Quadratically convergent algorithm for computing real root of nonlinear transcendental equations
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Abstract
Objectives
The present paper describes a new algorithm to find a root of nonlinear transcendental equations. It is found that RegulaFalsi method always gives guaranteed result but slow convergence. However, Newton–Raphson method does not give guaranteed result but faster than RegulaFalsi method. Therefore, the present paper used these two ideas and developed a new algorithm which has better convergence than RegulaFalsi and guaranteed result. One of the major issue in Newton–Raphson method is, it fails when first derivative is zero or approximately zero.
Results
The proposed method implemented the failure condition of Newton–Raphson method with better convergence. Error calculation has been discussed for certain real life examples using Bisection, RegulaFalsi, Newton–Raphson method and new proposed method. The computed results show that the new proposed quadratically convergent method provides better convergence than other methods.
Keywords
Root of transcendental equations RegulaFalsi method Newton–Raphson method Quadratic convergenceAbbreviations
 BM
Bisection method
 RF
RegulaFalsi
 N–R
Newton–Raphson
 PM
proposed method
Mathematics Subject Classification
65Hxx 65H04Introduction
Most of the real lifeproblems are nonlinear in nature therefore it is a challenging task for the mathematician and engineer to find the exact solution of such problems [1, 2]. In this reference, a number of methods have been proposed/implemented in the last two decades [1, 3, 4, 5, 6, 7, 8]. Analytical solutions of such nonlinear equations are very difficult, therefore only numerical method based iterative techniques are the way to find approximate solution. In the literature, there are some numerical methods such as Bisection, Secant, RegulaFalsi, Newton–Raphson, Mullers methods, etc., to calculate an approximate root of the nonlinear transcendental equations. It is well known [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14] that all the iterative methods require one or more initial guesses for the initial approximations.
In RegulaFalsi method, two initial guesses are taken in such a way that the corresponding function values have opposite signs. Then these two points are connected through the straight line and next approximation is the point where this line intersect the xaxis. This method gives guaranteed result but slow convergence therefore several researchers have improved this standard RegulaFalsi method into different hybrid models to speed up the convergence [1, 3, 4, 5, 7, 10, 11, 15, 16]. Thus previously published works have revised/implemented RegulaFalsi method in several ways to obtain better convergence. However, it is found that modified form of RegualFalsi method becomes more complicated from computational point of view. Therefore, in the present work RegualFalsi method has been used as its standard form with Newton–Raphson method and found better convergence. Newton–Raphson method is generally used to improve the result obtained by one of the above methods. This method uses the concept of tangent at the initial approximation point. The next approximate root is taken those value where the tangent intersect the xaxis. So this method fails where tangent is parallel to xaxis, i.e. the derivative of the function is zero or approximately zero. The order of convergence of Newton–Raphson method is two, therefore it converges very rapidly than other methods (Bisection, RegulaFalsi, etc.). However it does not always give guaranteed root. Many scientists and engineers have been proposed different hybrid models on Newton–Raphson method [8, 9, 12, 13, 14, 17, 18, 19, 20, 21, 22].
It is clear from the survey [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], that the most of new algorithms are either based on three classical methods namely Bisection, RegulaFalsi and Newton–Raphson or created by hybrid processes. In the present work, the proposed new algorithm is based on standard RegulaFalsi and Newton–Raphson methods, which provides guaranteed results and higher order convergence over RegulaFalsi method. The new proposed algorithm will work even the first derivative equals to zero where Newton–Raphson method fails.
Main text

Choose two values a and b where the root exists as in RegulaFalsi method.

Select the value such that the corresponding function is closer to zero as a and the other one as b, i.e. \(f(a) < f(b)\).

