GGOPT: an unconstrained non-linear optimizer
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Authors
Bassingthwaighte, J.B.
Chan, I.S.
Goldstein, A.A.
Russak, I.B.
Subjects
Advisors
Date of Issue
2012
Date
Publisher
Language
Abstract
GGOPT is a derivative-free non-linear optimizer for smooth functions with added noise. If the
function values arise from observations or from extensive computations, these errors can be
considerable. GGOPT uses an adjustable mesh together with linear least squares to find smoothed
values of the function, gradient and Hessian at the center of the mesh. These values drive a descent
method that estimates optimal parameters. The smoothed values usually result in increased
accuracy.
Type
Article
Description
Comput Methods Programs Biomed. Author manuscript; available in PMC 2012 Jun 7
Series/Report No
Department
Applied Mathematics
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
grant RR 01243 and EB08407 from the National Institutes of Health
Funder
grant RR 01243 and EB08407 from the National Institutes of Health
Format
Citation
GGOPT: an unconstrained non-linear optimizer; J.B. Bassingthwaighte, I.S. Chan, A.A. Goldstein, I.B. Russak; Comput Methods Programs Biomed. Author manuscript; available in PMC 2012 Jun 7
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.