A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems

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dc.contributor.authorDennis, J.E. Jr.en_US
dc.contributor.authorSongbai, Shengen_US
dc.contributor.authorVu, Phuong Ahnen_US
dc.date.accessioned2018-06-18T17:24:30Zen_US
dc.date.available2018-06-18T17:24:30Zen_US
dc.date.issued1985-02en_US
dc.date.noteFebruary 1985en_US
dc.description.abstractIn 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 problemsen_US
dc.format.extent27 ppen_US
dc.identifier.citationDennis, J.E. Jr., Songbai, Sheng and Vu, Phuong Ahn. "A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems." (1985) <a href="https://hdl.handle.net/1911/101578">https://hdl.handle.net/1911/101578</a>.en_US
dc.identifier.digitalTR85-01en_US
dc.identifier.urihttps://hdl.handle.net/1911/101578en_US
dc.language.isoengen_US
dc.titleA Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problemsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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