TY - JOUR
T1 - A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems
AU - Jr , Dennis J. E.
AU - Sheng , Song-Bai
AU - Vu , Phuong Anh
JO - Journal of Computational Mathematics
VL - 4
SP - 355
EP - 374
PY - 1988
DA - 1988/06
SN - 6
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jcm/9524.html
KW - 
AB - <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>