Research Article | Open Access
New Iterative Method Based on Laplace Decomposition Algorithm
We introduce a new form of Laplace decomposition algorithm (LDA). By this form a new iterative method was achieved in which there is no need to calculate Adomian polynomials, which require so much computational time for higher-order approximations. We have implemented this method for the solutions of different types of nonlinear pantograph equations to support the proposed analysis.
Since 2001, Laplace decomposition algorithm (LDA) has been one of the reliable mathematical methods for obtaining exact or numerical approximation solutions for a wide range of nonlinear problems.
The Laplace decomposition algorithm was developed by Khuri in  to solve a class of nonlinear differential equations. The basic idea in Laplace decomposition algorithm, which is a combined form of the Laplace transform method with the Adomian decomposition method, was developed to solve nonlinear problems. The disadvantage of the Laplace decomposition algorithm is that the solution procedure for calculation of Adomian polynomials is complex and difficult and takes a lot of computational time for higher-order approximations as pointed out by many researchers [3–5].
The Laplace decomposition algorithm plays an important role in modern scientific research for solving various kinds of nonlinear models; for example, Laplace decomposition algorithm was used in  to solve a model for HIV infection of cells; LDA was employed in  to solve Abel's second kind singular integral equations. In  it was used to solve boundary Layer equation.
The main purpose of this paper is to introduce a new iterative method based on Laplace decomposition algorithm procedure without the need to compute Adomian polynomials and thus reduce the size of calculations needed.
The scheme is tested for some classes of pantograph equations, and the results demonstrate reliability and efficiency of the proposed method.
2. Basic Idea of LDA and the New Technique
To illustrate the basic concept of Laplace decomposition algorithm, we consider the following general nonlinear model: where is the highest order derivative, and are linear and nonlinear operators, respectively, and is an inhomogeneous term.
Applying the Laplace transform (denoted throughout this paper by ) to both sides of (1) and using given conditions, we obtain where
The Laplace decomposition algorithm defines the unknown function by an infinite series as where the components will be determined recurrently. Substituting this infinite series into (2) and using the linearity of Laplace transform lead to
Also the nonlinear functions are defined by infinite series as follows: where are the Adomian polynomials , depending only on , and defined by
The Laplace decomposition algorithm presents the recurrence relation as
Applying the inverse Laplace transform to (8) leads to
Equation (10) can be written as
By taking and (11), the following procedure can be constructed:
Consequently, the exact solution may be obtained by
For the analytic nonlinear operator , we can write
Equation (15) is a new iteration method based on LDA. The advantage of this scheme is that there is no need to calculate Adomian polynomials.
3. Test Problems
In this section we will apply our scheme to different types of pantograph equations.
Example 1 (see ). Consider the following nonlinear pantograph differential equation:
The exact solution of this problem .
Based on the iteration formula (15), we get
Thus, we get
Knowing that the exact solution of this exampleisgiven in ,
We see that the approximation solutions obtained by the present method have good agreement with the exact solution of this problem.
In Table 1 the absolute errors of the present method and standard LDA for are compared.
Figure 1 compares the numerical errors for , and obtained by (a) the present method and (b) the standard LDA. This plot indicates that the series solution obtained by the present method converges faster than the standard LDA.
Example 2. Consider the following nonlinear pantograph integrodifferential equation (PIDE):
For this example we write iteration formula (15) as
and the first terms are which gives the exact solution by .
In Table 2 we compare the absolute errors of the present method for and the standard LDA for and the differential transform method described in  with nine terms.
Figure 2 displays the numerical errors obtained by the present method and the standard LDA.
Example 3. Consider the following nonlinear PIDE:
which has the exact solution . The iteration form of (15) for this example is
We obtain the following successive approximations:
Note that the exact solution of this example is
In Table 3 we compare the absolute errors of the present method for and the standard LDA for and the differential transform method described in  with four terms.
Figure 3 displays the numerical errors obtained by the present method and the standard LDA.
Example 4. Consider a system of multipantograph equations:
We can adapt (15) to solve this system as follows:
Table 4 gives the absolute errors of the present method. The table clearly indicates that when we increase the truncation limit , we have less error.
Table 5 summarizes the CPU times needed to obtain the first three components of the series solutions pertaining to the four above-mentioned examples by the present method and the standard LDA. The CPU time analysis was conducted on a personal computer with a 3.77 GHz processor and 4 GB of RAM using MATLAB 7.10.
In this work, we have presented a new iteration method based on the Laplace decomposition algorithm.
The advantage of the new method is that it does not require Adomian polynomials and thus reduces the calculation size.
The new iterative method has been employed to solve different classes of nonlinear pantograph equations, in which the results obtained are in close agreement with the exact solutions.
The convergence of this method is the subject of ongoing research.
The authors wish to thank the referees for valuable comments. The research was supported by the NSF of China no. 11071050.
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Copyright © 2013 Sabir Widatalla and M. Z. 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.