# A new Fibonacci type collocation procedure for boundary value problems

- Ayşe Betül Koç
^{1}Email author, - Musa Çakmak
^{2}, - Aydın Kurnaz
^{1}and - Kemal Uslu
^{1}

**2013**:262

https://doi.org/10.1186/1687-1847-2013-262

© Koç et al.; licensee Springer. 2013

**Received: **10 May 2013

**Accepted: **6 August 2013

**Published: **27 August 2013

## Abstract

In this study, we present a new procedure for the numerical solution of boundary value problems. This approach is mainly founded on the Fibonacci polynomial expansions, the so-called pseudospectral methods with the collocation method. The applicability and effectiveness of our proposed approach is shown by some illustrative examples. Then, the results indicate that this method is very effective and highly promising for linear differential equations defined on any subinterval of the real domain.

**MSC:** 35A25.

## Keywords

## 1 Introduction

where the set $\{{\phi}_{k}(x)\}$ is a special trial polynomial (or function) basis and ${a}_{k}$’s are constant coefficients [1–9]. One of the main utilization areas of the expansions with different basis can be seen in the solution methods for the differential equations. The idea of finding the solution of a differential equation in form (1.1) goes back, at least, to Lanczos (1938) [10, 11]. Then, three main techniques have been evolved from that idea, and each of these techniques has its own advantages in the implementation (details and applications of those can be seen in [11–13]). Here, we deal only with the pseudospectral (collocation) type method. In the pseudospectral method, the solution of a differential equation is expressed as a linear combination of the polynomials in the basis set. Therefore, the coefficients in the combination are determined by the use of certain discrete points called *collocation points*. Thus, the accuracy of the approximation and the efficiency of its implementation are closely related to the choice of the grid points and the basis set. Determination of an appropriate set of basis should be kept in view of some rules. The most common one of those rules is that the geometry or the field of applicability determines the basis set. Then, collocation points are chosen according to the basis (for details see [12]). For example, the well-known basis functions of the Fourier expansion $\{1,cos(nx),sin(nx),\dots \}$ are all periodic. Thus, Fourier expansion is good for the solutions of the problems with periodic behaviors [12–14]. On the other hand, since the Chebyshev polynomials of the first kind and Legendre polynomials are orthogonal in the interval $[-1,1]$, non-periodic problems on the range of $[-1,1]$ can be solved by the collocation method, the matrix method or the Tau method [15–23]. However, when the problem is posed on an unbounded interval, alternative strategies are developed for the solution, such as domain truncation [24] and choosing a basis functions intrinsic to an infinite interval as Sinc [25], Hermite [26], and exponential Chebyshev [27]) or to semi-infinite interval as rational Chebyshev [28–30], and Laguerre [31].

In this work, our aim is to develop a new type of collocation method for the boundary value problems (BVPs) on any subinterval of the real axis requiring no domain translation. For this reason, Fibonacci polynomials that have, so far, never been used as a basis for the collocation are considered. Even though this new approach is mainly intended for the BVPs, it can be successfully implemented on the initial value problems (IVPs).

with the initial conditions ${F}_{0}(x)=0$, ${F}_{1}(x)=1$. It is also noteworthy that the polynomial ${\phi}_{k}(t)$ turns into ${F}_{k}(x)$ when the variable change of $t=\frac{x}{2}$ is used in equation (1.2).

Byrd [32] has investigated some fundamental properties and certain applications for the expansion of an analytic function in a series of basic set of Fibonacci polynomials. Moreover, many other important properties of those polynomials are also studied by Falcon and Plaza in [34, 35] and references therein.

## 2 Properties of the Fibonacci polynomials

The *k*-Fibonacci numbers and polynomials have been defined as follows:

**Definition 1** [34]

*k*, the

*k*-Fibonacci sequence is defined recurrently by

**Definition 2** [34]

*k*is a real variable

*x*in equation (2.1), then it is obvious that ${F}_{k,n}={F}_{x,n}$. Therefore, the corresponding Fibonacci polynomials are given by the following general formula

The next proposition indicates the relation between the derivatives of the Fibonacci polynomials followed by the integral equation.

**Proposition 1** [35]

*The equality*

*holds for all natural numbers*

*n*.

*Thus*,

*in view of*(2.3),

*it is easy to verify the integral equation*

*If* *n* *is even*, *it can be seen that* ${F}_{n+1}(0)={F}_{n-1}(0)=1$ *and*, *for odd* *n’s*, *we write* ${F}_{n+1}(0)={F}_{n-1}(0)=0$.

### Function approximation

*N*Fibonacci polynomials can be written

**A**are given, respectively, by

### Matrix relations of the derivatives of approximation

*k*th order derivative of the function (2.5) can be written as

*N*terms, we get the approximation

shows the coefficient vector of the polynomial approximation of *k* th order derivative.

