Research Article  Open Access
M. Sharifi, S. Karimi Vanani, F. Khaksar Haghani, M. Arab, S. Shateyi, "On a DerivativeFree Variant of King’s Family with Memory", The Scientific World Journal, vol. 2015, Article ID 514075, 5 pages, 2015. https://doi.org/10.1155/2015/514075
On a DerivativeFree Variant of King’s Family with Memory
Abstract
The aim of this paper is to construct a method with memory according to King’s family of methods without memory for nonlinear equations. It is proved that the proposed method possesses higher Rorder of convergence using the same number of functional evaluations as King’s family. Numerical experiments are given to illustrate the performance of the constructed scheme.
1. Introduction
Many problems arising in diverse disciplines of mathematical sciences can be described by a nonlinear equation of the following form (see, e.g., [1]): where is a sufficiently differentiable function in a neighborhood of a simple zero of (1). If we are interested in approximating the root , we can do it by means of an iterative fixedpoint method in the following form: provided that the starting point is given.
In this work, we are concerned with the fixedpoint methods that generate sequences presumably convergent to the true solution of a given single smooth equation. These schemes can be divided into onepoint and multipoint schemes. We remark that the onepoint methods can possess high order by using higher derivatives of the function, which is expensive from a computational point of view. On the other hand, the multipoint methods are allowing the user not to waste information that had already been used. This approach provides the construction of efficient iterative rootfinding methods [2].
In such circumstance, special attention is devoted to multipoint methods with memory that use already computed information to considerably increase convergence rate without additional computational costs. This would be the focus of this paper.
Traub in [2] proposed the following method with memory (TM):with the order of convergence .
The iterative methods with memory can improve the order of convergence of the without memory method without any additional functional calculations, and this results in a higher computational efficiency index. We remark that it is assumed that an initial approximation close enough to the sought simple zero and are given for iterative methods of type (3).
Recently, authors in [3] designed an approach to make derivativefree families with low complexity out of optimal methods. In fact, they conjectured that every time that one applies the approximation of the derivative , with , on an optimal order , we will need for preserving the order of convergence.
For instance, choosing the wellknown optimal twostep family of King (KM) [4],and the conjecture of CorderoTorregrosa, one may propose the following method (DKM): wherein
In this work, we propose a twostep method with memory possessing a high efficiency index according to the wellknown family of King’s methods (5).
Our inspiration and motivation for constructing a higherorder method are linked in a direct manner with the fundamental concept of numerical analysis that any numerical method should give as accurate as possible output results with minimal computational cost. To state the matter differently, it is necessary to pursue methods of higher computational efficiency.
For more background concerning this topic, one may refer to [5, 6].
The paper is organized as follows. In Section 2, the aim of this paper is presented by contributing an iterative method with memory based on (5) for solving nonlinear equations. The proposed scheme is an extension over (4) and has a simple structure with an increased computational efficiency. In Section 3, we compare the theoretical results by applying the definition of efficiency index and further supports are furnished whereas numerical reports are stated. Some concluding remarks will be drawn in Section 4 to end the paper.
2. A New Method with Memory
In this section, we propose the following iterative method with memory based on (5):wherein the selfaccelerating parameter is . The error equation of (5) is () where . We now must find a way so as to vanish the asymptotic error constant .
Toward this goal, one can increase the order by considering the following substitution: Since the zero is not known, relation (9) cannot be used in its exact form and we must approximate it recursively. This builds a variant with memory for King’s family by using where . Now if we consider to be Newton’s interpolation polynomial of third degree set through four available approximations at the end of each cycle, we can propose the following new method with memory:
Note that, for example, we have the following formulation for the interpolating polynomial:
Acceleration in convergence for (11) is based on the use of a variation of one free nonzero parameter in each iterative step. This parameter is calculated using information from the current and previous iteration(s) so that the developed method may be regarded as method with memory according to Traub’s classification [2].
We are at the time to write about the theoretical aspects of our proposed solver (11).
Theorem 1. Let the function be sufficiently differentiable in a neighborhood of its simple zero . If an initial approximation is sufficiently close to , then, the order of convergence of the twostep method (11) with memory is at least 4.23607.
Proof. Let be a sequence of approximations generated by an iterative method. The error relations with the selfaccelerating parameter for (11) are in what follows: Using a symbolic computations, we attain that Substituting the value of from (16) in (15), one may obtain Note that in general we know that the error equation should read , where and are to be determined. Hence, one has , and subsequently Thus, it is easy to obtain wherein is a constant. This results inwith two solutions . Clearly the value for is acceptable and would be the convergence order of method (11) with memory. The proof is complete.
The increase of order is attained without any (new) additional function calculations so that the novel method with memory possesses a high computational efficiency index. This technique is an extension over scheme (5) to increase the order from 4 to 4.23607.
The accelerating method (11) is new, simple, and useful, providing considerable improvement of convergence rate without any additional function evaluations in contrast to the optimal twostep methods without memory.
We also remark that an alternative form of our proposed method with memory could be deduced using backward finite difference formula at the beginning of the first substep and a minor modification in the accelerators; that is to say, we have the following alternative method with memory possessing 4.23607 as its order (APM) as well:
Theorem 2. Let the function be sufficiently differentiable in a neighborhood of its simple zero . If an initial approximation is sufficiently close to , then, the order of convergence of the twostep method (21) with memory is at least 4.23607.
