Abstract
A powerful technique based on the sincGalerkin method is presented for obtaining numerical solutions of secondorder nonlinear Dirichlettype boundary value problems (BVPs). The method is based on approximating functions and their derivatives by using the Whittaker cardinal function. Without any numerical integration, the differential equation is reduced to a system of algebraic equations via new accurate explicit approximations of the inner products; therefore, the evaluation is based on solving a matrix system. The solution is obtained by constructing the nonlinear (or linear) matrix system using Maple and the accuracy is compared with the Newton method. The main aspect of the technique presented here is that the obtained solution is valid for various boundary conditions in both linear and nonlinear equations and it is not affected by any singularities that can occur in variable coefficients or a nonlinear part of the equation. This is a powerful side of the method when being compared to other models.
Keywords:
Maple; sincGalerkin approximation; sinc basis function; nonlinear matrix system; Newton method1 Introduction
We present here the sincGalerkin approximation technique using Maple to solve systems of nonlinear BVPs such as
where NL is the nonlinear part of Eq. (1.1) which can take any form of nonlinearity, and we investigate the approximate solution on some closed interval in R.
We start by casting a given linear or nonlinear BVP into a sincGalerkin form accurate to the order [1]. This discretization yields a set of linear or nonlinear algebraic equations that include all unknown coefficients. These equations are expressed in a nonlinear or linear matrix form depending on (1.1). If the equation is linear, the LU decomposition method can be used to find unknown coefficients. However, if it is not linear, the coefficients can be found by the Newton interpolation method for nonlinear equation systems by using Maple. The methodology is illustrated on nonlinear ordinary differential equations with Dirichlettype boundaries. Once the solution is obtained, we compare its accuracy with the Newton method as a graphical and numerical simulation by using Maple.
We start with some literature on the sincGalerkin methods. The sinc methods were introduced in [2] and expanded in [1] by Frank Stenger. Sinc functions were first analyzed in [3] and [4]. An extensive research of sinc methods for twopoint boundary value problems can be found in [5,6]. In [7,8] parabolic and hyperbolic problems are discussed in detail. Some kind of singular elliptic problems are solved in [9], and the symmetric sincGalerkin method is introduced in [10]. The sinc domain decomposition is presented in [1114]. Also, iterative methods for symmetric sincGalerkin systems are given in [1517]. Sinc methods are studied thoroughly in [18]. Applications of sinc methods can also be found in [1921]. The article [22] summarizes the results that are obtained by sinc numerical methods of computation. In [14] a numerical solution of the Volterra integrodifferential equation by means of the sinc collocation method is considered. The paper [1] illustrates the application of a sincGalerkin method to the approximate solution of linear and nonlinear secondorder ordinary differential equations and to the approximate solution of some linear elliptic and parabolic partial differential equations in the plane. The fully sincGalerkin method is developed for a family of complexvalued partial differential equations with timedependent boundary conditions [19]. In [23] some novel procedures of using sinc methods to compute the solutions of three types of medical problems are illustrated. In [24], the sincbased algorithm is used to solve a nonlinear set of partial differential equations. A new sincGalerkin method is developed for approximating the solution of convection diffusion equations with mixed boundary conditions on halfinfinite intervals in [25]. The work which is presented in [26] deals with the sincGalerkin method for solving nonlinear fourthorder differential equations with homogeneous and nonhomogeneous boundary conditions. In [27], the sinc methods are used to solve secondorder ordinary differential equations with homogeneous Dirichlettype boundary conditions. In the paper given in [28], the sincGalerkin method is applied to solving Troesch’s problem. The properties of the sinc procedure are utilized to reduce the computation of Troesch’s equation to nonlinear equations with unknown coefficients.
2 Sinc basis functions
Let C denote the set of all complex numbers, and for all define the sine cardinal or sinc function by
For , the translated sinc function with evenly spaced nodes is given by
For various values of k, the sinc basis function on the whole real line, , is illustrated in Figure 1. For various values of h, the central function is illustrated in Figure 2.
If a function is defined over the real line, then for the series
is called the Whittaker cardinal expansion of f whenever this series converges. The infinite strip Ds of the complex w plane, where , is given by
In general, approximations can be constructed for infinite, semiinfinite, and finite intervals. Define the function
which is a conformal mapping from , the eyeshaped domain in the zplane, onto the infinite strip , where
This is shown in Figure 3.
Figure 3. The relationship between the eyeshaped domainand the infinite strip.
For the sincGalerkin method, the basis functions are derived from the composite translated sinc functions:
for . These are shown in Figure 4 for real values of x. The function is an inverse mapping of . We may define the range of on the real line as
For the evenly spaced nodes on the real line, the image which corresponds to these nodes is denoted by
A list of conformal mappings may be found in Table 2.1 [6].
Figure 4. Three adjacent memberswhenandof the mapped sinc basis on the interval.
Definition 2.1 Let be a simply connected domain in the complex plane C, and let denote the boundary of . Let a, b be points on and ϕ be a conformal map onto such that and . If the inverse map of ϕ is denoted by φ, define
We can use Table 1 to choose convenient conformal map according to boundary conditions.
