A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction
type: Item
instance of: scholarly article
title: A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction
zbMATH Open document ID: 1398.65137
author: Douglas S. Gonçalves
author: S. A. Santos
published in: Bulletin of Computational Applied Mathematics
publication date: 2018-08-07T00:00:00Z
full work available at URL: http://drive.google.com/open
review text: Summary: This work addresses a spectral correction for the Gauss-Newton model in the solution of nonlinear least-squares problems within a globally convergent algorithmic framework. The nonmonotone line search of Zhang and Hager is the chosen globalization tool. We show that the search directions obtained from the corrected Gauss-Newton model satisfy the conditions that ensure the global convergence under such a line search scheme. A numerical study assesses the impact of using the spectral correction for solving two sets of test problems from the literature.
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 90C53
zbMATH DE Number: 6914257
zbMATH Keywords: nonlinear least squares
zbMATH Keywords: spectral parameter
zbMATH Keywords: Gauss-Newton method
zbMATH Keywords: global convergence
zbMATH Keywords: numerical tests
describes a project that uses: minpack
describes a project that uses: Schittkowski
describes a project that uses: GQTPAR
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6914257