搜索资源列表
matrix_factorization
- 实现矩阵的各种分解，LU分解，QR分解，Householder分解，Givens 分解-To achieve a variety of matrix decomposition, LU decomposition, QR decomposition, Householder decomposition, Givens decomposition
qr1
- Householder变换进行矩阵的QR分解-Householder transform matrix QR decomposition
house
- 利用householder变换对矩阵做QR分解，它将矩阵分解成一个正规正交矩阵Q与上三角形矩阵R。 - The matrix is decomposed into a regular orthogonal matrix Q and an upper triangular matrix R by QR decomposition using the householder transform.
reduction
- 输入矩阵的维数以及各个元素，选择矩阵分解的方法，进行分解。 有四种分解方法： 1、LU 分解 2、Gram-schmidt分解 3、Householder分解 4、Givens分解 -matrix decomposition methods
QR
- 有关QR分解的matlab的算法。分别是Givens_Rotations、Householder、Lanczos、Arnoldi-About QR decomposition matlab algorithm. Are Givens_Rotations, Householder, Lanczos, Arnoldi
4MUAV
- 用householder变化以及变形qr算法对一般实矩阵进行奇异值分解-Singular value decomposition for general real matrices with Householder and QR algorithm
QR-decomposition_md_qrh
- 矩阵分析与应用，使用Householder变换方法实现QR分解。-QR decomposition of matrix using the Householder transform
GS Cholesky
- GS迭代， Cholesky 分解 Givens方法，Householder 方法(Gs iteration cholesky decomposition)
Householder
- 使用household法分解矩阵的源程序 方便下载的(Using household method to decompose the source of the matrix Easy to download)
house
- householder decomposition with column pivoting
qrfact
- householder qr factorization
QRdecomposition
- QR 分解，包括householder 变换，用于矩阵分解，最小二乘法(QR decomosition including householder transform, you can use it to solve least square problem.)
QR
- 自动识别矩阵规模，进行QR分解运算，采用HouseHolder变换做成上Hessenberg矩阵，然后通过Givens变换做QR分解。(The size of the matrix is automatically identified, the QR decomposition operation is performed, the HouseHolder transform is used to make the upper Hessenberg matrix, and then the Q
dll
- matlab 程序 基于Householder的QR分解(Householder QR www.pudn.com/Download/upload.html)
matlab数值特征值与特征向量计算 实例源程序代码
- 特征值与特征向量的计算 339 10.1 特征值问题概述 339 10.1.1 特征多项式 339 10.1.2 特征值范围估计 340 10.2 幂法及反幂法 341 10.2.1 幂法 341 10.2.2 幂法的加速 344 10.2.3 反幂法 350 10.2.4 混合幂法 352 10.3 实对称矩阵的Jacobi法 353 10.3.1 Givens变换 353 10.3.2 基本Jacobi法 358 10.