surely asymptotically stable if for any bounded variables , .

Theorem 4.2. Let conditions (3.2)-(3.3) hold. Assume that satisfies the linear growth condition, namely, there exists a positive constant such that Then there exists a such that if , then for any given finite-valued -measurable random variables , , the EM approximate solution (4.1) is almost surely asymptotically stable.

Proof. Let . Then, it follows from (4.1) that Squaring both sides of (4.4), we have Using (3.2) and (4.3), we can obtain that where It therefore follows that which implies that Note that Then, we have where . By [18], is a martingale with . From (3.3), we obtain that where . Hence, from Lemma 2.2, we therefore have Then, we conclude that The proof is completed.

Theorem 4.2 shows that if the coefficient obeys the linear growth condition, in addition to the conditions imposed in Theorem 3.2, then the EM approximate solution (4.1) reproduces the almost surely asymptotic stability of exact solutions to (2.1) for sufficiently small stepsize.

5. Stability of the Backward Euler Method

Applying the backward Euler method (BEM) to (2.1) yields As a standing hypothesis, we assume that the BEM (5.1) is well defined. The following theorem shows that if the above assumption and the conditions imposed in Theorem 3.2 hold, then the BEM approximate solution (5.1) inherits the almost surely asymptotic stability of exact solutions to (2.1) without any stepsize restriction.

Theorem 5.1. Let conditions (3.2)-(3.3) hold. Then for any given finite-valued -measurable random variables , , the BEM approximate solution (5.1) is almost surely asymptotically stable.

Proof. Let . Then, it follows from (5.1) that Squaring both sides of (5.2), we have Using (3.2), we can obtain that where It therefore follows that which implies that Note that Then, we have Namely, where . By [18], is a martingale with . From (3.3), we obtain that Using Lemma 2.2 yields Then, we conclude that The proof is completed.

6. Numerical Experiments

In this section, we present numerical experiments to illustrate the theoretical results presented in the previous sections.

Consider the following scalar linear problem For test (6.1), we have that , , , , and . By Theorem 3.2, the exact solution to (6.1) is almost surely asymptotically stable.

Theorem 4.2 shows that the EM approximate solution to (6.1) can preserve the almost surely asymptotic stability of exact solutions for . In Figure 1, we compute three different paths using EM (4.1) to approximate (6.1) with the stepsize . Figure 1 shows that are asymptotically stable. Theorem 5.1 shows that the BEM approximate solution to (6.1) reproduces the almost surely asymptotic stability of exact solutions for any . In Figure 2, three different paths are computed by using the BEM (5.1) to approximate (6.1) with the stepsize . Figure 2 demonstrates that these paths are asymptotically stable.

217672.fig.001
Figure 1: Almost surely asymptotic stability of the EM approximate solution with the stepsize .
217672.fig.002
Figure 2: Almost surely asymptotic stability of the BEM approximate solution with the stepsize .

7. Conclusions

This paper deals with the almost surely asymptotic stability of Euler-type methods for NSDDEs by using the discrete semimartingale convergence theorem. We show that the EM reproduces the almost surely asymptotic stability of exact solutions to NSDDEs under an additional linear growth condition. If we assume the BEM is well defined, the BEM can also preserve the almost surely asymptotic stability without the additional linear growth condition.

Acknowledgments

The authors would like to thank the referees for their helpful comments and suggestions. This work is supported by the NSF of China (no.11071050).

