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Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations
Algorithms, Volume: 13, Issue: 4, Start page: 78
Swansea University Authors:
Ankush Aggarwal , Sanjay Pant
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DOI (Published version): 10.3390/a13040078
Abstract
Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is Newton’s method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In this short note, we...
Published in: | Algorithms |
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ISSN: | 1999-4893 |
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MDPI AG
2020
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URI: | https://cronfa.swan.ac.uk/Record/cronfa53886 |
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2020-10-22T13:35:11.5296806 v2 53886 2020-03-30 Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations 33985d0c2586398180c197dc170d7d19 0000-0002-1755-8807 Ankush Aggarwal Ankush Aggarwal true false 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2020-03-30 Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is Newton’s method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In this short note, we seek to enlarge the basin of attraction of the classical Newton’s method. The key idea is to develop a relatively simple multiplicative transform of the original equations, which leads to a reduction in nonlinearity, thereby alleviating the limitation of Newton’s method. Based on this idea, we derive a new class of iterative methods and rediscover Halley’s method as the limit case. We present the application of these methods to several mathematical functions (real, complex, and vector equations). Across all examples, our numerical experiments suggest that the new methods converge for a significantly wider range of initial guesses. For scalar equations, the increase in computational cost per iteration is minimal. For vector functions, more extensive analysis is needed to compare the increase in cost per iteration and the improvement in convergence of specific problems Journal Article Algorithms 13 4 78 MDPI AG 1999-4893 Newton–Raphson method; Newton’s iteration; nonlinear equations; iterative solution; gradient-based methods 29 3 2020 2020-03-29 10.3390/a13040078 COLLEGE NANME COLLEGE CODE Swansea University 2020-10-22T13:35:11.5296806 2020-03-30T15:15:14.8168681 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Ankush Aggarwal 0000-0002-1755-8807 1 Sanjay Pant 0000-0002-2081-308X 2 53886__16975__27535594021e475693b54921ab028dc6.pdf 53886.pdf 2020-03-30T15:18:19.8303471 Output 2544397 application/pdf Version of Record true This is an open access article distributed under the Creative Commons Attribution License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/ |
title |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
spellingShingle |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations Ankush Aggarwal Sanjay Pant |
title_short |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
title_full |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
title_fullStr |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
title_full_unstemmed |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
title_sort |
Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations |
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33985d0c2586398180c197dc170d7d19 43b388e955511a9d1b86b863c2018a9f |
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33985d0c2586398180c197dc170d7d19_***_Ankush Aggarwal 43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant |
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Ankush Aggarwal Sanjay Pant |
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Ankush Aggarwal Sanjay Pant |
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Algorithms |
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Swansea University |
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1999-4893 |
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10.3390/a13040078 |
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MDPI AG |
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description |
Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is Newton’s method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In this short note, we seek to enlarge the basin of attraction of the classical Newton’s method. The key idea is to develop a relatively simple multiplicative transform of the original equations, which leads to a reduction in nonlinearity, thereby alleviating the limitation of Newton’s method. Based on this idea, we derive a new class of iterative methods and rediscover Halley’s method as the limit case. We present the application of these methods to several mathematical functions (real, complex, and vector equations). Across all examples, our numerical experiments suggest that the new methods converge for a significantly wider range of initial guesses. For scalar equations, the increase in computational cost per iteration is minimal. For vector functions, more extensive analysis is needed to compare the increase in cost per iteration and the improvement in convergence of specific problems |
published_date |
2020-03-29T10:04:22Z |
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11.413177 |