Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications

This research presents a new form of iterative technique for Garcia-Falset mapping that outperforms previous iterative methods for contraction mappings. We illustrate this fact through comparison and present the findings graphically. The research also investigates convergence of the new iteration in...

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Main Authors: Quanita Kiran, Shaista Begum
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Axioms
Subjects:
Online Access:https://www.mdpi.com/2075-1680/14/1/71
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author Quanita Kiran
Shaista Begum
author_facet Quanita Kiran
Shaista Begum
author_sort Quanita Kiran
collection DOAJ
description This research presents a new form of iterative technique for Garcia-Falset mapping that outperforms previous iterative methods for contraction mappings. We illustrate this fact through comparison and present the findings graphically. The research also investigates convergence of the new iteration in uniformly convex Banach space and explores its stability. To further support our findings, we present its working on to a BV problem and to a delay DE. Finally, we propose a design of an implicit neural network that can be considered as an extension of a traditional feed forward network.
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institution Kabale University
issn 2075-1680
language English
publishDate 2025-01-01
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record_format Article
series Axioms
spelling doaj-art-c5fbc3cf6154469fb43d4d1bc148d3f32025-01-24T13:22:20ZengMDPI AGAxioms2075-16802025-01-011417110.3390/axioms14010071Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various ApplicationsQuanita Kiran0Shaista Begum1School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Sector H-12, Islamabad 44000, PakistanSchool of Natural Sciences, National University of Sciences and Technology, Sector H-12, Islamabad 44000, PakistanThis research presents a new form of iterative technique for Garcia-Falset mapping that outperforms previous iterative methods for contraction mappings. We illustrate this fact through comparison and present the findings graphically. The research also investigates convergence of the new iteration in uniformly convex Banach space and explores its stability. To further support our findings, we present its working on to a BV problem and to a delay DE. Finally, we propose a design of an implicit neural network that can be considered as an extension of a traditional feed forward network.https://www.mdpi.com/2075-1680/14/1/71Garcia-Falsetstabilityiterative methodsimplicit neural networkdelay differential equationsboundary value problems
spellingShingle Quanita Kiran
Shaista Begum
Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
Axioms
Garcia-Falset
stability
iterative methods
implicit neural network
delay differential equations
boundary value problems
title Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
title_full Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
title_fullStr Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
title_full_unstemmed Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
title_short Four-Step <i>T</i>-Stable Generalized Iterative Technique with Improved Convergence and Various Applications
title_sort four step i t i stable generalized iterative technique with improved convergence and various applications
topic Garcia-Falset
stability
iterative methods
implicit neural network
delay differential equations
boundary value problems
url https://www.mdpi.com/2075-1680/14/1/71
work_keys_str_mv AT quanitakiran fourstepitistablegeneralizediterativetechniquewithimprovedconvergenceandvariousapplications
AT shaistabegum fourstepitistablegeneralizediterativetechniquewithimprovedconvergenceandvariousapplications