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Description: 用于Hooke—Jeeve模式搜索算法，完成多变量的寻优。多变量搜索中直接搜索算法的一种。-for Hooke-mode Jeeves search algorithm, complete multivariate optimization. Multivariable direct search of a search algorithm.
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Size: 10367 |
Author: 陈镇静 |
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Description: 优化算法中hooke-jeeves直接搜索法
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Size: 569 |
Author: conan9666 |
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Description: 用于Hooke—Jeeve模式搜索算法，完成多变量的寻优。多变量搜索中直接搜索算法的一种。-for Hooke-mode Jeeves search algorithm, complete multivariate optimization. Multivariable direct search of a search algorithm.
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Size: 10240 |
Author: 陈镇静 |
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Description: hooke算法的自我编写的小小的程序能够用来计算问题的最优值-hooke
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Size: 18432 |
Author: zhuxin |
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Description: 文化算法的MATLAB实现。实验结果表明该算法具有良好的性能。-MATLAB implementation of cultural algorithms. Experimental results show that the algorithm has good performance.
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Size: 17408 |
Author: 花自飘零 |
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Description: 优化算法中直接搜索法，Hooke-Jeeves算法-Direct search optimization method, Hooke-Jeeves Algorithm
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Size: 1024 |
Author: 韩智光 |
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Description: 用Matlab编写的用Hooke-Jeeves方法求函数极小点程序-Written with Matlab using Hooke-Jeeves method of procedure demand function minimizer
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Size: 1024 |
Author: 赵乐 |
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Description: Hooke And Jeeves Using Line Searches
nonlinear optimization
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Size: 1024 |
Author: Roozbeh |
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Description: hooke-jeeves的学习代码 c编写-hooke-jeeves learning code c to write
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Size: 1024 |
Author: HD |
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Description: Hooke and Jeeves search optimization fortran solution
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Size: 2048 |
Author: frt |
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Description: Hooke and Jeeves Search Method Matlab m file
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Size: 1024 |
Author: frt |
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Description: Hooke-Jeeves Directional Search Method
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Size: 2048 |
Author: aaa |
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Description: many optimization methods(BFGS Method / Golden section search method / Hooke & Jeeves direct search method / Simplex method)
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Size: 3886080 |
Author: Duknom Do |
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Description: Method of hooke and jeeves and the golden section in SCILAB for function 2d
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Size: 1024 |
Author: Anderson |
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Description: A simple optimization algorithm example :Hooke and Jeeves method
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Size: 8192 |
Author: Shahri Mammadova |
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Description: hooke-jeeves模式搜索法具有很好的求解最优解的功能,本算法是Mark G. Johnson 编写的，大家可以参考学习-Nonlinear Optimization using the algorithm of Hooke and Jeeves
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Size: 5120 |
Author: zhangsong |
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Description: Hook & Jeeves method implemented by matlab
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Size: 2048 |
Author: mani sabeti |
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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.
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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.
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Size: 1024 |
Author: Volkan |
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