Research Article  Open Access
W. M. AbdElhameed, E. H. Doha, Y. H. Youssri, "New Wavelets Collocation Method for Solving SecondOrder Multipoint Boundary Value Problems Using Chebyshev Polynomials of Third and Fourth Kinds", Abstract and Applied Analysis, vol. 2013, Article ID 542839, 9 pages, 2013. https://doi.org/10.1155/2013/542839
New Wavelets Collocation Method for Solving SecondOrder Multipoint Boundary Value Problems Using Chebyshev Polynomials of Third and Fourth Kinds
Abstract
This paper is concerned with introducing two wavelets collocation algorithms for solving linear and nonlinear multipoint boundary value problems. The principal idea for obtaining spectral numerical solutions for such equations is employing third and fourthkind Chebyshev wavelets along with the spectral collocation method to transform the differential equation with its boundary conditions to a system of linear or nonlinear algebraic equations in the unknown expansion coefficients which can be efficiently solved. Convergence analysis and some specific numerical examples are discussed to demonstrate the validity and applicability of the proposed algorithms. The obtained numerical results are comparing favorably with the analytical known solutions.
1. Introduction
Spectral methods are one of the principal methods of discretization for the numerical solution of differential equations. The main advantage of these methods lies in their accuracy for a given number of unknowns (see, e.g., [1–4]). For smooth problems in simple geometries, they offer exponential rates of convergence/spectral accuracy. In contrast, finite difference and finiteelement methods yield only algebraic convergence rates. The three most widely used spectral versions are the Galerkin, collocation, and tau methods. Collocation methods [5, 6] have become increasingly popular for solving differential equations, also they are very useful in providing highly accurate solutions to nonlinear differential equations.
Many practical problems arising in numerous branches of science and engineering require solving high evenorder and high oddorder boundary value problems. Legendre polynomials have been previously used for obtaining numerical spectral solutions for handling some of these kinds of problems (see, e.g., [7, 8]). In [9], the author has constructed some algorithms by selecting suitable combinations of Legendre polynomials for solving the differentiated forms of highoddorder boundary value problems with the aid of PetrovGalerkin method, while in the two papers [10, 11], the authors handled third and fifthorder differential equations using Jacobi tau and Jacobi collocation methods.
Multipoint boundary value problems (BVPs) arise in a variety of applied mathematics and physics. For instance, the vibrations of a guy wire of uniform crosssection composed of parts of different densities can be set up as a multipoint BVP, as in [12]; also, many problems in the theory of elastic stability can be handled by the method of multipoint problems [13]. The existence and multiplicity of solutions of multipoint boundary value problems have been studied by many authors; see [14–17] and the references therein. For twopoint BVPs, there are many solution methods such as orthonormalization, invariant imbedding algorithms, finite difference, and collocation methods (see, [18–20]). However, there seems to be little discussion about numerical solutions of multipoint boundary value problems.
Secondorder multipoint boundary value problems (BVP) arise in the mathematical modeling of deflection of cantilever beams under concentrated load [21, 22], deformation of beams and plate deflection theory [23], obstacle problems [24], Troesch's problem relating to the confinement of a plasma column by radiation pressure [25, 26], temperature distribution of the radiation fin of trapezoidal profile [21, 27], and a number of other engineering applications. Many authors have used numerical and approximate methods to solve secondorder BVPs. The details about the related numerical methods can be found in a large number of papers (see, for instance, [21, 23, 24, 28]). The Walsh wavelets and the semiorthogonal Bspline wavelets are used in [23, 29] to construct some numerical algorithms for the solution of secondorder BVPs with Dirichlet and Neumann boundary conditions. Na [21] has found the numerical solution of second, third, and fourthorder BVPs by converting them into initial value problems and then applying a class of methods like nonlinear shooting, method of reduced physical parameters, method of invariant imbedding, and so forth. The presented approach in this paper can be applied to both BVPs and IVPs with a slight modification, but without the transformation of BVPs into IVPs or vice versa.
Wavelets theory is a relatively new and an emerging area in mathematical research. It has been applied to a wide range of engineering disciplines; particularly, wavelets are very successfully used in signal analysis for wave form representation and segmentations, time frequency analysis, and fast algorithms for easy implementation. Wavelets permit the accurate representation of a variety of functions and operators. Moreover, wavelets establish a connection with fast numerical algorithms, (see [30, 31]).
