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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 794716, 6 pages
Solving Singularly Perturbed Multipantograph Delay Equations Based on the Reproducing Kernel Method
Department of Mathematics, Changshu Institute of Technology, Changshu, Jiangsu 215500, China
Received 13 November 2013; Accepted 20 January 2014; Published 27 February 2014
Academic Editor: Valery Y. Glizer
Copyright © 2014 F. Z. Geng and S. P. Qian. 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.
A numerical method is presented for solving the singularly perturbed multipantograph delay equations with a boundary layer at one end point. The original problem is reduced to boundary layer and regular domain problems. The regular domain problem is solved by combining the asymptotic expansion and the reproducing kernel method (RKM). The boundary layer problem is treated by the method of scaling and the RKM. Two numerical examples are provided to illustrate the effectiveness of the present method. The results from the numerical example show that the present method can provide very accurate analytical approximate solutions.
In this paper, we consider the following singularly perturbed multipantograph delay equation: where , , is a positive integer and and are assumed to be sufficiently smooth, such that (1) has a unique solution with a boundary layer at .
Singularly perturbed problems arise frequently in applications including geophysical fluid dynamics, oceanic and atmospheric circulation, chemical reactions, and optimal control. These problems are characterized by the presence of a small parameter that multiplies the highest order derivative, and they are stiff and there exist boundary layers where the solutions change rapidly.
Functional differential equations with proportional delays are usually referred to as pantograph equations. These equations arise in a variety of applications, such as number theory, electrodynamics, astrophysics, nonlinear dynamical systems, probability theory on algebraic structure, quantum mechanics, and cell growth.
Recently, singularly perturbed delayed differential equations have attracted significant attention. The numerical treatment of such problems presents some major computational difficulties, and therefore discussion on numerical solutions of singularly perturbed delayed differential equation is rare. Amiraliyev et al. [1, 2] proposed a uniform numerical method for dealing with singularly perturbed delay initial value problems. Kadalbajoo et al. [3–6] presented some effective methods for solving singularly perturbed delay boundary value problems. In , Amiraliyev and Cimen also introduced a numerical method for singularly perturbed delay boundary value problems. In , Rai and Sharma described a numerical method based on fitted operator finite difference scheme for the boundary value problems for singularly perturbed delay differential equations with turning point and mixed shifts.
Reproducing kernel theory has important applications in numerical analysis, differential equations, probability, and statistics, amongst other fields [9–20]. Recently, using the reproducing kernel method (RKM), the authors have discussed various differential equations [11–20]. However, it is very difficult to expand the application of the RKM to singularly perturbed delayed differential equations. Geng  developed a method for solving a class of singularly perturbed boundary value problems based on the RKM and a proper transformation. Nevertheless, this method fails to solve singularly perturbed delayed differential equations.
The rest of the paper is organized as follows. In the next section, the numerical technique for (1) is introduced. Error analysis is introduced in Section 2. The numerical example is given in Section 3. Section 4 ends this paper with a brief conclusion.
2. Numerical Method
We divide the domain into two subdomains, namely, and , where is a positive real number. The asymptotic approximation technique and the RKM are combined to solve (1) in the regular domain . And then the value of asymptotic approximation in the regular domain is used as the boundary condition at the so-called transition point . In the boundary layer domain , (1) is solved by combining the method of scaling and the RKM. After solving both the regular and boundary layer domain problems their solutions are combined to obtain an approximate solution to the original problem over the entire domain .
2.1. Solution of the Regular Domain
We seek the regular region solution as an asymptotic expansion of the form where are unknown functions to be determined.
Substituting into (1) and equating the coefficients of like powers of , we obtain
By the RKM, the solutions of the above equations can be approximated by where , , .
Therefore, the solution of regular region can be approximated by
In the following, we will show how to solve (3) using the RKM in detail.
The reproducing kernel space is defined as are absolutely continuous real valued functions, . The inner product and norm in are given, respectively, by Its reproducing kernel is For detailed method of obtaining reproducing kernel , please refer to [9, 15].
In (6), it is clear that is a bounded linear operator (please see  for the definition of ). Put and , where is the reproducing kernel of , is the adjoint operator of . The orthonormal system of can be derived from the Gram-Schmidt orthogonalization process applied to ,
Theorem 1. For (6), if is dense on , then is the complete system of and .
Proof. Note here that
For each fixed , let , which means that Since is dense on , . It follows that from the existence of . So the proof of the Theorem 1 is complete.
Proof. Applying Theorem 1, it is easy to see that is the complete orthonormal basis of .
Note that for each ; hence we have and the proof of the theorem is complete.
The approximate solution can be obtained by taking finitely many terms in the series representation of and
2.2. Solution of the Boundary Layer Domain
Consider For the boundary layer domain, we scale with ; then (15) becomes Using the similar method for solving (3), we can obtain the approximate solution of (16) in the reproducing kernel space is an absolutely continuous real valued function, , in which the inner product and norm are given, respectively, by The approximate solution of (16) can be represented by where . Therefore, the approximation of solution of boundary layer domain problem (15) can be obtained by From (5) and (19), the approximate solution of (1) on the entire region is immediately obtained as follows:
3. Numerical Examples
Example 1. Consider the following singular perturbation problem with pantograph delay where is given such that its exact solution is . Using the present method and taking , , (regular domain), (boundary layer domain), and , , , respectively, the numerical results are given in Figures 1, 2, and 3. All computations are performed by using Mathematica 5.1.
Example 2. For comparison, we consider the following singular perturbation problem without delay [21–25] Its exact solution is . Using the present method and taking , , (regular domain), (boundary layer domain), , the numerical results compared with other methods are given in Tables 1, 2, and 3. It is shown that the present method can yield better results than existing methods.
In this paper, a new method is proposed for solving singularly perturbed multipantograph delay equations. The present method is based on the RKM, the asymptotic expansion technique, and the method of scaling. The major advantage of the method is that it can produce good globally continuous approximate solutions. The results from the numerical example show that the present method is an accurate and reliable analytical technique for treating singularly perturbed multipantograph delay equations.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to express their thanks to the unknown referees for their careful reading and helpful comments. The work was supported by the NSFC (Grant nos. 11201041 and 11026200), the Special Funds of the National Natural Science Foundation of China (Grant no. 11141003), and Qing Lan Project of Jiangsu Province.
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