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  5. An Unconstrained Optimization Algorithm Which Uses Function and Gradient Values

An Unconstrained Optimization Algorithm Which Uses Function and Gradient Values

File(s)
75-246.ps (439.26 KB)
75-246.pdf (1.15 MB)
Permanent Link(s)
https://hdl.handle.net/1813/6392
Collections
Computer Science Technical Reports
Author
Dennis, John E., Jr.
Mei, Howell Hung-Wei
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.

Date Issued
1975-06
Publisher
Cornell University
Keywords
computer science
•
technical report
Previously Published as
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR75-246
Type
technical report

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