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Stepsize Restrictions for Nonlinear Stability Properties of Neutral Delay Differential Equations
The present paper is concerned with the relationship between stepsize restriction and nonlinear stability of Runge-Kutta methods for delay differential equations. We obtain a special stepsize condition guaranteeing global and asymptotical stability properties of numerical methods. Some confirmations of the conditions on Runge-Kutta methods are illustrated at last.
Neutral delay differential equations (NDDEs) are widely used in various kinds of applied disciplines such as biology, ecology, electrodynamics, and physics and hence intrigue lots of researchers in numerical simultation and analysis (see, e.g., [1–3]). Up to now, many researchers have discussed nonlinear stability properties for NDDEs. In 2000, Bellen et al.  studied -stable continuous Runge-Kutta methods for NDDEs. They extended the contractivity requirements to the numerical stability analysis. Vermiglio and Torelli further pointed out that the numerical solution produced by the methods can preserve the contractivity property of the theoretical solution in . In 2002, Zhang  derived nonlinear stability properties for theoretical and numerical solutions of NDDEs based on natural Runge-Kutta schemes. After that, Wang et al. [7, 8] first introduced the concepts of GS()- and GAS()-stability for nonautonomous nonlinear problems. They proved that ()-algebraically stable Runge-Kutta methods and -algebraically stable general linear methods lead to GS()- and GAS()-stability for NDDEs, respectively. Recently, Bhrawy et al. [9–11] studied several kinds of collocation method for some NDDEs. For more analogues results, we refer readers to [12–15]. Useful as these stability results are, however, no conclusions have been found to develop the relationship between nonlinear stability analysis and stepsize restriction with some numerical schemes for NDDEs, especially for some Runge-Kutta methods.
The present paper was in part inspired by the work of Spijker et al. With stepsize restriction to some numerical schemes, they revealed to us some monotonicity and stability properties for ODEs, respectively (see, [16–19]). We extend their study to nonlinear NDDEs in the present paper. With stepsize restriction to Runge-Kutta schemes, global and asymptotical stability results for NDDEs are obtained, respectively.
The rest of the paper is organized as follows. In Section 2, we consider Runge-Kutta schemes with linear interpolation procedure for NDDEs. Some concepts, such as global and asymptotical stability, are also collected. Section 3 is devoted to stability analysis. The given results set up a relationship between the stepsize restriction and nonlinear stability for nonlinear NDDEs. Some examples of Runge-Kutta schemes are presented in Section 4. Finally, we end up with some conclusions and extension in the last section.
2. Runge-Kutta Methods for NDDEs
In the present paper, we consider the following nonlinear NDDEs: and the perturbed problem Here, denotes a positive delay term, is a constant matrix with , and are continuous, and : , such that (1) and (2) own a unique solution, respectively, where is a real or complex Hilbert space. As in [20, 21], we assume there exist some inner product and the induced norm such that where , and are real constants.
When , the problem (1) degenerates into nonlinear DDEs of the following type: Nonlinear stability analysis for such systems can be found in [6, 22–25]. Condition (3) can be equivalent to the assumptions in these literatures (see , Remark ).
Now, let us consider s-stage Runge-Kutta methods for (1); the coefficients of the schemes may be organized in Buther tableau as follows: where , , and .
According to Liu in , Runge-Kutta methods for NDDEs can be written as where is stepsize and and are approximations to the analytic solutions , , , and , respectively. We set with , and the arguments and are determined by where for and for .
3. Stability Analysis
In the section, we will discuss the relationship between the stepsize restriction and nonlinear stability of the method.
Theorem 3. Assume condition (3) holds, , and there exists a positive real number , such that the matrix is nonnegative definite, where , . Then the Runge-Kutta method with linear interpolation procedure for NDDEs (1) is globally stable under the stepsize restriction
Proof. Let and be two sequences of approximations to problems (1) and (2), respectively, and write
With the notation, Runge-Kutta methods with the same stepsize for (1) and (2) yield
Thus, we have
Now, in view of the nonnegative definite matrix , we obtain
On the other hand, in terms of condition (3), we find
Then, together with (14), (15), and (16) and using the conditions , we get Noting that and , we have
This implies that where .
Note that ; we have An induction to (21) yields Therefore, the conclusion is proven.
Remark 5. A Runge-Kutta method is algebraically stable if the matrix is nonnegative definite and . For example, the s-stage Gauss, Radau , Radau , and Lobatto methods are algebraically stable. Corollary 4 can be derived for . This implies that the stepsize restriction for DDEs disappears.
Corollary 6. Assume condition (3) holds, , and there exists a positive real number , such that the matrix is nonnegative definite, where , . Then the Runge-Kutta method with linear interpolation procedure for DDEs (4) is globally stable under the stepsize restriction
Theorem 7. Assume condition (3) holds, , the function is uniformly Lipschitz continuous with constant in variables and , and there exists a positive real number , such that the matrix is nonnegative definite, where , . Then the Runge-Kutta method with linear interpolation procedure for NDDEs (1) is asymptotically stable under the stepsize restriction
Proof. Like in the proof of Theorem 3, let , and we can easily find Note and ; we have On the other hand, Now, in view of (13), (29), and (30), we obtain Since and , an induction to (32) gives which completes the proof.
Corollary 8. Assume condition (3) holds, , the function is uniformly Lipschitz continuous with constant in variables and . Then an algebraically stable Runge-Kutta method with linear interpolation procedure for DDEs (4) or NDDEs (1) is asymptotically stable.
Corollary 9. Assume condition (3) holds, , the function is uniformly Lipschitz continuous with constant in variables and , and there exists a positive real number , such that the matrix is nonnegative definite, where , . Then the Runge-Kutta method with linear interpolation procedure for DDEs (4) is asymptotically stable under the stepsize restriction
4. Some Examples
As it is shown in the theorems, the parameters and in the matrix play a key role in the stability analysis. The larger the existed parameter is, the larger stepsize we could choose. In this section, we will show some examples.
In particular, let , where , are constants independent of , respectively. We have
Next, we give some examples on how to calculate the parameter .
Example 1. Consider s-stage 1-order Runge-Kutta methods (see , section ) and we have Thus, the matrix is nonnegative definite for . They imply that these methods for DDE with interpolation are stable with stepsize restriction .
Example 2. Consider 2-stage 2-order Runge-Kutta method: and we obtain Therefore, the matrix is nonnegative definite for . They imply that the stepsize is feasible under the assumptions (3) for NDDEs (1).
For more Runge-Kutta methods with the nonnegative definite matrix , we refer readers to Section in . Higueras revealed to us how to find the largest such that the matrix is nonnegative definite. He pointed that if the matrix is positive definite, the largest can be determined by where denotes the smallest eigenvalue of the matrix .
5. Conclusions and Discussions
In this study, we show that the Runge-Kutta methods with stepsize restrictions can preserve global and asymptotical stability of the continuous DDEs or NDDEs. An algebraically stable Runge-Kutta method with linear interpolation procedure for DDEs or NDDEs is globally stable and asymptotically stable. These results can be easily extended to the following equation with several delays: under the following assumption: where , , and . We do not list them here for the sake of brevity.
Moreover, the present results have certain instructive effect in numerical simulation. In the future, we hope to apply the results to some real-world problems, for example, reaction-diffusion dynamical systems with time delay  and non-Fickian delay reaction-diffusion equations [25, 29].
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is supported by NSFC (Grant nos. 11201161, 11171125, and 91130003).
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