Volume 6, Issue 4
A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems

J. E. Dennis ,  Jr ,  Song-bai Sheng and Anh Vu Phuong

J. Comp. Math., 6 (1988), pp. 355-374

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  • 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.

  • History

Published online: 1988-06

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