Description: Matlab编写的最速下降法算法程序，美国一本权威数值计算方面的书上的源程序。-Matlab prepared by the steepest descent algorithm, a numerical calculation authoritative book on the source. Platform: |
Size: 1024 |
Author:李军 |
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Description: ％ COMPDIR Computes a search direction in a subspace defined by Z.
％ Helper function for NLCONST.
％ Returns Newton direction if possible.
％ Returns random direction if gradient is small.
％ Otherwise, returns steepest descent direction.
％ If the steepest descent direction is small it computes a negative
％ curvature direction based on the most negative eigenvalue.
％ For singular matrices, returns steepest descent even if small.- COMPDIR Computes a search direction in a subspace defined by Z. Helper function for NLCONST. Returns Newton direction if possible. Returns random direction if gradient is small. Otherwise, returns steepest descent direction. If the steepest descent direction is small it computes a negative curvature direction based on the most negative eigenvalue. For singular matrices, returns steepest descent even if small. Platform: |
Size: 1024 |
Author:张风 |
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Description: matlab另一中简单些的源码,只给出预测结果,不进行精度分析-Another more simple matlab source code, only the results of prediction are given, not to accuracy analysis Platform: |
Size: 1024 |
Author:杨香联 |
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Description: 最速下降法是一种沿着N维目标函数的负梯度方向搜索最小值的方法。该程序是用最速下降法求无约束最优化解。-Steepest descent method is an N-dimensional objective function along the negative gradient direction of the minimum search method. The program is used steepest descent method for unconstrained optimal resolve. Platform: |
Size: 1024 |
Author:xutianyuan |
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Description: 最小均方（LMS）自适应算法就是一中已期望响应和滤波输出信号之间误差的均方值最小为准的，依据输入信号在迭代过程中估计梯度矢量，并更新权系数以达到最优的自适应迭代算法。LMS算法是一种梯度最速下降方法，其显著的特点是它的简单性。这算法不需要计算相应的相关函数，也不需要进行矩阵运算。-Minimum mean-square (LMS) adaptive algorithm is that one has to respond to the expectations and filtering the output signal of the mean square error between the value of the minimum-based, based on the input signal is estimated in the iterative process of gradient vector, and to update the weights in order to achieve the most Adaptive iterative algorithm gifted. LMS algorithm is a steepest descent gradient method, and its significant features is its simplicity. This algorithm does not require calculation of the corresponding correlation function, nor the need for matrix calculation. Platform: |
Size: 3072 |
Author:闫丰 |
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Description: 最速下降法的MATLAB源程序，不知道是否喜欢-Steepest descent method of the MATLAB source code, I do not know like it or not Platform: |
Size: 4096 |
Author:张晓 |
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Description: 非线性规划中的最速下降法,黄金分割法,阻尼牛顿法,牛顿切线法.-Non-linear programming in the steepest descent method, golden section method, damping Newton s Law, Newton s tangent method. Platform: |
Size: 2048 |
Author: |
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Description: 最优化学习的Matlab程序:包括最速下降法,黄金分割法,曲线拟合等.-Matlab optimization learning process: including the steepest descent method, golden section method, such as curve fitting. Platform: |
Size: 5120 |
Author:lin |
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Description: 计算梯度下降法计算极值，只能找到局部最小点。可以通过调整步长实现全局最小-Calculation of gradient descent method to calculate extreme value, can only find local minimum point. By adjusting the step size can achieve the global minimum Platform: |
Size: 1024 |
Author:宗丹 |
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Description: 约束最优化方法－－最速下降法(也叫梯度法)，是人们用来求多个变量函数极值问题的最早的一种方法。-Constrained optimization methods- steepest descent method (also known as gradient method), is used for multiple variables function Extremum Problems earliest methods. Platform: |
Size: 2048 |
Author:anytry |
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Description: 采用最速下降法查找最小值，采用H终止准则，计算时间很快，很适合初学者使用-Steepest descent method using the minimum search using H termination criteria, the computation time soon, it is suitable for beginners to use Platform: |
Size: 5120 |
Author:sailorkai |
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Description: 基于LM算法实现的均衡器。采用梯度估计近似实现最陡下降法。给出最后实验误差平方的均值曲线图。有完整的注释。-LM algorithm based on the equalizer. Similar to the realization of the estimated gradient steepest descent method. Finally, given the experimental error of the mean-square curve. Note complete. Platform: |
Size: 1024 |
Author:why98 |
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Description: NUMERICAL OPTIMIZATION:
This steepest descent method with constant step length to find the minima of
f(x, y) = xy exp(− 2x^2 − y^2 + 0.3y)
Graphical represxentation in 5 ways of solution, simple and clear explained.-NUMERICAL OPTIMIZATION:
This is steepest descent method with constant step length to find the minima of
f(x, y) = xy exp(− 2x^2 − y^2+ 0.3y)
Graphical represxentation in 5 ways of solution, simple and clear explained.
Platform: |
Size: 1024 |
Author:venera |
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Description: Steepest descent have been applied to a biomedical application data and the component responsible for key operation is obtained
Platform: |
Size: 3072 |
Author:Madhavaraja |
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Search - steepest descent matlab