Description: 求多维函数极值的一种算法，由Nelder和Mead提出，又叫单纯形算法，但和线性规划中的单纯形算法是不同的，由于未利用任何求导运算，算法比较简单，但收敛速度较慢，适合变元数不是很多的方程求极值-Multi-dimensional function extremum seeking an algorithm proposed by Nelder and Mead, also called the simplex algorithm, but in the linear programming simplex algorithm is different, because not to use any derivation operator, the algorithm is relatively simple, but slow convergence, the number of variables fit the equation is not a lot of extremal Platform: |
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Author:李军 |
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Description: nelder_mead优化算法，求多维函数极值的一种算法，不利用任何求导。利用多面体逼近。-nelder_mead optimization algorithm, and a multi-dimensional function extremum algorithm, do not use any derivation. The use of polyhedral approximation. Platform: |
Size: 2048 |
Author:menglingsai |
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Description: Nelder Mead simplex algorithm for minimizing N-dimension const function
Copyright (C) 2008 Colin Caprani - www.colincaprani.com
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
-Nelder Mead simplex algorithm for minimizing N-dimension const function
Copyright (C) 2008 Colin Caprani - www.colincaprani.com
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
Platform: |
Size: 10240 |
Author:ohadm |
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Description: The shuffled complex evolution with principal components analysis–University of California at Irvine (SP-UCI) method is a global optimization algorithm designed for high-dimensional and complex problems. It is based on the Shuffled Complex Evolution (SCE-UA) Method (by Dr. Qingyun Duan et al.), but solves a serious problem in searching over high-dimensional spaces," population degeneration". The population degeneration problem refers to the phenomenon that, when searching over the highdimensional parameter spaces, the population of the searching particles is very likely to collapse into a subspace of the parameter space, therefore losing the capability of exploring the entire parameter space. In addition, the SP-UCI method also combines the strength of shuffled complex, the Nelder-Mead simplex, and mutinormal resampling to achieve efficient and effective high-dimensional optimization. Platform: |
Size: 129024 |
Author:Shinva |
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Description: presents convergence properties of the Nelder{Mead algorithm applied to strictly convex
functions in dimensions 1 and 2. We prove convergence to a minimizer for dimension 1, and various
limited convergence results for dimension 2. Platform: |
Size: 427008 |
Author:苏斌 |
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