-
EMD-C
EMD分解C语言算法,内含EMD分解所需的三次样条插值C语言算法。(EMD decomposition algorithm C language, containing the decomposition EMD cubic spline interpolation algorithm C language.)
- 2021-02-03 01:10:00下载
- 积分:1
-
oofem-2.4
有限元计算开源库,可以用于计算动力、静力、磁场等等方面的计算(Finite element calculation open source library)
- 2017-12-06 12:01:58下载
- 积分:1
-
jisuanfangfa
合工大大二计算方法实验的源代码,里面有实验指导书.有牛顿下山,迭代啊什么的(Sophomore calculation experiment source code, which has experimental guide books)
- 2010-06-19 14:42:44下载
- 积分:1
-
最速下降法与牛顿法
说明: 此代码包括牛顿法和梯度下降法的实现过程,最优化以及算法中常见的问题解决方式(This code includes the process of Newton method and gradient descent method)
- 2020-01-07 09:42:51下载
- 积分:1
-
Gauss_main_element_solve_equation
求解线性方程组,高斯消去法精确求解出任意个方程组的解,(Solving linear equations, Gaussian elimination exact solution,)
- 2011-09-08 18:51:27下载
- 积分:1
-
guassseidel
实现高斯施戴尔迭代,可以直接输出迭代矩阵,并且,误差可以手动修改。(computate guass-sedal process )
- 2009-12-14 09:00:22下载
- 积分:1
-
SVD
最小二乘估值的SVD分解计算方法,本程序可将最小二乘估值问题转化为超定方程组的问题处理,且可用奇异值分解的方法计算最小二乘问题。(Least Squares Estimates of the SVD decomposition method, the valuation process can be transformed into squares overdetermined equations deal with the problem, and can be calculated singular value decomposition least squares problem.)
- 2020-07-04 15:20:02下载
- 积分:1
-
Allocation-problem
用计算机编程的方法建立试卷的合理分配问题的优化模型(Optimization model to establish the papers a reasonable allocation problem with computer programming methods)
- 2013-03-14 21:05:15下载
- 积分:1
-
复数函数与积分变换
说明: 复数函数与积分变换 Fourier变换 Laplace逆变换(Complex number function and integral transformation Fourier transformation Laplace inverse transformation)
- 2020-10-05 19:08:48下载
- 积分:1
-
SVD
% 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法;SVD是最可靠的分解法,
% 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A),其中U和V代表二个相互正交矩阵,
% 而S代表一对角矩阵。 和QR分解法相同者, 原矩阵A不必为正方矩阵。
% 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组,在Quke2中就用这个来计算图形信息,性能相当的好。在计算线性方程组时,一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)
- 2020-12-21 10:29:08下载
- 积分:1