If first derivative at a is zero (i.e., \(f'(a) =0\)) then interchange the values of a and b i.e., interchange (a, b) to (b, a).
Formulation of proposed algorithm
The following Theorem gives the generalization of above formulation.
Lemma 1
Let f(x) be a continuous function and (a, b) be a sufficiently small interval such that \(f(a)f(b) < 0\), and \(f'(x)\) exists on [a, b]. Then the approximation of a root of f(x) can be find using the iterative formula given in Eq. (6).
Steps for calculating a root
 I.
Select two initial approximations \(x_{n1}\) and \(x_{n+1}\) such that product of the corresponding function values must be negative, i.e. \(f(x_{n1})f(x_{n+1})<0\).
 II.Now calculate \(x_n\) using the formula given in Eq. (6). Check \(f(x_n) = 0\), if so, then \(x_n\) is required root and process stop. Otherwise we check the following possible conditions.
 (i)
For \(f(x_n)f(x_{n1})<0\), suppose \(f(x_{n1}) < f(x_{n})\) then \(x_n\) replace by \(x_{n1}\) and \(x_{n1}\) replace by \(x_{n}\).
 (ii)
For \(f(x_n)f(x_{n+1})<0\), suppose \(f(x_{n}) < f(x_{n+1})\) then \(x_{n+1}\) replace by \(x_{n}\).
 (i)
 III.
If \(f'(x_{n1}) \approx 0\) then interchange \(x_{n1}\) and \(x_{n+1}\).
 IV.
Repeat steps I, II and III until we get required approximate solution.
The implementation of the proposed algorithm in Matlab is also discussed (See, Additional file 1). This algorithm would help to implement the manual calculations in commercial packages such as Maple, Mathematica, SCILab, Singular,etc.
Order of convergence
Theorem 2
Let \(\beta\) be a exact root of a continuous function f(x) and [a, b] be a sufficiently small neighbourhood of \(\beta\). Then the sequence \(\{x_n\}\) generated by the iterative formula (6) is at least quadratically convergent.
Proof
Results
This section provides three examples to discuss the algorithm presented in “Main text” section and comparisons are taken into account to conform that the algorithm is more efficient than other existing methods. Moreover, it is also observed that the proposed method takes less time in comparison of Regula Falsi method but takes more convergence time in comparison of Newton–Raphson method.
Example 3
Comparison between different methods with errors for Example 3
Ite no.  BM approx. root  % deviation  RF approx. root  % deviation  N–R approx. root  % deviation  PM approx. root  % deviation 

1  0.5000  –  0.3147  –  1.0000  –  0.6573  – 
2  0.7500  100  0.4467  100  0.6531  100  0.4886  100 
3  0.6250  33.33  0.4940  29.56  0.5313  53.12  0.5165  34.52 
4  0.5625  20.00  0.5099  09.57  0.5179  22.91  0.5176  05.40 
5  0.5313  11.11  0.5152  03.12  0.5178  02.59  0.5177  00.23 
6  0.5156  05.88  0.5169  01.02  0.5178  00.03  0.5177  00.01 
7  0.5234  03.03  0.5177  00.10  0.5178  00.00  0.5178  00.00 
\(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  
14  \(\vdots\)  \(\vdots\)  0.5178  00.00  
\(\vdots\)  \(\vdots\)  \(\vdots\)  
22  0.5178  00.00 
It is found that bisection and RegulaFalsi methods converged after 22 and 14 iterations respectively (Table 1), while the proposed algorithm converged in 7th iteration. Thus proposed method is efficient over bisection and RegulaFalsi methods. It is also clear that both of the methods (proposed and Newton–Raphson) are converged in 7th iteration. But one of the main advantage of proposed method is that it gives guaranteed result over the Newton–Raphson method. Therefore, the proposed method is not only reduce the computational affords but also provide the guaranteed result for solving the real life problem.
The error estimation after the 3rd iteration (Table 1), show that the proposed method having \(5.4\%\) error in comparison to Bisection (\(20\%\)), RegulaFalsi (\(9.57\%\)) and Newton–Raphson (\(22.91\%\)) methods. Thus, the proposed method is also efficient for error estimation.
Most of the real life problems take too much computational time for convergence because of the complex flow physics and higher degree polynomial equations. Therefore, the proposed method is useful also for solving such the real life problem.
Example 4
Comparison between different methods with errors for Example 4
Ite no.  BM approx. root  % deviation  RF approx. root  % deviation  N–R approx. root  % deviation  PM approx. root  % deviation 

1  2.0000  –  2.6767  –  3.7631  –  3.2199  – 
2  2.5000  50.00  2.7392  62.64  2.8067  73.43  2.6935  68.94 
3  2.7500  20.00  2.7406  02.28  2.7410  34.08  2.7398  19.54 
4  2.6250  09.09  2.7406  00.05  2.7406  02.39  2.7406  01.69 
5  2.6875  04.76  2.7406  0.001  2.7406  00.01  2.7406  00.03 
6  2.7188  02.33  2.7406  00.00  2.7406  00.00  2.7406  00.00 
\(\vdots\)  \(\vdots\)  \(\vdots\)  
21  2.7406  00.00 
Example 5
Consider the real root of \(f(x) = 1x^2\) in the interval (0, 2).
Numerical results for Example 5
Iter no.  a  Approx. root  b  % error 

