TY - JOUR
T1 - A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems
AU - J. E. Dennis, Jr, Song-bai Sheng & Anh Vu Phuong
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 - 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.