scholarly journals On a Bi-Parametric Family of Fourth Order Composite Newton–Jarratt Methods for Nonlinear Systems

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 492 ◽  
Author(s):  
Janak Raj Sharma ◽  
Deepak Kumar ◽  
Ioannis K. Argyros ◽  
Ángel Alberto Magreñán

We present a new two-parameter family of fourth-order iterative methods for solving systems of nonlinear equations. The scheme is composed of two Newton–Jarratt steps and requires the evaluation of one function and two first derivatives in each iteration. Convergence including the order of convergence, the radius of convergence, and error bounds is presented. Theoretical results are verified through numerical experimentation. Stability of the proposed class is analyzed and presented by means of using new dynamics tool, namely, the convergence plane. Performance is exhibited by implementing the methods on nonlinear systems of equations, including those resulting from the discretization of the boundary value problem. In addition, numerical comparisons are made with the existing techniques of the same order. Results show the better performance of the proposed techniques than the existing ones.

2019 ◽  
Vol 24 (1) ◽  
pp. 105-126 ◽  
Author(s):  
Janak Raj Sharma ◽  
Deepak Kumar

Based on a two-step Newton-like scheme, we propose a three-step scheme of convergence order p+2 (p >=3) for solving systems of nonlinear equations. Furthermore, on the basis of this scheme a generalized k+2-step scheme with increasing convergence order p+2k is presented. Local convergence analysis including radius of convergence and uniqueness results of the methods is presented. Computational efficiency in the general form is discussed. Theoretical results are verified through numerical experimentation. Finally, the performance is demonstrated by the application of the methods on some nonlinear systems of equations.


Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 937 ◽  
Author(s):  
Hessah Faihan Alqahtani ◽  
Ramandeep Behl ◽  
Munish Kansal

We present a three-step family of iterative methods to solve systems of nonlinear equations. This family is a generalization of the well-known fourth-order King’s family to the multidimensional case. The convergence analysis of the methods is provided under mild conditions. The analytical discussion of the work is upheld by performing numerical experiments on some application oriented problems. Finally, numerical results demonstrate the validity and reliability of the suggested methods.


Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1194 ◽  
Author(s):  
Francisco I. Chicharro ◽  
Alicia Cordero ◽  
Neus Garrido ◽  
Juan R. Torregrosa

A generalized high-order class for approximating the solution of nonlinear systems of equations is introduced. First, from a fourth-order iterative family for solving nonlinear equations, we propose an extension to nonlinear systems of equations holding the same order of convergence but replacing the Jacobian by a divided difference in the weight functions for systems. The proposed GH family of methods is designed from this fourth-order family using both the composition and the weight functions technique. The resulting family has order of convergence 9. The performance of a particular iterative method of both families is analyzed for solving different test systems and also for the Fisher’s problem, showing the good performance of the new methods.


Author(s):  
Francisco Casesnoves

<p>In a previous contribution, the mathematical-computational base of Interior Optimization Method was demonstrated. Electronics applications were performed with numerical optimization data and graphical proofs. In this evoluted-improved paper a series of electronics applications of Interior Optimization in superconductors BCS algorithms/theory are shown. In addition, mathematical developments of Interior Optimization Methods related to systems of Nonlinear Equations are proven. The nonlinear multiobjective optimization problem constitutes a difficult task to find/determine a global minimum, approximated-global minimum, or a convenient local minimum whith/without constraints. Nonlinear systems of equations principles set the base in the previous article for further development of Interior Optimization and Interior-Graphical Optimization [Casesnoves, 2016-7]. From Graphical Optimization 3D optimization stages [Casesnoves, 2016-7], the demonstration that solution of nonlinear systems of equations is not unique in general emerges. Software-engineering and computational simulations are shown with electronics superconductors [several elements, Type 1 superconductors] and electronics physics applications. Extensions to similar applications for materials-tribology models and Biomedical Tribology are explained.</p>


2004 ◽  
Vol 2004 (2) ◽  
pp. 127-136
Author(s):  
Bernard Beauzamy

We give a fast method to solve numerically some systems of nonlinear equations. This method applies basically to all systems which can be put in the formU∘V(X)=Y, whereUandVare two possibly nonlinear operators. It uses a modification of Newton's algorithm, in the sense that one projects alternatively onto two subsets. But, here, these subsets are not subspaces any more, but manifolds in a Euclidean space.


Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1221 ◽  
Author(s):  
Raudys R. Capdevila ◽  
Alicia Cordero ◽  
Juan R. Torregrosa

In this work, a new class of iterative methods for solving nonlinear equations is presented and also its extension for nonlinear systems of equations. This family is developed by using a scalar and matrix weight function procedure, respectively, getting sixth-order of convergence in both cases. Several numerical examples are given to illustrate the efficiency and performance of the proposed methods.


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