A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems
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1985-02
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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
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Technical report
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Dennis, J.E. Jr., Songbai, Sheng and Vu, Phuong Ahn. "A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems." (1985) https://hdl.handle.net/1911/101578.