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

Simple item page
dc.contributor.authorDennis, J.E. Jr.
dc.contributor.authorSongbai, Sheng
dc.contributor.authorVu, Phuong Ahn
dc.date.accessioned2018-06-18T17:24:30Z
dc.date.available2018-06-18T17:24:30Z
dc.date.issued1985-02
dc.date.noteFebruary 1985
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 problems
dc.format.extent27 pp
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>.
dc.identifier.digitalTR85-01
dc.identifier.urihttps://hdl.handle.net/1911/101578
dc.language.isoeng
dc.titleA Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems
dc.typeTechnical report
dc.type.dcmiText
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TR85-01.pdf
Size:
325 KB
Format:
Adobe Portable Document Format
Download
Collections
CAAM Technical Reports