Abstract
Golden section search method is one of the fastest direct search algorithms to solve single variable optimization problems, in which the search space is reduced from [a, b] to [0,1]. This paper describes an extended golden section search method in order to find the minimum of an n-variable function by transforming its n-dimensional cubic search space to the zero-one n-dimensional cube. The paper also provides a MATLAB code for two-dimensional and three-dimensional golden section search algorithms for a zero-one n-dimensional cube. Numerical results for some benchmark functions up to five dimensions and a comparison of the proposed algorithm with the Neldor Mead Simplex Algorithm is also provided.
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