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[Other resource] lsq DL : 0: The module LSQ is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar-rotation algorithm is used to update the QR- factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill- conditioned problems, such as fitting polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back-substitution routine BKSUB2 which it calls, the routines behave as in AS 274.-The module is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar - rotation algorithm is used to update the QR-factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill-conditioned problems, such as fitting Polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back - substitution
Update : 2008-10-13 Size : 56.51kb Publisher : AiQing
[Other resource] power2 DL : 0: 2． Using QR factorization to find all of the eigenvalues and eigenvectors for the following matrix
Update : 2008-10-13 Size : 82.55kb Publisher : 吕鹏
[Other resource] matrixqqq DL : 0: Common Martix Operation ,include transpose, qr-factorization, trangular martix
Update : 2008-10-13 Size : 2.01kb Publisher : 金弟
[Oracle] matrixqqq DL : 0: Common Martix Operation ,include transpose, qr-factorization, trangular martix
Update : 2025-04-04 Size : 2kb Publisher : 金弟
[Algorithm] lsq DL : 0: The module LSQ is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar-rotation algorithm is used to update the QR- factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill- conditioned problems, such as fitting polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back-substitution routine BKSUB2 which it calls, the routines behave as in AS 274.-The module is for unconstrained linear least-squares fitting. It is based upon Applied Statistics algorithm AS 274 (see comments at the start of the module). A planar- rotation algorithm is used to update the QR-factorization. This makes it suitable for updating regressions as more data become available. The module contains a test for singularities which is simpler and quicker than calculating the singular-value decomposition. An important feature of the algorithm is that it does not square the condition number. The matrix X X is not formed. Hence it is suitable for ill-conditioned problems, such as fitting Polynomials. By taking advantage of the MODULE facility, it has been possible to remove many of the arguments to routines. Apart from the new function VARPRD, and a back- substitution
Update : 2025-04-04 Size : 56kb Publisher :
[Algorithm] power2 DL : 0: 2． Using QR factorization to find all of the eigenvalues and eigenvectors for the following matrix-2. Using QR factorization to find all of the eigenvalues and eigenvectors for the following matrix
Update : 2025-04-04 Size : 83kb Publisher : 吕鹏
[Algorithm] NumericalLinearAlgebra DL : 1: 数值线性代数的Matlab应用程序包共13个程序函数，每个程序函数有相应的例子函数一一对应，以*Example.m命名程序名称用途 Method 方法 GrmSch.m QR因子分解 classical Gram－Schmidt orthogonalization 格拉母－斯密特 MGrmSch.m QR因子分解 modified Gram－Schmidt iteration 修正格拉母－斯密特 householder.m QR因子分解 Householder 豪斯霍尔德QR因子分解 ZXEC.m 最小二乘拟合 polynomial interpolant 最小二乘插值多项式 NCLU.m LU因子分解 Gaussian elimination 不选主元素的高斯消元 PALU.m LU因子分解 partial pivoting Gaussian elimination 部分选主元的高斯消元 cholesky.m 楚因子分解 Cholesky Factorization 楚列斯基因子分解 PwItrt.m 求最大特征值 Power Iteration 幂迭代 Jacobi.m 求特征值 Jacobi iteration 按标准行方式次序的雅可比算法 Anld.m 求上Hessenberg Arnoldi Iteration 阿诺尔迪迭代 zuisu.m 解线性方程组 Steepest descent 最速下降法 CG.m 解线性方程组 Gradients 共轭梯度 BCG.m 解线性方程组 Biconjugate Gradients 双共轭梯度
Update : 2025-04-04 Size : 6kb Publisher : YUXIANGCHENG
[matlab] work DL : 0: QR factorization of an m × n matrix A with m ≥ n is a pair of matrices A = QR
Update : 2025-04-04 Size : 1kb Publisher : anaid
[Process-Thread] SPQR-1.1.2.tar DL : 0: 基于C++编程的多线程的多波前稀疏矩阵的QR分解，Timothy A. Davis编写-SuiteSparseQR is an implementation of the multifrontal sparse QR factorization method. Parallelism is exploited both in the BLAS and across different frontal matrices using Intel s Threading Building Blocks, a shared-memory programming model for modern multicore architectures. It can obtain a substantial fraction of the theoretical peak performance of a multicore computer. The package is written in C++ with user interfaces for MATLAB, C, and C++.
Update : 2025-04-04 Size : 1.11mb Publisher : mengdieaaq
[matlab] Modi_Gram_S DL : 1: modified Gram-Schmidt (reduced QR factorization). A is a m-by-n matrix(m>=n)
Update : 2025-04-04 Size : 1kb Publisher : Ji Hye Park
[matlab] Gram_S DL : 0: classical Gram-Schmidt(unstable) (reduced QR factorization).A:m-by-n matrix. (m>=n)Q:m-by-n unitary matrix.R:n-by-n upper triangular.-classical Gram-Schmidt(unstable) (reduced QR factorization).A:m-by-n matrix. (m>=n)Q:m-by-n unitary matrix.R:n-by-n upper triangular.
Update : 2025-04-04 Size : 1kb Publisher : Ji Hye Park
[matlab] Comparison_of_QR_Factorization_Algorithms DL : 0: Comparison of QR Factorization Algorithms (Matlab script)
Update : 2025-04-04 Size : 2kb Publisher : Oliver,cheng
[MPI] qr DL : 0: OpenMP 实现QR 分解环境曙光 4000A集群-OpenMP to achieve QR decomposition Dawning 4000A cluster environment
Update : 2025-04-04 Size : 2kb Publisher : liuqiang
[Algorithm] QR DL : 0: 对一般矩阵的矩阵QR分解，其中R是对角线元素全为非负实数的上三角矩阵，Q为正交矩阵-QR factorization
Update : 2025-04-04 Size : 275kb Publisher : lang
[matlab] QR_factorization DL : 0: These are the QR/RQ factorization techniques required for the Zero forcing detection technique for MIMO.
Update : 2025-04-04 Size : 1kb Publisher : Karim Hamdy
[Mathimatics-Numerical algorithms] QR_and_Least_Square_problems DL : 0: 如何对随机生成的矩阵进行QR分解和并利用QR分解解决最小二乘问题.-Use Householder reflector to compute the QR factorization of a randomly generated matrix and then solve the Least-Square problems.
Update : 2025-04-04 Size : 2kb Publisher : 独孤星
[Algorithm] QR DL : 0: 矩阵的QR分解，编译执行，按提示操作输入矩阵的元素即可输出结果.- The QR factorization algorithm of matrix,compile and run,input the element according the suggestion,then you can have the desire result!
Update : 2025-04-04 Size : 256kb Publisher : 良仔
[SCM] QR DL : 0: a QR decomposition (also called a QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and an upper triangular matrix. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigenvalue algorithm, the QR algorithm.
Update : 2025-04-04 Size : 2kb Publisher : James
[Algorithm] QR_MATLAB DL : 0: QR 分解（包括Householer, Givens, 经典Gram Schmidt, 修正的GS methods)-QR factorization including Householer, Givens, CGS, MGS
Update : 2025-04-04 Size : 1kb Publisher : lulu
[matlab] qrfact DL : 0: householder qr factorization
Update : 2025-04-04 Size : 1kb Publisher : kiang

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