Iteratively regularized Gauss-Newton type methods for approximating quasi-solutions of irregular nonlinear operator equations in Hilbert space with an application to COVID-19 epidemic dynamics

DOI10.1016/J.AMC.2022.127312OpenAlexW4281744297WikidataQ114210829 ScholiaQ114210829MaRDI QIDQ2152711

A. V. Semenova, Mikhail Yu. Kokurin, Mikhail M. Kokurin

Publication date: 11 July 2022

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2022.127312

zbMATH Keywords

parameter identificationnonlinear equationclosed rangeiterative regularizationepidemic dynamicsaccuracy estimate

Mathematics Subject Classification ID

Epidemiology (92D30) Nonlinear ill-posed problems (47J06) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (1)

Mathematical modeling to investigate the influence of vaccination and booster doses on the spread of Omicron

Cites Work

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• Accuracy estimates of Gauss-Newton-type iterative regularization methods for nonlinear equations with operators having normally solvable derivative at the solution
• Regularization methods in Banach spaces.
• Iteratively regularized methods for irregular nonlinear operator equations with a normally solvable derivative at the solution
• Iterative regularization methods for nonlinear ill-posed problems
• Regularization algorithms for ill-posed problems
• A multi-grid method with a priori and a posteriori level choice for the regularization of nonlinear ill-posed problems
• Finite-dimensional iteratively regularized processes with an a posteriori stopping for solving irregular nonlinear operator equations
• Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model
• Optimal regularization of ill-posed normally solvable operator equations
• Finite dimensional iteratively regularized Gauss–Newton type methods and application to an inverse problem of the wave tomography with incomplete data range
• Computational Modeling of the COVID-19 Disease
• On iteratively regularized predictor–corrector algorithm for parameter identification ^*
• Convergence of projected iterative regularization methods for nonlinear problems with smooth solutions

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