Table 1. Conformal mappings and nodes for several subintervals ofR
Definition 2.2 Let be the class of functions F that are analytic in and satisfy
where
and on the boundary of it satisfies
The proof of following theorems can be found in [1].
Theorem 2.1Let Γ be, , then forsufficiently small,
where
For the sincGalerkin method, the infinite quadrature rule must be truncated to a finite sum. The following theorem indicates the conditions under which an exponential convergence results.
Theorem 2.2If there exist positive constantsα, βandCsuch that
then the error bound for the quadrature rule (2.14) is given by
The infinite sum in (2.14) is truncated with the use of (2.16) to arrive at (2.17).
Making the selections
whereis an integer part of the statement andNis the integer value which specifies the grid size, then
We used Theorems 2.1 and 2.2 to approximate the integrals that arise in the formulation of the discrete systems corresponding to the secondorder boundary value problem.
Theorem 2.3Letϕbe a conformal onetoone map of the simply connected domainonto. Then
3 Convergence analysis
Consider the following problem:
with Dirichlettype boundary condition
where P, Q, R, and F are analytic on D. We consider sinc approximation by the formula
The unknown coefficients in Eq. (3.3) are determined by orthogonalizing the residual with respect to the sinc basis functions. The Galerkin method enables us to determine the coefficients by solving the nonlinear system of equations
Let and be analytic functions on D. The inner product in (3.5) is defined as follows:
where w is the weight function. For the secondorder problems, it is convenient to take [1]
For Eq. (3.1), we use the notations (2.21)(2.23) together with the inner product given in (3.5) [1] to get the following approximation formulas:
where etc. The choices and yield [1] accuracy for each of the approximations in (3.8)(3.11).
Using (3.5), (3.8)(3.11), we obtain a nonlinear system of equations for numbers .
The nonlinear system with unknowns given in (3.5) can be expressed by means of matrices. Let and let , , be column vectors defined by
Let denote a diagonal matrix whose diagonal elements are and nondiagonal elements are zero, and also let , and denote the matrices
With these notations, the discrete system in (3.5) takes the form:
Theorem 3.1Letc, be an mvector whosejth component isandthen the system (3.16) yields the following matrix system whose dimensions are:
Now we have a nonlinear system withequations in theunknown coefficients. If we solve (3.17) with the Newton method (for nonlinear equation systems) by using Maple, we can obtaincoefficients for the approximate sincGalerkin solution
4 Examples
In this section, three examples are given to illustrate the performance of the sincGalerkin method by solving nonlinear Dirichlettype boundary value problems. Each of these problems have been chosen to simulate how the solutions change in different zero boundary intervals. In the following examples, the discrete sinc system defined by (3.18) is used to compute the coefficients ; . The computations are done by the algorithm which we developed for sincGalerkin method by using Maple. The algorithm automatically compares the sincmethod to the Newton method. The following examples show that the sincGalerkin method is a very efficient and powerful tool for nonlinear Dirichlettype boundary value problems.
Example 4.1 Consider the following nonlinear Dirichlettype boundary value problem on the interval :
We choose the weight function according to [1], , and by taking , , for , the solutions presented in Figure 5 and Table 2.
Figure 5. The redcolored curve displays the Newton solution and the green one is an approximate solution of Eq. (4.1).
Table 2. The numerical results for the approximate solutions obtained by sincGalerkin in comparison with the Newton solutions of Eq. (4.1) for
Example 4.2 Let us have the following form of nonlinear Dirichlettype boundary value problem on the interval :
where , and by taking , , for we get the solutions presented in Figure 6 and Table 3.
Figure 6. The redcolored curve displays the Newton solution and the green one is an approximate solution of Eq. (4.2).
Table 3. The numerical results for the approximate solutions obtained by sincGalerkin in comparison with the exact solutions of Eq. (4.2) for
Example 4.3 In this case, we take the problem to be given on the interval
where we chose , and by taking , , for we get the results presented in Figure 7 and Table 4.
5 Discussion
A new efficient computer application of sincGalerkin method has been presented for nonlinear BVPs. The main advantage of our technique compared to other methods (e.g., Newton’s method) is that the solution is independent of the singularity conditions and valid for Dirichlettype boundary conditions. The order of accuracy used in this paper is . We have used different N node points for all figures presented in this paper. Even though the numerical solution looks complex for even node points, Maple handles it very well. In the Appendix, a useful Maple program is given to explain the technique and to show how the same solution can be used for different boundary conditions. By using the same program, substituting N and other parameters (like equations, boundaries), different solutions and graphics can be produced. The total time taken on a 3.5 GHz Pentium I7 processor with 8 Core and 8 GB RAM for producing figures and numerical results is less than 20 seconds.
