A simplified L-curve method as error estimator
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Publication:2303352
DOI10.1553/etna_vol53s217zbMath1475.65024arXiv1908.10140OpenAlexW3003624317MaRDI QIDQ2303352
Publication date: 3 March 2020
Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1908.10140
Related Items (20)
Error estimates for Golub–Kahan bidiagonalization with Tikhonov regularization for ill–posed operator equations ⋮ A gradient-based regularization algorithm for the Cauchy problem in steady-state anisotropic heat conduction ⋮ Weighted tensor Golub-Kahan-Tikhonov-type methods applied to image processing using a t-product ⋮ Golub-Kahan vs. Monte Carlo: a comparison of bidiagonlization and a randomized SVD method for the solution of linear discrete ill-posed problems ⋮ Generalized cross validation for \(\ell^p\)-\(\ell^q\) minimization ⋮ Limited memory restarted \(\ell^p\)-\(\ell^q\) minimization methods using generalized Krylov subspaces ⋮ An Arnoldi-based preconditioner for iterated Tikhonov regularization ⋮ Adaptive cross approximation for Tikhonov regularization in general form ⋮ Some numerical aspects of Arnoldi-Tikhonov regularization ⋮ A numerical comparison of some heuristic stopping rules for nonlinear Landweber iteration ⋮ Range restricted iterative methods for linear discrete ill-posed problems ⋮ Numerical considerations of block GMRES methods when applied to linear discrete ill-posed problems ⋮ Solution of ill-posed problems with Chebfun ⋮ A novel modified TRSVD method for large-scale linear discrete ill-posed problems ⋮ On the choice of regularization matrix for an \(\ell_2\)-\(\ell_q\) minimization method for image restoration ⋮ Tensor Krylov subspace methods with an invertible linear transform product applied to image processing ⋮ Tensor Arnoldi-Tikhonov and GMRES-type methods for ill-posed problems with a t-product structure ⋮ Golub-Kahan bidiagonalization for ill-conditioned tensor equations with applications ⋮ Regularization for the inversion of fibre Bragg grating spectra ⋮ Krylov subspace split Bregman methods
Uses Software
Cites Work
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- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- ErresTools
- Comparison of parameter choices in regularization algorithms in case of different information about noise level
- Comparing parameter choice methods for regularization of ill-posed problems
- On the convergence of the quasioptimality criterion for (iterated) Tikhonov regularization
- Error estimates for linear systems with applications to regularization
- Error estimates for the regularization of least squares problems
- A mathematical procedure for solving the inverse potential problem of electrocardiography. Analysis of the time-space accuracy from in vitro experimental data
- Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
- \(L\)-curve curvature bounds via Lanczos bidiagonalization
- Limitations of the \(L\)-curve method in ill-posed problems
- Old and new parameter choice rules for discrete ill-posed problems
- IR tools: a MATLAB package of iterative regularization methods and large-scale test problems
- A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level
- Convergence analysis of minimization-based noise level-free parameter choice rules for linear ill-posed problems
- Discretization independent convergence rates for noise level-free parameter choice rules for the regularization of ill-conditioned problems
- Heuristic parameter selection based on functional minimization: Optimality and model function approach
- Morozov’s Principle for the Augmented Lagrangian Method Applied to Linear Inverse Problems
- The convergence of a new heuristic parameter selection criterion for general regularization methods
- ON NUMERICAL REALIZATION OF QUASIOPTIMAL PARAMETER CHOICES IN (ITERATED) TIKHONOV AND LAVRENTIEV REGULARIZATION
- Remarks on choosing a regularization parameter using the quasi-optimality and ratio criterion
- Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
- The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
- The quasi-optimality criterion in the linear functional strategy
- Non-convergence of the L-curve regularization parameter selection method
- A Regularization Parameter in Discrete Ill-Posed Problems
- L- and V-curves for optimal smoothing
- Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-Curve
- A numerical study of heuristic parameter choice rules for total variation regularization
- On the quasioptimal regularization parameter choices for solving ill-posed problems
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