References

  1. V. B. Kolamnovskii and V. R. Nosov, Stability and Periodic Modes of Control Systems with Aftereffect, Nauka, Moscow, Russia, 1981.
  2. X. Mao, Stochastic Differential Equations and Their Applications, Horwood Publishing Series in Mathematics & Applications, Horwood Publishing, 1997. View at Zentralblatt MATH
  3. D. Z. Liu, G. Y. Yang, and W. Zhang, “The stability of neutral stochastic delay differential equations with Possion jumps by fixed poins,” Journal of Computational and Applied Mathematics, vol. 235, pp. 3115–3120, 2011. View at Publisher · View at Google Scholar
  4. X. Mao, Y. Shen, and C. Yuan, “Almost surely asymptotic stability of neutral stochastic differential delay equations with Markovian switching,” Stochastic Processes and Their Applications, vol. 118, no. 8, pp. 1385–1406, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  5. B. J. Yin and Z. H. Ma, “Convergence of the semi-implicit Euler method for neutral stochastic delay differential equations with phase semi-Markovian switching,” Applied Mathematical Modelling, vol. 35, pp. 2094–2109, 2011. View at Google Scholar
  6. S. B. Zhou and F. K. Wu, “Convergence of numerical solutions to neutral stochastic delay differential equations with Markovian switching,” Journal of Computational and Applied Mathematics, vol. 229, no. 1, pp. 85–96, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  7. S. Gan, H. Schurz, and H. Zhang, “Mean square convergence of stochastic θ-methods for nonlinear neutral stochastic differential delay equations,” International Journal of Numerical Analysis and Modeling, vol. 8, no. 2, pp. 201–213, 2011. View at Google Scholar
  8. C. T. H. Baker and E. Buckwar, “Exponential stability in p-th mean of solutions, and of convergent Euler-type solutions, of stochastic delay differential equations,” Journal of Computational and Applied Mathematics, vol. 184, no. 2, pp. 404–427, 2005. View at Publisher · View at Google Scholar
  9. M. Liu, W. Cao, and Z. Fan, “Convergence and stability of the semi-implicit Euler method for a linear stochastic differential delay equation,” Journal of Computational and Applied Mathematics, vol. 170, no. 2, pp. 255–268, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  10. D. J. Higham, “Mean-square and asymptotic stability of the stochastic theta method,” SIAM Journal on Numerical Analysis, vol. 38, no. 3, pp. 753–769, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  11. Y. Saito and T. Mitsui, “Stability analysis of numerical schemes for stochastic differential equations,” SIAM Journal on Numerical Analysis, vol. 33, no. 6, pp. 2254–2267, 1996. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  12. S. Pang, F. Deng, and X. Mao, “Almost sure and moment exponential stability of Euler-Maruyama discretizations for hybrid stochastic differential equations,” Journal of Computational and Applied Mathematics, vol. 213, no. 1, pp. 127–141, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  13. D. J. Higham, X. Mao, and C. Yuan, “Almost sure and moment exponential stability in the numerical simulation of stochastic differential equations,” SIAM Journal on Numerical Analysis, vol. 45, no. 2, pp. 592–609, 2007. View at Google Scholar · View at Zentralblatt MATH
  14. Y. Saito and T. Mitsui, “T-stability of numerical scheme for stochastic differential equations,” in Contributions in Numerical Mathematics, vol. 2, pp. 333–344, World Scientific Series in Applicable Analysis, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  15. W. Q. Wang and Y. P. Chen, “Mean-square stability of semi-implicit Euler method for nonlinear neutral stochastic delay differential equations,” Applied Numerical Mathematics, vol. 61, pp. 696–701, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  16. A. N. Shiryayev, Probablity, Springer, Berlin, Germany, 1996.
  17. A. Rodkina and H. Schurz, “Almost sure asymptotic stability of drift-implicit θ-methods for bilinear ordinary stochastic differential equations in 1,” Journal of Computational and Applied Mathematics, vol. 180, no. 1, pp. 13–31, 2005. View at Publisher · View at Google Scholar
  18. F. Wu, X. Mao, and L. Szpruch, “Almost sure exponential stability of numerical solutions for stochastic delay differential equations,” Numerische Mathematik, vol. 115, no. 4, pp. 681–697, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  19. Q. Li and S. Gan, “Almost sure exponential stability of numerical solutions for stochastic delay differential equations with jumps,” Journal of Applied Mathematics and Computing. In press. View at Publisher · View at Google Scholar