Research Article  Open Access
Diyashvir Kreetee Rajiv Babajee, Rajinder Thukral, "On a 4Point SixteenthOrder King Family of Iterative Methods for Solving Nonlinear Equations", International Journal of Mathematics and Mathematical Sciences, vol. 2012, Article ID 979245, 13 pages, 2012. https://doi.org/10.1155/2012/979245
On a 4Point SixteenthOrder King Family of Iterative Methods for Solving Nonlinear Equations
Abstract
A oneparameter 4point sixteenthorder Kingtype family of iterative methods which satisfy the famous KungTraub conjecture is proposed. The convergence of the family is proved, and numerical experiments are carried out to find the best member of the family. In most experiments, the best member was found to be a sixteenthorder Ostrowskitype method.
1. Introduction
Solutions of nonlinear equations by iterative methods have been of great interest to numerical analysts. One of the popular methods is the classic Newton method (Newton Raphson method). It has quadratic convergence close to the root, that is the number of good digits is roughly doubled at each iteration. Higher order methods which require the second or higher order derivatives can be costly and thus time consuming. Also, the Newton method can suffer from numerical instabilities. It is consequently important to study higher order variants of Newton's method, which require only function and first derivative calculation and are more robust as compared to Newton's method. Such methods are known as multipoint NewtonLike methods in the Traub sense [1]. Multipoint methods without memory are methods that use new information at a number of points. It is an efficient way of generating higher order methods free from second and higher order derivatives. For a survey of these methods, please refer to [2–4]. In this work, we develop a oneparameter 4point sixteenthorder Kingtype family of iterative methods, which satisfy the famous KungTraub conjecture. We prove the local convergence of the methods and its asymptotic error constant. We test our methods by varying the parameter of the family in a suitable interval and obtain the best value of the parameter for the methods with the highest computational order of convergence. We also compare our methods with other optimal sixteenth order methods. Furthermore, we test the family with two more nonlinear functions and find the best member based on the highest number of successful converging points and lowest mean iteration number.
2. Preliminaries
Let define an iterative function (IF).
Definition 2.1 (see [6]). If the sequence tends to a limit in such a way that
for , then the order of convergence of the sequence is said to be , and is known as the asymptotic error constant. If , , or , the convergence is said to be linear, quadratic, or cubic, respectively.
Letting , then the relation
is called the error equation. The value of is called the order of convergence of the method.
Definition 2.2 (see [6]). The efficiency index is given by where is the total number of new function evaluations (the values of and its derivatives) per iteration.
KungTraub Conjecture (see [7])
Let be an IF without memory with evaluations. Then
where is the maximum order.
We use the approximate computationalorder of convergence, [8] given by
3. Developments of the Methods
The secondorder NewtonRaphson method is given by
It is an optimal 1point IF with efficiency index of 1.414.
A oneparameter King family of fourthorder IF [9] is given by
The members of the family are 2point I.F.s with efficiency index of 1.587. The case corresponds to the famous Ostrowski method [6].
Several optimal eightorder methods are developed in [10–12]. Recently, Thukral and Petković [13] developed a family of optimal eighthorder Kingtype IF given by where and is a weight function satisfying
If we choose satisfying (3.5), we get a family of optimal eighthorder IF given by where
The members of the family are 3point eighthorder I.F.s with efficiency index of 1.682.
Geum and Kim [14] developed a biparametric family of optimally convergent sixteenthorder 4point I.F. with their fourthstep weighting function as a sum of a rational and a generic twovariable function: where are weighting functions, is an analytic function in a region containing the region , are to be chosen freely, and
We consider the case and for numerical experiments and term the I.F. as 16th GK1.
Geum and Kim [15] proposed another family of optimal sixteenthorder 4point I.F.s with a linear fraction plus a trivariate polynomial as the fourthstep weighting function. Their family is given by where is an analytic function in a region containing the region and are to be chosen freely.
We consider the case and for numerical experiments and term the I.F. as 16th GK2.
We observe that the 16th FGK1 and 16th FGK2 family of IFs require two parameters and an analytic function. Therefore, we develop a simplified oneparameter optimal 4point sixteenthorder Kingtype family of IFs based on 4th FK and 8th FK families. We propose the following family: where
4. Convergence Analysis of the 16th FK Family of IFs
In this section, we prove the local and sixteenthorder of the 16th FK family of I.F.s using classical Taylor expansion.
