A model function method in regularized total least squares
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Publication:3058423
DOI10.1080/00036811.2010.492502zbMath1201.65096OpenAlexW1987436040WikidataQ58181692 ScholiaQ58181692MaRDI QIDQ3058423
Shuai Lu, Sergei V. Pereverzyev, Ulrich Tautenhahn
Publication date: 22 November 2010
Published in: Applicable Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00036811.2010.492502
Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Numerical methods for ill-posed problems for initial value and initial-boundary value problems involving PDEs (65M30)
Related Items (4)
Multi-penalty regularization in learning theory ⋮ Multi-task learning via linear functional strategy ⋮ On the generalized discrepancy principle for Tikhonov regularization in Hilbert scales ⋮ Tikhonov regularization for polynomial approximation problems in Gauss quadrature points
Uses Software
Cites Work
- An Analysis of the Total Least Squares Problem
- Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
- Multi-parameter regularization techniques for ill-conditioned linear systems
- Regularized total least squares based on quadratic eigenvalue problem solvers
- Efficient determination of multiple regularization parameters in a generalized L-curve framework
- Optimal regularization with two interdependent regularization parameters
- Regularized Total Least Squares: Computational Aspects and Error Bounds
- On the Choice of the Regularization Parameter in Nonlinear Inverse Problems
- Iterative choices of regularization parameters in linear inverse problems
- An improved model function method for choosing regularization parameters in linear inverse problems
- Tikhonov Regularization and Total Least Squares
- Adaptive estimation for inverse problems with noisy operators
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