矩阵分解，对MIMO可能有用

lillian

【文件名】:06831@52RD_Matrix_YM_.rar
【格　式】:rar
【大　小】:114K
【简　介】:“Matrix decomposition refers to the transformation of a given matrix into a given canonical form.” [1],when the given matrix is transformed to a right-hand-side product of canonical matrices the process of producing this decomposition is also called “matrix factorization”. Matrix decomposition is a fundamental theme in linear algebra and applied statistics which has both scientific and engineering significance.The purposes of matrix decomposition typically involve two aspects: computational convenience and analytic simplicity. In the real world, it is not feasible for most of the matrix computations to be calculated in an optimal explicit way, such as matrix inversion, matrix determinant, solving linear system and least square fitting, thus to convert a difficult matrix computation problem into several easier tasks such as solving triangular or diagonal system will greatly facilitate the calculations. Data matrices representing some numerical observations such as proximity matrix or correlation matrix are often huge and hard to analyze, therefore to decompose the data matrices into some lower-order or lower-rank canonical forms will reveal the inherent characteristic and structure of the matrices and help to interpret their meaning readily.
【目　录】:
1. Overview 2
2 Matrix Multiplication and Definitions
3 Singular Value Decomposition
4 LU and Cholesky Decomposition
5 QR Decomposition
6 Schur Decomposition and Eigenvalue Decomposition
7 Biconjugate Decomposition