Description: 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 Platform: |
Size: 57867 |
Author:AiQing |
Hits:
Description: 2． Using QR factorization to find all of the eigenvalues and eigenvectors for the following matrix Platform: |
Size: 84531 |
Author:吕鹏 |
Hits:
Description: 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 Platform: |
Size: 57344 |
Author: |
Hits:
Description: 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 Platform: |
Size: 84992 |
Author:吕鹏 |
Hits:
Description: 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++.
Platform: |
Size: 1162240 |
Author:mengdieaaq |
Hits:
Description: These are the QR/RQ factorization techniques required for the Zero forcing detection technique for MIMO. Platform: |
Size: 1024 |
Author:Karim Hamdy |
Hits:
Description: 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. Platform: |
Size: 2048 |
Author:独孤星 |
Hits:
Description: 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! Platform: |
Size: 262144 |
Author:良仔 |
Hits:
Description: 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. Platform: |
Size: 2048 |
Author:James |
Hits:
Search - QR factorization