Year: 1988
Author: Dennis J. E. Jr, Song-Bai Sheng, Phuong Anh Vu
Journal of Computational Mathematics, Vol. 6 (1988), Iss. 4 : pp. 355–374
Abstract
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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/1988-JCM-9524
Journal of Computational Mathematics, Vol. 6 (1988), Iss. 4 : pp. 355–374
Published online: 1988-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 20