An efficient Gauss-Newton algorithm for solving regularized total least squares problems
type: Item
instance of: scholarly article
title: An efficient Gauss-Newton algorithm for solving regularized total least squares problems
zbMATH Open document ID: 1490.65073
author: Hossein Zare
author: Masoud Hajarian
published in: Numerical Algorithms
publication date: 2022-03-16T00:00:00Z
Mathematics Subject Classification ID: 65F22
Mathematics Subject Classification ID: 65F20
zbMATH DE Number: 7490865
zbMATH Keywords: total least squares
zbMATH Keywords: ill-posed problems
zbMATH Keywords: regularization
zbMATH Keywords: Gauss-Newton method
Wikidata QID: Q114224297
describes a project that uses: cosereg
describes a project that uses: UTV
describes a project that uses: VanHuffel
describes a project that uses: PicardREG
describes a project that uses: Regularization tools
MaRDI profile type: Publication
full work available at URL: s11075-021-01145-2
OpenAlex ID: W3174295122
cites work: On the Solution of the Tikhonov Regularization of the Total Least Squares Problem
cites work: Finding a Global Optimal Solution for a Quadratically Constrained Fractional Quadratic Problem with Applications to the Regularized Total Least Squares
cites work: Q4868585
cites work: Methods for large scale total least squares problems
cites work: Comparison of a-posteriori parameter choice rules for linear discrete ill-posed problems
cites work: Estimation of the \(L\)-curve via Lanczos bidiagonalization
cites work: L-curve and curvature bounds for Tikhonov regularization
cites work: Evolutionary Algorithms for Solving Multi-Objective Problems
cites work: Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares
cites work: Multi-objective optimization using evolutionary algorithms
cites work: Multicriteria Optimization
cites work: Q4895893
cites work: A Gauss-Newton iteration for total least squares problems
cites work: GCV for Tikhonov regularization by partial SVD
cites work: Regularization by Truncated Total Least Squares
cites work: A constrained eigenvalue problem
cites work: Tikhonov Regularization and Total Least Squares
cites work: An Analysis of the Total Least Squares Problem
cites work: Q4917542
cites work: Q4530421
cites work: Q4905817
cites work: Regularization tools version \(4.0\) for matlab \(7.3\)
cites work: The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
cites work: On a quadratic eigenproblem occurring in regularized total least squares
cites work: A fast algorithm for solving regularized total least squares problems
cites work: Solving regularized total least squares problems based on eigenproblems
cites work: Efficient determination of the hyperparameter in regularized total least squares problems
cites work: Large-scale Tikhonov regularization of total least squares
cites work: Q4868577
cites work: Two projection methods for regularized total least squares approximation
cites work: Fast High-Resolution Image Reconstruction Using Tikhonov Regularization Based Total Least Squares
cites work: Estimation of the Regularization Parameter in Linear Discrete Ill-Posed Problems Using the Picard Parameter
cites work: Bibliography on total least squares and related methods
cites work: Overview of total least-squares methods
cites work: Trade-off analysis approach for interactive nonlinear multiobjective optimization
cites work: Q5491447
cites work: Parameter determination for Tikhonov regularization problems in general form
cites work: The parameter choice rules for weighted Tikhonov regularization scheme
cites work: Old and new parameter choice rules for discrete ill-posed problems
cites work: A new \(L\)-curve for ill-posed problems
cites work: Efficient Algorithms for Solution of Regularized Total Least Squares
cites work: Efficient estimation of regularization parameters via downsampling and the singular value expansion, downsampling regularization parameter estimation
cites work: Regularized total least squares based on quadratic eigenvalue problem solvers
cites work: Practical mathematical optimization. An introduction to basic optimization theory and classical and new gradient-based algorithms.
cites work: Q4001897
cites work: Multiplicative Noise and Blur Removal by Framelet Decomposition and $l_{1}$ -Based L-Curve Method
cites work: Efficiently solving total least squares with Tikhonov identical regularization
cites work: Determination of regularization parameter via solving a multi-objective optimization problem
DOI: 10.1007/S11075-021-01145-2
description: scientific article; zbMATH DE number 7490865