Analysis of the iteratively regularized Gauss–Newton method under a heuristic rule
From MaRDI portal
Publication:4634752
DOI10.1088/1361-6420/aaa0fbOpenAlexW2772987450MaRDI QIDQ4634752
Publication date: 11 April 2018
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1088/1361-6420/aaa0fb
convergencea posteriori error estimatesnonlinear inverse problemsheuristic selection rulethe iteratively regularized Gauss-Newton method
Related Items (12)
A Discretizing Levenberg-Marquardt Scheme for Solving Nonlinear Ill-Posed Integral Equations ⋮ A numerical Haar wavelet-finite difference hybrid method and its convergence for nonlinear hyperbolic partial differential equation ⋮ Iterative Lavrentiev regularization method under a heuristic rule for nonlinear ill-posed operator equations ⋮ Heuristic rule for inexact Newton-Landweber iteration with convex penalty terms of nonlinear: ill-posed problems ⋮ Landweber-type method with uniformly convex constraints under conditional stability assumptions ⋮ Hanke-Raus rule for Landweber iteration in Banach spaces ⋮ Analysis of a heuristic rule for the IRGNM in Banach spaces with convex regularization terms ⋮ Heuristic parameter choice rule for solving linear ill-posed integral equations in finite dimensional space ⋮ On iteratively regularized predictor–corrector algorithm for parameter identification * ⋮ A projected Bouligand–Landweber iteration for non-smooth ill-posed problems ⋮ Analysis of a generalized regularized Gauss–Newton method under heuristic rule in Banach spaces ⋮ On stable parameter estimation and short-term forecasting with quantified uncertainty with application to COVID-19 transmission
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Iterative solution of large sparse systems of equations
- Iterative regularization methods for nonlinear ill-posed problems
- On the convergence of the quasioptimality criterion for (iterated) Tikhonov regularization
- On the discrepancy principle for some Newton type methods for solving nonlinear inverse problems
- A posteriori parameter choice strategies for some Newton type methods for the regularization of nonlinear ill-posed problems
- The problem of the convergence of the iteratively regularized Gauss- Newton method
- On the iteratively regularized Gauss-Newton method in Banach spaces with applications to parameter identification problems
- Hanke-Raus heuristic rule for variational regularization in Banach spaces
- Further convergence results on the general iteratively regularized Gauss-Newton methods under the discrepancy principle
- A General Convergence Analysis of Some Newton-Type Methods for Nonlinear Inverse Problems
- A Lepskij-type stopping rule for regularized Newton methods
- Convergence rates for Tikhonov regularisation of non-linear ill-posed problems
- A convergence analysis of the iteratively regularized Gauss–Newton method under the Lipschitz condition
- Remarks on choosing a regularization parameter using the quasi-optimality and ratio criterion
- Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy
- A convergence analysis of iterative methods for the solution of nonlinear ill-posed problems under affinely invariant conditions
- The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
- On autoconvolution and regularization
- On convergence rates for the iteratively regularized Gauss-newton method
- Some Newton-type methods for the regularization of nonlinear ill-posed problems
- Logarithmic convergence rates of the iteratively regularized Gauss - Newton method for an inverse potential and an inverse scattering problem
- Optimality for ill-posed problems under general source conditions
- On the iteratively regularized Gauss-Newton method for solving nonlinear ill-posed problems
- A General Heuristic for Choosing the Regularization Parameter in Ill-Posed Problems
- About a deficit in low-order convergence rates on the example of autoconvolution
- How general are general source conditions?
This page was built for publication: Analysis of the iteratively regularized Gauss–Newton method under a heuristic rule
