FourDimensional Golden Search
Abstract
The Golden search technique is a method to search a multipledimension space to find the minimum. It basically subdivides the possible ranges of parameters until it brackets, to within an arbitrarily small distance, the minimum. It has the advantages that (1) the function to be minimized can be nonlinear, (2) it does not require derivatives of the function, (3) the convergence criterion does not depend on the magnitude of the function. Thus, if the function is a goodness of fit parameter such as chisquare, the convergence does not depend on the noise being correctly estimated or the function correctly following the chisquare statistic. And, (4) the convergence criterion does not depend on the shape of the function. Thus, long shallow surfaces can be searched without the problem of premature convergence. As with many methods, the Golden search technique can be confused by surfaces with multiple minima.
 Authors:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1171678
 Report Number(s):
 LAUR1521424
 DOE Contract Number:
 AC5206NA25396
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
 Subject:
 99 GENERAL AND MISCELLANEOUS
Citation Formats
Fenimore, Edward E. FourDimensional Golden Search. United States: N. p., 2015.
Web. doi:10.2172/1171678.
Fenimore, Edward E. FourDimensional Golden Search. United States. doi:10.2172/1171678.
Fenimore, Edward E. 2015.
"FourDimensional Golden Search". United States.
doi:10.2172/1171678. https://www.osti.gov/servlets/purl/1171678.
@article{osti_1171678,
title = {FourDimensional Golden Search},
author = {Fenimore, Edward E.},
abstractNote = {The Golden search technique is a method to search a multipledimension space to find the minimum. It basically subdivides the possible ranges of parameters until it brackets, to within an arbitrarily small distance, the minimum. It has the advantages that (1) the function to be minimized can be nonlinear, (2) it does not require derivatives of the function, (3) the convergence criterion does not depend on the magnitude of the function. Thus, if the function is a goodness of fit parameter such as chisquare, the convergence does not depend on the noise being correctly estimated or the function correctly following the chisquare statistic. And, (4) the convergence criterion does not depend on the shape of the function. Thus, long shallow surfaces can be searched without the problem of premature convergence. As with many methods, the Golden search technique can be confused by surfaces with multiple minima.},
doi = {10.2172/1171678},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 2
}

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