Regularized Total Least Squares: Computational Aspects and Error Bounds

DOI10.1137/070709086zbMath1198.65094OpenAlexW2016852860MaRDI QIDQ3584138

Shuai Lu, Ulrich Tautenhahn, Sergei V. Pereverzyev

Publication date: 19 August 2010

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/1d8e2b8b9a74140b2d5ea2cf4291d71da698316e

zbMATH Keywords

inverse problemsnumerical experimentsHilbert spaceserror boundTikhonov regularizationill-posed problemsleast squares methodnoisy operatornoisy right-hand side

Mathematics Subject Classification ID

Numerical solutions to equations with linear operators (65J10) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Linear operators and ill-posed problems, regularization (47A52) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (14)

Perturbation analysis and condition numbers for the Tikhonov regularization of total least squares problem and their statistical estimation ⋮ Semi-heuristic parameter choice rules for Tikhonov regularisation with operator perturbations ⋮ On the generalized discrepancy principle for Tikhonov regularization in Hilbert scales ⋮ Two projection methods for regularized total least squares approximation ⋮ Efficient determination of the hyperparameter in regularized total least squares problems ⋮ Tikhonov regularization for polynomial approximation problems in Gauss quadrature points ⋮ Efficient local search procedures for quadratic fractional programming problems ⋮ Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions ⋮ A model function method in regularized total least squares ⋮ Improving the robustness of recursive consequent parameters learning in evolving neuro-fuzzy systems ⋮ A survey on variational characterizations for nonlinear eigenvalue problems ⋮ Solving inverse problems using data-driven models ⋮ Total least squares problems on infinite dimensional spaces ⋮ Simple and efficient determination of the Tikhonov regularization parameter chosen by the generalized discrepancy principle for discrete ill-posed problems

Uses Software

• Regularization tools

This page was built for publication: Regularized Total Least Squares: Computational Aspects and Error Bounds