2018
DOI: 10.1016/j.cam.2018.02.028
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On the local convergence study for an efficient k-step iterative method

Abstract: This paper is devoted to a family of Newton-like methods with frozen derivatives used to approximate a locally unique solution of an equation. The methods have high order of convergence but only using first order derivatives. Moreover only one LU decomposition is required in each iteration. In particular, the methods are real alternatives to the classical Newton method. We present a local convergence analysis based on hypotheses only on the first derivative. This type of local results were usually proved based… Show more

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Cited by 24 publications
(7 citation statements)
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“…6,7 This technique can be used to improve the applicability of other methods, since it is so general. 1,[3][4][5]12,13…”
Section: Ta B L E 9 Radius Of Convergencementioning
confidence: 99%
See 1 more Smart Citation
“…6,7 This technique can be used to improve the applicability of other methods, since it is so general. 1,[3][4][5]12,13…”
Section: Ta B L E 9 Radius Of Convergencementioning
confidence: 99%
“…In computational sciences and other disciplines, applications appear like Equation (1) (in general) using mathematical modeling. [1][2][3] It is rarely attainable to find solutions for these equations in closed form. Consequently, most methods to obtain the solution are iterative.…”
Section: Introductionmentioning
confidence: 99%
“…In Table 3, the radius R is provided. Example 4 [28] Consider the nonlinear integral equation given by . The value of R is presented in Table 4.…”
Section: Numerical Examplesmentioning
confidence: 99%
“…Also, important findings on the local analysis of fourth, fifth, and sixth order schemes are presented in [22][23][24][25][26][27]. More results on the local analysis of iterative procedures have been established in the literature [28][29][30][31][32][33][34][35]. In this paper, we expand the convergence domain as well as the applicability of a fifth-order scheme using Hölder condition on Q' only.…”
Section: Introductionmentioning
confidence: 97%
“…What makes this research study novel is that no additional information is used because the constants are specializations that we can see in References 1‐25. If k=1, then the aforementioned improvements hold for the corresponding to () and () algorithms.…”
Section: Introductionmentioning
confidence: 99%