abstract 22


22
A globally convergent method for nonlinear least-squares  problems based on the Gauss-Newton model with spectral correction
Douglas S. Gonçalves, Sandra A. Santos

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.

Keywords: Nonlinear least squares, spectral parameter, Gauss-Newton method, global convergence, numerical tests

Cite this paper:
Gon\c{c}alves D.S., Santos S.A., A globally convergent method for nonlinear least-squares problems based on the Gauss-Newton model with spectral correction.
Bull. Comput. Appl. Math. (Bull CompAMa),
Vol. 4, No. 2, Jul-Dec, pp.7-26, 2016