-
矩阵鞍点求法
用C++写的矩阵鞍点求法(C wrote saddle point Matrix Method)
- 2005-02-12 19:52:05下载
- 积分：1
-
AddMatrix
用三元组表为结构，实现稀疏矩阵的加法并输出。(Achieve sparse matrix addition)
- 2014-02-19 19:59:00下载
- 积分：1
-
bp.example
采用动量梯度下降算法训练BP网络，采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络(Gradient descent algorithm using momentum BP network training, using two training methods, namely, LM optimization algorithm (trainlm) and Bayesian regularization algorithm (trainbr), to facilitate the training of BP network)
- 2009-01-18 20:49:24下载
- 积分：1
-
homotopycontinue
说明： 同伦连续算法，很好得用来求解非线性方程组(Homotopy continuation method, may very well be used to solve nonlinear equations)
- 2021-02-19 21:59:43下载
- 积分：1
-
Moving-Least-Squares-Methods
移动最小二乘曲线拟合原理，论文中分析了一维和二维的例子，并且同最小二乘法进行比较。(Moving least squares curve fitting principle, the paper analyzes the one-dimensional and two-dimensional case, and compared with the least squares method.)
- 2020-11-06 08:49:49下载
- 积分：1
-
labview_zerophase-shift_filtering
zero shift fitering to be used in offline system identification
- 2010-08-29 23:00:34下载
- 积分：1
-
CPPsimplex
单纯形法，求解线性规划问题的通用方法。单纯形是美国数学家G.B.丹齐克于1947年首先提出来的。它的理论根据是：线性规划问题的可行域是 n维向量空间Rn中的多面凸集，其最优值如果存在必在该凸集的某顶点处达到。顶点所对应的可行解称为基本可行解。单纯形法的基本思想是：先找出一个基本可行解，对它进行鉴别，看是否是最优解；若不是，则按照一定法则转换到另一改进的基本可行解,再鉴别；若仍不是，则再转换,按此重复进行。因基本可行解的个数有限，故经有限次转换必能得出问题的最优解。如果问题无最优解也可用此法判别。
(Simplex method for the minimum)
- 2011-09-18 22:57:30下载
- 积分：1
-
Array-1-dimensi(prak-7)
program array 1 dimensi
- 2012-11-26 08:31:23下载
- 积分：1
-
Conjugate-Gradient-Method
共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)
- 2017-03-14 15:48:15下载
- 积分：1
-
counting-the-student-number
统计[0, 100] 之间的学生人数并且求出最值和均值(Statistics [100], the number of students and find the best value and the mean)
- 2012-06-24 21:37:37下载
- 积分：1
