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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/6392
Title: An Unconstrained Optimization Algorithm Which Uses Function and Gradient Values
Authors: Dennis, John E., Jr.
Mei, Howell Hung-Wei
Keywords: computer science
technical report
Issue Date: Jun-1975
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR75-246
Abstract: A new method for unconstrained optimization is presented. It consists of a modification of Powell's 1970 dogleg strategy with the approximate Hessian given by Davidson's 1975 updating scheme which uses the projections of $\triangle x$ and $\triangle g$ in updating H and G and optimizes the condition number of $H^{-1}H_{+}$. This new algorithm performs well without Powell's special iterations and singularity safeguards. Only symmetric and positive definite updates to the Hessian are used.
URI: http://hdl.handle.net/1813/6392
Appears in Collections:Computer Science Technical Reports

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