Regularization methods for uniformly rank-deficient nonlinear least-squares problems

DOI10.1007/s10957-005-6389-0zbMath1152.65437OpenAlexW2035764960MaRDI QIDQ948627

Mårten Gulliksson, Jerry Eriksson, Inge Söderkvist, Per-Åke Wedin

Publication date: 17 October 2008

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-005-6389-0

zbMATH Keywords

rank-deficient matricesTikhonov regularizationill-posed problemsstabilization methodsGauss-Newton methodnonlinear least squaresminimum norm problemstruncation problems

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Quadratic programming (90C20)

Related Items (4)

Numerical solution of separable nonlinear equations with a singular matrix at the solution ⋮ Newton-MR: inexact Newton method with minimum residual sub-problem solver ⋮ The minimal-norm Gauss-Newton method and some of its regularized variants ⋮ A doubly relaxed minimal-norm Gauss-Newton method for underdetermined nonlinear least-squares problems

Uses Software

• ELSUNC_f90

Cites Work

• Unnamed Item
• The Use and Properties of Tikhonov Filter Matrices
• Tensor Methods for Nonlinear Equations
• Algorithms for the Solution of the Nonlinear Least-Squares Problem
• Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm
• Local results for the Gauss-Newton method on constrained rank-deficient nonlinear least squares

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