Treffer: Applying faster algorithm for obtaining convergence, stability, and data dependence results with application to functional-integral equations
Title:
Applying faster algorithm for obtaining convergence, stability, and data dependence results with application to functional-integral equations
Authors:
Source:
AIMS Mathematics, Vol 7, Iss 10, Pp 19026-19056 (2022)
Publisher Information:
American Institute of Mathematical Sciences (AIMS), 2022.
Publication Year:
2022
Subject Terms:
Inverse Problems in Mathematical Physics and Imaging, Economics, data-dependent result, Fixed-Point Problems, Mathematical analysis, 01 natural sciences, Machine learning, functional integral equation, QA1-939, FOS: Mathematics, numerical experiment, Iterative Algorithms, 0101 mathematics, Stability (learning theory), Mathematical Physics, Anomalous Diffusion Modeling and Analysis, Integral equation, Economic growth, Banach space, Fredholm integral equation, Iterative Algorithms for Nonlinear Operators and Optimization, Applied mathematics, Computer science, convergence result, ξ-stable, Algorithm, fixed point, Computational Theory and Mathematics, Modeling and Simulation, Computer Science, Physical Sciences, Convergence (economics), Mathematics
Document Type:
Fachzeitschrift
Article<br />Other literature type
ISSN:
2473-6988
DOI:
10.3934/math.20221046
DOI:
10.60692/0d660-42z37
DOI:
10.60692/1y7wk-ry366
Accession Number:
edsair.doi.dedup.....32f2f3f32e1cb8422e8f7624126e5a7f
Database:
OpenAIRE
Weitere Informationen
The goal of this manuscript is to create a new faster iterative algorithm than the previous writing's sober algorithms. In the setting of Banach spaces, this algorithm is used to analyze convergence, stability, and data-dependence results. Basic numerical examples are also provided to highlight the behavior and effectiveness of our approach. Ultimately, the proposed approach is used to solve the functional Volterra-Fredholm integral problem as an application.