6 Conclusion
In this study, the sincGalerkin method has been employed to find the solutions of secondorder nonlinear Dirichlettype boundary value problems on some closed real interval and the method has been compared to the Newton method. Our main purpose is to find the solution of boundary value problems which arise from the singular problems for which the Newton method does not converge at singular points. The powerful side of our method is that it can easily compute solutions even if the equation has singularities. The Newton method can fail when computing some complicated forms of governing equations; on the other hand, our method can easily handle this situation. The examples show that the accuracy improves by increasing the number of sinc grid points N. The method presented here is simple and gives a numerical solution, which is valid for various boundary conditions. We have developed a very efficient algorithm to solve secondorder nonlinear Dirichlettype boundary value problems with sincGalerkin method in Maple Computer Algebra System. Several nonlinear BVPs have been solved by using our technique in less than 20 seconds. All computations and graphical representations have been prepared automatically by our algorithm.
Appendix: A computer application of numeric solutions for nonlinear boundary value problems (NBVPs)
We demonstrate below how to solve and simulate for a nonlinear BVP. For example, the following Maple code computes and simulates Example 4.3.
Set all parameters as default values
> restart:
For drawing approximation graphics, we must type the following line
> with(plots):
A user has to specify with (linalg) for linear algebra operations in Maple
> with(linalg):
A user can define the grid point size N for sincGalerkin approximation
> N:=48:
The boundary conditions are given as Eq. (4.3).
> a:=4:
> b:=5:
> Boundaries:=y(a)=0,y(b)=0;
P, Q and R are the variable coefficients of Eq. (1.1). In Maple for Eq. (4.3) they are defined as follows:
> P(x):=1;
> Q(x):=1;
> R(x):=1;
F is right side of Eq. (4.3)
> F(x):=cos(Pi*x^2)*x;
We can write a nonlinear part of Eq. (1.1) as follows. User can define any form of nonlinearity in this section.
> NLPart:=exp(sin(y(x)))*y(x)^2/(1+y(x));
The main form of Eq. (1.1)
> Equation:=P(x)*diff(y(x),x$2)+Q(x)*diff(y(x),x$1)+R(x)*NLPart=F(x);
If the user needs, the main equation can be written in the latex format
In order to compare our method with the Newton interpolation (for nonlinear ODE) method, we first solve Eq. (4.3) numerically as follows:
Prepare the plot of the Newton solution
> PlotNewtonSolution:=odeplot(NewtonSolution,a....b):
To define , and matrices given in Eqs. (3.13)(3.15), we use piecewise functions in Maple in the following way:
> delta[0]:=unapply(piecewise(j=k,1,j<>k,0),j,k):
> delta[1]:=unapply(piecewise(j=k,0,j<>k,((1)^(kj))/(kj)),j,k):
> delta[2]:=unapply(piecewise(j=k,(Pi^2)/3,j<>k,2*(1)^(kj)/(kj)^2),j,k):
The parameters for sincapproximation given [1]
> d:=Pi/2:
> h:=2/sqrt(N):
The evenly spaced nodes given (2.9) and Table 1 are defined as follows:
> xk:=unapply((a+b*exp(k*h))/(1+exp(k*h)),k);
The conformal map in Table 1 for sincGalerkin method and its derivatives is computed as follows:
> phi:=unapply(log((xa)/(bx)),x);
> Dphi:=unapply(simplify(diff(phi(x),x)),x):
> D2phi:=unapply(simplify(diff(phi(x),x$2)),x):
The weight function and its derivatives are computed for using an inner product to discretization Eq. (4.3)
> w:=unapply(1/Dphi(x),x):
> Dw:=unapply(simplify(diff(w(x),x$1)),x):
> D2w:=unapply(simplify(diff(w(x),x$2)),x):
By using sincdiscretization in (3.16), the matrix system with dimensions defined in (3.17) is obtained by the following iteration:
If we want to obtain solutions of linear BVPs, we can use the following lines. They can reduce time complexity. Here, the linear solution is given as a comment (“#”).
> #for Linear system
> #vars:=seq(c[i],i=N..N):
> #A,b:=LinearAlgebra[GenerateMatrix](evalf(MatrixSystem),[vars]):
> #c:=linsolve(A,b);
In this paper, we want to solve nonlinear problems. Then we use fsolve function given by Maple to find unknown coefficients (3.17)(3.18) from nonlinear matrix systems. This function can solve any nonlinear systems by using the Newton method (for nonlinear equation systems).
> c:=fsolve(evalf(MatrixSystem)):
Finally, we have unknown coefficients for the approximate sincGalerkin solution (3.18)
We define plot of Eq. (4.3) obtained by the sincGalerkin solution
> SincGalerkinPlot:=plot({ApproximateSol(x)},x=a..b,color=green,thickness=1):
Simulation: Figure 5, Figure 6, and Figure 7 are obtained as
Enter the number of digits here
> Digits := 15:
Tables 2, 3, and 4 are obtained by the following code:
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AS proposed main idea of the solution schema by using Sinc Method. He developed computer algorithm and worked on theoretical aspect of problem. MK searched the materials about study and compared with other techniques. MAA contributed us with his experience on Nonlinear Approximation methods. MB contributed us with his experience on Nonlinear Approximation methods, suggested us some valuable techniques.
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