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Household变换的Matlab程序,有很强的应用和参考价值-Household Transform Matlab program has a strong application and reference value
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void qr(double *a, double *d, int n) 矩阵的QR分解
假设数组an*n在内存中按行优先次序存放,此函数使用HouseHolder变换将其就地进行QR分解。
d为输出参数,d[0,n)存放QR分解的上三角矩阵对角线元素。
bool householder(double const *qr, double const *d, double *b, int n) 求线性代数方程组的解。
假设矩阵qrn*n为某个矩阵an*n的QR分解,在内
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约化对称矩阵为三对角矩阵的householder变换法的C语言实现,包括源代码和编译后的可执行文件-Reduced matrix is tridiagonal symmetric matrix of the householder transform the C language, including source code and compiled executable file
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多入多出(MIMO)系统可以获得比单发单收系统更高的容量,对于MIMO系统,最大似然检测是最优接收,但其
指数复杂度难以在实际中应用。针对该问题,结合格缩减理论提出了基于Householder变换的复数域格缩减算法,将该算法和
MIMO次优检测算法相结合,给出了量化判决方法,且该复数域格缩减算法复杂度小于实数域格缩减算法。仿真结果表明,
基于Householder变换复数域格缩减的MIMO次优检测算法,通过优化信道矩阵可以得到更好的判决域,取得了逼近最优最大
似然检测算法的性能。
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本程序是利用Householder变换分解得到Q,U-This program is using Householder transform decomposition of Q, U
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约化对称矩阵的豪斯荷尔德变换法,C代码,常用,较经典-The symmetric matrix reduction Householder transform method, C code, commonly used than the classic
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用householder变换法求矩阵A的正交分解A=QR -Householder transform matrix A orthogonal decomposition A = QR
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用HOUSEHOLDER变换将N阶实对称矩阵约化为对称三对角阵-N-order with HOUSEHOLDER transform into a real symmetric matrix approximately symmetric tridiagonal matrix
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qr变换和householder求特征值的算法,可与matlab结果比对-qr transform and householder eigenvalue algorithm, the results compare with matlab
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Householder变换进行矩阵的QR分解-Householder transform matrix QR decomposition
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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。
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The matrix is decomposed into a regular orthogonal matrix Q and an upper triangular matrix R by QR decomposition using the householder transform.
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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
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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.)
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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
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