On iteratively regularized predictor–corrector algorithm for parameter identification *

DOI10.1088/1361-6420/abc530zbMath1461.65112OpenAlexW3095253415MaRDI QIDQ5148411

Anatoly B. Bakushinsky, Alexandra B. Smirnova

Publication date: 4 February 2021

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/abc530

zbMATH Keywords

iterative regularizationepidemiologyconstrained least squares in Hilbert spacesstable parameter estimation

Mathematics Subject Classification ID

Epidemiology (92D30) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (2)

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 ⋮ On stable parameter estimation and short-term forecasting with quantified uncertainty with application to COVID-19 transmission

Cites Work

• Unnamed Item
• Unnamed Item
• Iterative regularization methods for nonlinear ill-posed problems
• A theory of growth
• Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. With a comment by J. A. Hartigan and a rejoinder by the authors
• Analysis of logistic growth models
• Ill-posed problems with a priori information
• Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions
• Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model
• Optimal convergence rates for inexact Newton regularization with CG as inner iteration
• Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss-Newton algorithm
• Regularization Based on All-At-Once Formulations for Inverse Problems
• On convergence rates for iteratively regularized procedures with linear penalty terms
• Regularizing properties of a truncated newton-cg algorithm for nonlinear inverse problems
• Minimization Based Formulations of Inverse Problems and Their Regularization
• Analysis of the iteratively regularized Gauss–Newton method under a heuristic rule
• Convergence analysis of (statistical) inverse problems under conditional stability estimates
• Goal oriented adaptivity in the IRGNM for parameter identification in PDEs: II. all-at-once formulations
• Convergence and application of a modified iteratively regularized Gauss–Newton algorithm

This page was built for publication: On iteratively regularized predictor–corrector algorithm for parameter identification ^*