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
SIMPS = Strategy Simplex
Constrained minimizer, based on iterations of full-dimensional
simplex calls (Nelder-Mead direct search method), each time followed
by a series of two-dimensional simplex calls (local improvements by
subspaces). Platform: |
Size: 9216 |
Author:Yinjun |
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Description: The Nelder–Mead method or downhill simplex method or amoeba method is a commonly used nonlinear optimization technique, which is a well-defined numerical method for twice differentiable and unimodal problems. However, the Nelder–Mead technique is only a heuristic, since it can converge to non-stationary points on problems that can be solved by alternative methods. Platform: |
Size: 3072 |
Author:masha |
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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: |
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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: |
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Author:Shinva |
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Description: This file gives the main function for the implementation of the
hybridized version of the cuckoo search algorithm by combining it with
the Nelder Mead Simplex algorithm. The method is designed to minimize
functions with a low number of dimensions in a low number of
iterations.
- This file gives the main function for the implementation of the
hybridized version of the cuckoo search algorithm by combining it with
the Nelder Mead Simplex algorithm. The method is designed to minimize
functions with a low number of dimensions in a low number of
iterations.
Platform: |
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Author:Umair ji |
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Description: The elapsed time of Nelder and Mead simplex method seems to be larger than Hooke and Jeeves method but, it reached the solution in just 2 iterations which is remarkably less than the previous method. From Tables 1 and 2 it can be said that the optimized R, t and weight values are same in both methods.
Results of Part (a) and Part (b) are also parallel with the results of Homework III. Sequential Linear Programming methods seems to be closest method to Hooke and Jeeves method and Nelder and Mead simplex method. Yet all the differences between the final values are less than our termination parameter as a result, it can be said that all results have converged to the similar values. Thus the current solutions also gave us reliable results for the problem.-The elapsed time of Nelder and Mead simplex method seems to be larger than Hooke and Jeeves method but, it reached the solution in just 2 iterations which is remarkably less than the previous method. From Tables 1 and 2 it can be said that the optimized R, t and weight values are same in both methods.
Results of Part (a) and Part (b) are also parallel with the results of Homework III. Sequential Linear Programming methods seems to be closest method to Hooke and Jeeves method and Nelder and Mead simplex method. Yet all the differences between the final values are less than our termination parameter as a result, it can be said that all results have converged to the similar values. Thus the current solutions also gave us reliable results for the problem. Platform: |
Size: 1024 |
Author:Volkan |
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Description: By comparing elapsed times one can say that Hooke and Jeeves methods converge faster than other methods and the slowest one seems to be Nelder and Mead Simplex Method.
In this part of the assignment we are going to reach the solution by using Nelder and Mead Simplex method. Note that the starting simplex points are given. We also have reflection, contraction, expansion and scaling parameters.
Inspecting the results given in Table 1 one can say that the elapsed time is low (the code is working smoothly) and iteration number is acceptable. Both step sizes and their norm is in the allowed range. Final or optimized R, t and weight values are really close to the results that we have obtained in Homework III. As a results we can deduct that we accomplished a good optimization problem solution by using Hooke and Jeeves method.-By comparing elapsed times one can say that Hooke and Jeeves methods converge faster than other methods and the slowest one seems to be Nelder and Mead Simplex Method.
In this part of the assignment we are going to reach the solution by using Nelder and Mead Simplex method. Note that the starting simplex points are given. We also have reflection, contraction, expansion and scaling parameters.
Inspecting the results given in Table 1 one can say that the elapsed time is low (the code is working smoothly) and iteration number is acceptable. Both step sizes and their norm is in the allowed range. Final or optimized R, t and weight values are really close to the results that we have obtained in Homework III. As a results we can deduct that we accomplished a good optimization problem solution by using Hooke and Jeeves method. Platform: |
Size: 1024 |
Author:Volkan |
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Description: In this paper, the hybrid TDOA/AOA geolocation method with an optimization solution was suggested through
Nelder-Mead simplex method. With TDOA and AOA measurement data, the localization equation was formulated as a
simple matrix form. Both environmental and NLOS noise causes the location estimate error which is a significant problem Platform: |
Size: 880640 |
Author:ali |
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Description: In this paper, the hybrid TDOA/AOA geolocation method with an optimization solution was suggested through
Nelder-Mead simplex method. With TDOA and AOA measurement data, the localization equation was formulated as a
simple matrix form. Both environmental and NLOS noise causes the location estimate error which is a significant problem Platform: |
Size: 2048 |
Author:ali |
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