@Article{JCM-6-355,
author = {Jr , Dennis J. E.Sheng , Song-Bai and Vu , Phuong Anh},
title = {A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems},
journal = {Journal of Computational Mathematics},
year = {1988},
volume = {6},
number = {4},
pages = {355--374},
abstract = {<p style="text-align: justify;">In this paper, we develop, analyze, and test a new algorithm for nonlinear least-squares problems. The algorithm uses a BFGS update of the Gauss-Newton Hessian when some heuristics indicate that the Gauss-Newton method may not make a good step. Some important elements are that the secant or quasi-Newton equations considered are not the obvious ones, and the method does not build up a Hessian approximation over several steps. The algorithm can be implemented easily as a modification of any Gauss-Newton code, and it seems to be useful for large residual problems.</p>},
issn = {1991-7139},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jcm/9524.html}
}