Proof. The proof of this theorem is similar to Theorem 1. It is hence omitted.
3. Numerical Computations
Computational efficiency of different iterative methods with and without memory can be measured in a prosperous manner by applying the definition of efficiency index. For an iterative method with convergence ()order that requires functional evaluations, the efficiency index (also named computational efficiency) is calculated by OstrowskiTraub’s formula [2]: According to this, we find where SM is the quadratically convergent method of Steffensen without memory [7].
It should be remarked that Džunić in [8] designed an efficient onestep Steffensentype method with memory possessing order of convergence as follows: and Cordero et al. in [9] presented a twostep biparametric Steffensentype iterative method with memory possessing seventh order of convergence:
Note that our main aim was to develop King’s family in terms of efficiencies index and was not to achieve the highest possible efficiency index.
Although these methods possess higher computational efficiency indices than our proposed method (11), we exclude them from numerical comparisons since our method is not a Steffensentype method and it is a Newtontype method with memory. For more refer to [10].
Now, we apply and compare the behavior of different methods for finding the simple zeros of some different nonlinear test functions in the programming package Mathematica [11] using multiple precision arithmetic to clearly reveal the high order of PM and APM. We compare methods with the same number of functional evaluations per cycle.
We notice that, by applying any root solver with local convergence, a special attention must be paid to the choice of initial approximations. If initial values are sufficiently close to the sought roots, then the expected (theoretical) convergence speed is obtainable in practice; otherwise, the iterative methods show slower convergence, especially at the beginning of the iterative process.
In this section, the computational order of convergence (coc) has been computed byThe calculated value coc estimates the theoretical order of convergence well when pathological behavior of the iterative method (i.e., slow convergence at the beginning of the implemented iterative method, oscillating behavior of approximations, etc.) does not exist.
Here the results of comparisons for the test functions are given by applying 1000 fixed floating point arithmetic using the stop termination .
Example 3. We consider the following nonlinear test function in the interval : using the initial approximation . The results are provided in Table 1.

In this section, we have used whenever required. Furthermore, for DKM we considered .
Example 4. We compare the behavior of different methods for finding the complex solution of the following nonlinear equation: using the initial approximation where . The results for this test are given in Table 2.

It is evident from Tables 1 and 2 that approximations to the roots possess great accuracy when the proposed method with memory is applied. Results of the fourth iterate in Tables 1 and 2 are given only for demonstration of convergence speed of the tested methods and in most cases they are not required for practical problems at present.
We also incorporated and applied the developed methods with memory (11) and (21) for different test examples and obtained results with the same behavior as above. Hence, we could mention that the theoretical results are upheld by numerical experiments and thus the new method is good with a high computational efficiency index.
4. Summary
In this paper, we have proposed a new twostep Steffensentype iterative method with memory for solving nonlinear scalar equations. Using one selfcorrecting parameter calculated by Newton interpolatory polynomial, the order of convergence of the constructed method was increased from 4 to 4.23607 without any additional calculations.
The new method was compared in performance and computational efficiency with some existing methods by numerical examples. We have observed that the computational efficiency index of the presented method with memory is better than those of other existing twostep Kingtype methods.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Authors’ Contribution
The authors have made the same contribution. All authors read and approved the final paper.
References
 A. S. AlFhaid, S. Shateyi, M. Z. Ullah, and F. Soleymani, “A matrix iteration for finding Drazin inverse with ninthorder convergence,” Abstract and Applied Analysis, vol. 2014, Article ID 137486, 7 pages, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 J. F. Traub, Iterative Methods for the Solution of Equations, PrenticeHall, New York, NY, USA, 1964. View at: MathSciNet
 A. Cordero and J. R. Torregrosa, “Lowcomplexity rootfinding iteration functions with no derivatives of any order of convergence,” Journal of Computational and Applied Mathematics, 2014. View at: Publisher Site  Google Scholar
 R. F. King, “A family of fourth order methods for nonlinear equations,” SIAM Journal on Numerical Analysis, vol. 10, pp. 876–879, 1973. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 N. Huang and C. Ma, “Convergence analysis and numerical study of a fixedpoint iterative method for solving systems of nonlinear equations,” The Scientific World Journal, vol. 2014, Article ID 789459, 10 pages, 2014. View at: Publisher Site  Google Scholar
 J. P. Jaiswal, “Some class of third and fourthorder iterative methods for solving nonlinear equations,” Journal of Applied Mathematics, vol. 2014, Article ID 817656, 17 pages, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 J. F. Steffensen, “Remarks on iteration,” Skandinavisk Aktuarietidskrift, vol. 16, pp. 64–72, 1933. View at: Google Scholar
 J. Džunić, “On efficient twoparameter methods for solving nonlinear equations,” Numerical Algorithms, vol. 63, no. 3, pp. 549–569, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 A. Cordero, T. Lotfi, P. Bakhtiari, and J. R. Torregrosa, “An efficient twoparametric family with memory for nonlinear equations,” Numerical Algorithms, 2014. View at: Publisher Site  Google Scholar
 X. Wang and T. Zhang, “A new family of Newtontype iterative methods with and without memory for solving nonlinear equations,” Calcolo, vol. 51, no. 1, pp. 1–15, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 S. Wagon, Mathematica in Action, Springer, New York, NY, USA, 3rd edition, 2010.
Copyright
Copyright © 2015 M. Sharifi et al. 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.