1  2.0000  0.8750  0.0000  – 
2  0.8750  0.9827  2.0000  100.0 
3  0.9827  0.9972  2.0000  10.96 
4  0.9972  0.9995  2.0000  01.45 
5  0.9995  0.9999  2.0000  00.24 
6  0.9999  1.0000  2.0000  00.04 
7  1.0000  1.0000  2.0000  00.01 
8  1.0000  1.0000  2.0000  00.00 
Limitations
The order of the presented method is quadratic. Even though there are methods with a higher order of convergence, the proposed method is simple and efficient quadratic convergence method.
Notes
Authors' contributions
ST is involved in creating the proposed algorithm and implementation of the algorithm in Matlab. VKS is participated in modifications, corrections and writing of the manuscript. Both authors read and approved the final manuscript.
Acknowledgements
The author is thankful to the editor for providing valuable inputs to improve the present format of manuscript.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Consent to publish
Not applicable.
Ethics approval and consent to participate
Not applicable.
Funding
Not applicable.
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Supplementary material
References
 1.Sharma JR, Goyal RK. Fourth order derivative methods for solving nonlinear equations. Int J Comput Math. 2006;83(1):101–6.CrossRefGoogle Scholar
 2.Gottlieb RG, Thompson BF. Bisected direct quadratic RegulaFalsi. Appl Math Sci. 2010;4(15):709–18.Google Scholar
 3.Wu W, Wu H. On a class of quadratic convergence iteration formula without derivatives. Appl Math Comput. 2000;107:77–80.Google Scholar
 4.Wu X, Shen Z, Jianlin X. An improved Regula Falsi method with quadratic convergence of both diameter and point for enclosing simple zeros of nonlinear equations. Appl Math Comput. 2003;144:381–8.Google Scholar
 5.Mamta VK, Kukreja VK, Singh S. On some thirdorder iterative methods for solving nonlinear equations. Appl Math Comput. 2005;171:272–80.Google Scholar
 6.Noor MA, Ahmad F. Numerical comparison of iterative methods for solving nonlinear equations. Appl Math Comput. 2006;180:167–72.Google Scholar
 7.Noor MA, Noor KI, Khan WA, Ahmad F. On iterative methods for nonlinear equations. Appl Math Comput. 2006;183:128–33.Google Scholar
 8.Thota S, Srivastav VK. Interpolation based hybrid algorithm for computing real root of nonlinear transcendental functions. Int J Math Comput Res. 2014;2(11):729–35.Google Scholar
 9.Hussain S, Srivastav VK, Thota S. Assessment of interpolation methods for solving the real life problem. Int J Math Sci Appl. 2015;5(1):91–5.Google Scholar
 10.Chen J, Li W. An exponential Regula Falsi method for solving nonlinear equations. Numer Algorithms. 2006;41:327–38.CrossRefGoogle Scholar
 11.Chen J, Li W. An improved exponential Regula Falsi methods with quadratic convergence of both diameter and point for solving nonlinear equations. Appl Numer Math. 2007;57:80–8.CrossRefGoogle Scholar
 12.Johan, Ronald. The Newton–Raphson method. Int J Math Educ Sci Technol. 1995;26(2):177–93.CrossRefGoogle Scholar
 13.Wu XY, Fu D. New highorder convergence iteration methods without employing derivatives for solving nonlinear equations. Comput Math Appl. 2001;41:489–95.CrossRefGoogle Scholar
 14.Frontini M, Sormani E. Modified Newton’s method with thirdorder convergence and multiple roots. J Comput Appl Math. 2003;156:345–54.CrossRefGoogle Scholar
 15.Dowell M, Jarratt P. A modified RegulaFalsi method for computing the real root of an equation. BIT Numer Math. 1971;11:168–74.CrossRefGoogle Scholar
 16.Gottlieb RG, Thompson BF. Bisected direct quadratic RegulaFalsi. Appl Math Sci. 2010;4(15):709–18.Google Scholar
 17.Wu XY, Xia JL, Shao R. Quadratically convergent multiple roots finding method without derivatives. Comput Math Appl. 2001;42:115–9.CrossRefGoogle Scholar
 18.Amata S, Busquiera S, Gutierrezb JM. Geometric constructions of iterative functions to solve nonlinear equations. J Comput Appl Math. 2003;157:197–205.CrossRefGoogle Scholar
 19.Kou J, Li Y, Wang X. A variant of superHalley method with accelerated fourthorder convergence. Appl Math Comput. 2007;186:535–9.Google Scholar
 20.Mamta VK, Kukreja VK, Singh S. On a class of quadratically convergent iteration formulae. Appl Math Comput. 2005;166:633–7.Google Scholar
 21.Sagraloff M. Computing real roots of real polynomials. 2015. arXiv:1308.4088v2 [cs.SC].
 22.Abbott J. Quadratic interval refinement for real roots. ACM Commun Comput Algebra. 2014;48(1):3–12.CrossRefGoogle Scholar
 23.Chapra Steven C. Solutions Manual to accompany. Applied numerical methods With MATLAB for engineers and scientists; Pennsylvania: Mc Graw Hill Higher education. ISBN 9780073401065. 2010.Google Scholar
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