The application of Legendre wavelets for solving differential and integral equations is thoroughly considered by many authors (see, for instance, [32, 33]). Also, Chebyshev wavelets are used for solving some fractional and integral equations (see, [34, 35]).
Chebyshev polynomials have become increasingly crucial in numerical analysis, from both theoretical and practical points of view. It is well known that there are four kinds of Chebyshev polynomials, and all of them are special cases of the more widest class of Jacobi polynomials. The first and second kinds are special cases of the symmetric Jacobi polynomials (i.e., ultraspherical polynomials), while the third and fourth kinds are special cases of the nonsymmetric Jacobi polynomials. In the literature, there is a great concentration on the first and second kinds of Chebyshev polynomials and and their various uses in numerous applications, (see, for instance, [36]). However, there are few articles that concentrate on the other two types of Chebyshev polynomials, namely, third and fourth kinds and , either from theoretical or practical point of view and their uses in various applications (see, e.g., [37]). This motivates our interest in such polynomials. We therefore intend in this work to use them in a marvelous application of multipoint BVPs arising in physics.
There are several advantages of using Chebyshev wavelets approximations based on collocation spectral method. First, unlike most numerical techniques, it is now well established that they are characterized by exponentially decaying errors. Second, approximation by wavelets handles singularities in the problem. The effect of any such singularities will appear in some form in any scheme of the numerical solution, and it is well known that other numerical methods do not perform well near singularities. Finally, due to their rapid convergence, Chebyshev wavelets collocation method does not suffer from the common instability problems associated with other numerical methods.
The main aim of this paper is to develop two new spectral algorithms for solving secondorder multipoint BVPs based on shifted third and fourthkind Chebyshev wavelets. The method reduces the differential equation with its boundary conditions to a system of algebraic equations in the unknown expansion coefficients. Large systems of algebraic equations may lead to greater computational complexity and large storage requirements. However the third and fourthkind Chebyshev wavelets collocation method reduces drastically the computational complexity of solving the resulting algebraic system.
The structure of the paper is as follows. In Section 2, we give some relevant properties of Chebyshev polynomials of third and fourth kinds and their shifted ones. In Section 3, the third and fourthkind Chebyshev wavelets are constructed. Also, in this section, we ascertain the convergence of the Chebyshev wavelets series expansion. Two new shifted Chebyshev wavelets collocation methods for solving secondorder linear and nonlinear multipoint boundary value problems are implemented and presented in Section 4. In Section 5, some numerical examples are presented to show the efficiency and the applicability of the presented algorithms. Some concluding remarks are given in Section 6.
2. Some Properties of and
The Chebyshev polynomials and of third and fourth kinds are polynomials of degree in defined, respectively, by (see [38]) where ; also they can be obtained explicitly as two particular cases of Jacobi polynomials for the two nonsymmetric cases correspond to . Explicitly, we have It is readily seen that Hence, it is sufficient to establish properties and relations for and then deduce their corresponding properties and relations for (by replacing by ).
The polynomials and are orthogonal on ; that is, where and they may be generated by using the two recurrence relations with the initial values with the initial values
The shifted Chebyshev polynomials of third and fourth kinds are defined on , respectively, as All results of Chebyshev polynomials of third and fourth kinds can be easily transformed to give the corresponding results for their shifted ones.
The orthogonality relations of and on are given by where
3. Shifted Third and FourthKind Chebyshev Wavelets
Wavelets constitute of a family of functions constructed from dilation and translation of single function called the mother wavelet. When the dilation parameter and the translation parameter vary continuously, then we have the following family of continuous wavelets: Each of the third and fourthkind Chebyshev wavelets has four arguments: , is the order of the polynomial or , and is the normalized time. They are defined explicitly on the interval as
3.1. Function Approximation
A function defined over may be expanded in terms of Chebyshev wavelets as where and the weights , , are given in (12).
Assume that can be approximated in terms of Chebyshev wavelets as
3.2. Convergence Analysis
In this section, we state and prove a theorem to ascertain that the third and fourthkind Chebyshev wavelets expansion of a function , with bounded second derivative, converges uniformly to .
Theorem 1. Assume that a function , with , can be expanded as an infinite series of thirdkind Chebyshev wavelets; then this series converges uniformly to . Explicitly, the expansion coefficients in (16) satisfy the following inequality:
Proof. From (16), it follows that If we make use of the substitution in (19), then we get which in turn, and after performing integration by parts two times, yields where Now, we have Finally, since , we have
Remark 2. The estimation in (18) is also valid for the coefficients of fourthkind Chebyshev wavelets expansion. The proof is similar to the proof of Theorem 1.