**Proposition 2**

*Let*$f(x)$

*and*

*kth order derivative be the functions given by*(2.6)

*and*(2.9),

*respectively*.

*Then*,

*there exists a relation between the Fibonacci coefficient matrices as*

*where*

**D**

*is*$N\times N$

*operational matrix for the derivative defined by*

*Proof*By using the integral relation (2.4), the Fibonacci coefficients ${a}_{r}^{(k)}$ and ${a}_{r}^{(k+1)}$ hold the equality,

where ${\mathbf{A}}^{(0)}=\mathbf{A}$. □

**Corollary**

*From equations*(2.9)

*and*(2.11),

*it is clear that the*

*kth order derivative of the function can be expressed in terms of the Fibonacci coefficients as follows*

## 3 Solution procedure for the ODE’s

*n*th order,

*k*th order derivatives of the unknown function at the collocation points can be written in the matrix form as

This form can also be achieved by replacing some rows of the matrix (3.8) by the rows of (3.12) or adding those rows to the matrix (3.8), provided that $det({\mathbf{W}}^{\ast})\ne 0$. Finally, the vector **A** (thereby vector of the coefficients ${a}_{r}$) is determined by applying some numerical methods designed especially to solve the system of linear equations. On the other hand, when the singular case $det({\mathbf{W}}^{\ast})=0$ appears, the least square methods are inevitably available to reach the best possible approximation. Therefore, the approximated solution can be obtained. This would be the Fibonacci series expansion of the solution to the problem (3.1) with specified conditions.

## 4 Numerical results

In this part, three illustrative examples have been shown in order to clarify the findings of the previous section. Then, the solutions ${y}^{a}(x)$ of the proposed method are compared with the exact solutions ${y}^{e}(x)$ in the tables and in the corresponding figures. It is noted here that the number of collocation points in the examples is indicated by the capital letter *N*.

**Example 1**Consider the linear nonhomogeneous IVP [36],

for which the exact solution is ${y}^{e}(x)=sinx$.

**Absolute errors of Example 1 at different points**

x | $\mathit{N}\mathbf{=}\mathbf{7}$ | $\mathit{N}\mathbf{=}\mathbf{9}$ |
---|---|---|

−1 | 0.361E−04 | 0.231E−06 |

−0.8 | 0.123E−05 | 0.229E−07 |

−0.6 | 0.232E−05 | 0.25E−08 |

−0.4 | 0.227E−05 | 0.43E−08 |

−0.2 | 0.462E−06 | 0.9E−09 |

0 | 0.305E−11 | 0.762E−09 |

0.2 | 0.462E−06 | 0.25E−08 |

0.4 | 0.227E−05 | 0.58E−08 |

0.6 | 0.232E−05 | 0.41E−08 |

0.8 | 0.123E−05 | 0.245E−07 |

1 | 0.361E−04 | 0.23E−06 |

**Example 2**Consider now a linear homogeneous BVP

which is known to have the exact solution ${y}^{e}(x)={e}^{-{x}^{2}}$.

${\mathit{L}}_{\mathbf{\infty}}$
**Errors of Example 2 for different**
N
**values**

**Example 3**Consider a linear nonhomogeneous BVP

*N*. The exact and the approximate solutions of Example 3 for $N=9$ are plotted in Figure 3.

${\mathit{L}}_{\mathbf{\infty}}$
**Errors of Example 3**

N | Present method | B-spline wavelet algorithm [38] |
---|---|---|

${\mathit{L}}_{\mathbf{\infty}}$ | ${\mathit{L}}_{\mathbf{\infty}}$ | |

8 | 2.58E−06 | - |

16 | 8.94E−09 | - |

32 | 4.04E−09 | 9.4E−05 |

64 | 2.09E−09 | 2.0E−05 |

## 5 Conclusion

In this study, our aim is to propose a novel matrix method, based on the Fibonacci polynomials. Therefore, the operational matrices of derivative **D** and Fibonacci coefficient matrix **F** are introduced in the main body of the study. The matrix **D** is computationally very attractive, since it has few nonzeros above the main diagonal. Additionally, unlike the Chebyshev polynomial method, the Fibonacci approach does not require interval translation of the problem to an appropriate domain. Then, the reliability and efficiency of the method are verified by some illustrative examples of the boundary value problems. When the results are compared with some existing methods, the proposed method demonstrates its power in accuracy.

## Declarations

### Acknowledgements

The authors would like to thank the editor and referees for their valuable comments and remarks, which led to a great improvement of the article. Also, authors would like to thank the Selcuk University and TUBITAK for their support. Authors reveal here that this study was partially presented orally at the International Congress in Honour of Professor Hari M Srivastava, August 23-26, 2012, Bursa, Turkey.

## Authors’ Affiliations

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