A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction
Journal Title: Bulletin of Computational Applied Mathematics (Bull CompAMa) - Year 2016, Vol 4, Issue 2
Abstract
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.
Authors and Affiliations
Douglas S. Gonçalves, Sandra A. Santos
Keywords
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