Description: 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
- [Matrixpackage.Rar] - C language used in the calculation of ma
- [matrix_book] - matrix theory (College Books) second edi
- [Img_compress] - Using singular value decomposition metho
- [SVD] - SVD of the relevant information, the mai
- [solveequation] - By c language solution of nonlinear equa
- [svd] - SVD algorithm: the use of SVD decomposit
- [PCA] - PCA method uses two types of procedures:
- [SVD] - I have written the singular value decomp
- [qiyizhi] - Singular Value Decomposition matlab proc
- [matrix] - The realization of the basic computing c
File list (Check if you may need any files):
Download