Theorem 4.1. Let a sufficiently smooth function have a simple root in the open interval . Then the class of methods without memory (3.15) is of local sixteenthorder convergence.
Proof. Let
Using the Taylor series and the symbolic software such as Maple we have
so that
Now, the Taylor expansion of about gives
Using (4.2), (4.6), and (4.5), we have
so that
Similarly, we have
so that
and finally we get
By a similar argument, we have
so that
and finally we get
In the next section, we carry out numerical experiments to find the best member of the family and compare it to the Geum and Kim sixteenthorder IFs
5. Numerical Experiments
The test functions and their exact root are displayed in Table 1. The approximation is calculated by using the same total number of function evaluations (TNFE) for all I.F.s considered. In the calculations, 15 TNFE are used by each I.F. For the 16th FK family, we choose a suitable range of values of , which are based on the initial approximation of the root. indicates the values of excluded in the range because of invalid estimate. The best value of is chosen based on the smallest value of and the highest computationalorder of convergence (COC). The range and best value of are given in Table 2 for each function with its starting point. For most functions, the best value of , which corresponds to optimal 4point sixteenthorder Ostrowskitype I.F. Furthermore, the approximation and the computational order of convergence (COC) for the best member of the 16th FK family and the and IFs are displayed in Table 3. The results show that the best member of the 16th FK family gives the smallest value of for when compared to Geum and Kim sixteenth order IF.


We next test the 16th FK family by varying the starting points. Let us consider the functions and . We focus on the behaviour of the IFs with the starting points, which are equally spaced with in the intervals for and for to check the robustness of the IFs. A starting point was considered as divergent if it does not satisfy the condition in at most 100 iterations. We denote the quantity as the mean number of iterations from a successful starting point until convergence with . Let denote the number of successful points of 100 starting points. We test for 101 of the family with in the interval . Figure 1 shows the variation of the converging points and mean iteration number with respect to for the function . We can observe the family is globally convergent for the values of and . It is the member that has the smallest mean iteration number and is the most efficient member for . In Figure 2, we observe that the family is globally convergent for the function for all given values of . It is the member which has the lowest mean iteration number. Figure 3 shows the number of iterations needed to achieve convergence is 2 for any starting point in the interval [2,8.3] enclosing the root. This illustrates the high speed of convergence of the method. That is, higher order I.F. can converge in few iterations even if the starting point is not very close to the root. We consider two more test functions, one of which is of simple cubic type [16, 17] for which the logarithm restricts the function to be positive and its convex properties of the function are favorable for global convergence [16, 17]. We test for 100 starting points in the interval . The root correct to 14 digits. A starting point was considered as divergent if it does not satisfy the convergence condition in at most 100 iterations together with at any iterates. A similar analysis is performed for another test function, the Oscillatory Cubic [16, 17]: in which the single root has been moved marginally to but many local extrema have been introduced on a small scale [17]. This means that when the iterates of the I.F.s fall in the region where , they become zero or negative, causing them to diverge.
(a)
(b)
(a)
(b)
Figure 4 shows the variation of the converging points and mean iteration number with respect to for the Cubic function. It can be observed that the 16th FK family is globally convergent for , and . This is the member , which is the most efficient I.F. since it is globally convergent with the smallest mean iteration number of . We note that the family has many diverging points for negative values of .
(a)
(b)
Figure 5 shows the variation of the converging points and mean iteration number with respect to for the Oscillatory Cubic function. It can be observed that due to the perturbations the 16th FK family has difficulty with this function because its members have less than of starting points successfully converging. The mean of the IFs has also risen. The most efficient member of the family with the highest number of converging points (38) is the member with mean .
(a)
(b)
6. Conclusion
We develop a 4point sixteenth order King family of iterative methods. We prove the local convergence of the methods. We test the family via some numerical experiments to find the best member, which corresponds to a sixteenthorder Ostrowski method for most cases.
Acknowledgments
The authors are grateful to the unknown referees for their valuable comments to improve the paper. D. K. R. Babajee is an IEEE member.
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Copyright
Copyright © 2012 Diyashvir Kreetee Rajiv Babajee and Rajinder Thukral. 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.