4. Solution of Multipoint BVPs
In this section, we present two Chebyshev wavelets collocation methods, namely, thirdkind Chebyshev wavelets collocation method (3CWCM) and fourthkind Chebyshev wavelets collocation method (4CWCM), to numerically solve the following multipoint boundary value problem (BVP): where , , and are piecewise continuous on ; may equal zero; ; ; , , , , and are constants such that ; is a nonlinear function of , and is a nonlinear function in .
Consider an approximate solution to (25) and (26) which is given in terms of Chebyshev wavelets as then the substitution of (27) into (25) enables one to write the residual of (25) in the form Now, the application of the typical collocation method (see, e.g., [5]) gives where are the first roots of or . Moreover, the use of the boundary conditions (26) gives Equations (29) and (30) generate equations in the unknown expansion coefficients, , which can be solved with the aid of the wellknown Newton's iterative method. Consequently, we get the desired approximate solution given by (27).
5. Numerical Examples
In this section, the presented algorithms in Section 4 are applied to solve both of linear and nonlinear multipoint BVPs. Some examples are considered to illustrate the efficiency and applicability of the two proposed algorithms.
Example 1. Consider the secondorder nonlinear BVP (see [6, 28]): The two proposed methods are applied to the problem for the case corresponding to and . The numerical solutions are shown in Table 1. Due to nonavailability of the exact solution, we compare our results with Haar wavelets method [6], ADM solution [28] and ODEs Solver from Mathematica which is carried out by using RungeKutta method. This comparison is also shown in Table 1.
Example 2. Consider the secondorder linear BVP (see, [39, 40]): The exact solution of problem (32) is given by In Table 2, the maximum absolute error is listed for and various values of , while in Table 3, we give a comparison between the best errors resulted from the application of various methods for Example 2, while in Figure 1, we give a comparison between the exact solution of (32) with three approximate solutions.

Example 3. Consider the secondorder singular nonlinear BVP (see [40, 41]): with the exact solution . In Table 4, the maximum absolute error is listed for and various values of , while in Table 5 we give a comparison between the best errors resulted from the application of various methods for Example 3. This table shows that our two algorithms are more accurate if compared with the two methods developed in [40, 41].

Example 4. Consider the secondorder nonlinear BVP (see [42]): where The exact solution of (35) is given by . In Table 6, the maximum absolute error is listed for and various values of , and in Table 7 we give a comparison between best errors resulted from the application of various methods for Example 4. This table shows that our two algorithms are more accurate if compared with the method developed in [42].


Example 5. Consider the secondorder singular linear BVP: where and is chosen such that the exact solution of (37) is . In Table 8, the maximum absolute error is listed for and various values of , while in Figure 2, we give a comparison between the exact solution of (37) with three approximate solutions.

Example 6. Consider the following nonlinear secondorder BVP: with the exact solution . We solve (39) using 3CWCM for the case corresponding to and , to obtain an approximate solution of . If we make use of (27), then the approximate solution can be expanded in terms of thirdkind Chebyshev wavelets as If we set then (42) reduces to the form If we substitute (44) into (39), then the residual of (39) is given by We enforce the residual to vanish at the first root of , namely, at , to get Furthermore, the use of the boundary conditions (40) and (41) yields The solution of the nonlinear system of (46) and (47) gives and consequently which is the exact solution.
Remark 3. It is worth noting here that the obtained numerical results in the previous solved six examples are very accurate, although the number of retained modes in the spectral expansion is very few, and again the numerical results are comparing favorably with the known analytical solutions.
6. Concluding Remarks
In this paper, two algorithms for obtaining numerical spectral wavelets solutions for secondorder multipoint linear and nonlinear boundary value problems are analyzed and discussed. Chebyshev polynomials of third and fourth kinds are used. One of the advantages of the developed algorithms is their availability for application on singular boundary value problems. Another advantage is that high accurate approximate solutions are achieved using a few number of terms of the approximate expansion. The obtained numerical results are comparing favorably with the analytical ones.
Acknowledgment
The authors would like to thank the referee for his valuable comments and suggestions which improved the paper in its present form.
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