N-Dimension Golden Section Search: Its Variants and Limitations

One-dimension (1-D) golden section search (GSS) is widely used in many fields. This algorithm is very suitable for searching without derivative for the extrema of objective functions with unimodal. Two-dimension (2-D) GSS was also implemented and used for object tracking. In this paper, a structured...

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Bibliographic Details
Published in2009 2nd International Conference on Biomedical Engineering and Informatics pp. 1 - 6
Main Author Yen-Ching Chang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Autocorrelation
Computational efficiency
Covariance matrix
Fractals
Gaussian noise
Parameter estimation
Probability density function
Uncertainty
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ISBN9781424441327
1424441323
ISSN1948-2914
DOI10.1109/BMEI.2009.5304779

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Summary:One-dimension (1-D) golden section search (GSS) is widely used in many fields. This algorithm is very suitable for searching without derivative for the extrema of objective functions with unimodal. Two-dimension (2-D) GSS was also implemented and used for object tracking. In this paper, a structured n-dimension GSS and its variants are proposed. It has been shown that 1-D GSS is the fastest algorithm except for Fibonacci search. However, the efficiency of n-dimension GSS, n > 1, is generally not the case. The phenomenon will be analyzed and experimented. In addition, the limitations of n-D GSS are also illustrated. These concepts are very important for the future use of GSS.
ISBN:9781424441327
1424441323
ISSN:1948-2914
DOI:10.1109/BMEI.2009.5304779