factorization.zip
標簽： factorization zip
上傳時間： 2015-03-03
上傳用戶：wendy15
Common Martix Operation ,include transpose, qr-factorization, trangular martix
標簽： qr-factorization Operation transpose trangular
上傳時間： 2013-11-30
上傳用戶：xc216
Code to run the Non-negative Matrix factorization algorithm as presented in the Lee, Seung 1999 Nature paper.
標簽： factorization Non-negative algorithm presented
上傳時間： 2016-01-19
上傳用戶：litianchu
2． Using QR factorization to find all of the eigenvalues and eigenvectors for the following matrix
標簽： factorization eigenvectors eigenvalues following
上傳時間： 2014-01-14
上傳用戶：cuiyashuo
非負矩陣分解技術（Nonnegtive Matrix factorization 一種信號或圖像的特征提取的方法，也可用于圖像壓縮
標簽： factorization Nonnegtive Matrix 非負矩陣分解
上傳時間： 2014-01-18
上傳用戶：kristycreasy
HankelToeplitz and Takagi factorization Package
標簽： HankelToeplitz factorization Package Takagi
上傳時間： 2017-03-29
上傳用戶：Shaikh
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.
標簽： least-squares unconstrained Statisti Applied
上傳時間： 2015-05-14
上傳用戶：aig85
平均因子分解法，適用于正定矩陣First, let s recall the definition of the Cholesky decomposition: Given a symmetric positive definite square matrix X, the Cholesky decomposition of X is the factorization X=U U, where U is the square root matrix of X, and satisfies: (1) U U = X (2) U is upper triangular (that is, it has all zeros below the diagonal). It seems that the assumption of positive definiteness is necessary. Actually, it is "positive definite" which guarantees the existence of such kind of decomposition.
標簽： 分解
上傳時間： 2013-12-24
上傳用戶：啊颯颯大師的
We present a particle filter construction for a system that exhibits time-scale separation. The separation of time-scales allows two simplifications that we exploit: i) The use of the averaging principle for the dimensional reduction of the system needed to solve for each particle and ii) the factorization of the transition probability which allows the Rao-Blackwellization of the filtering step. Both simplifications can be implemented using the coarse projective integration framework. The resulting particle filter is faster and has smaller variance than the particle filter based on the original system. The convergence of the new particle filter to the analytical filter for the original system is proved and some numerical results are provided.
標簽： construction separation time-scale particle
上傳時間： 2016-01-02
上傳用戶：fhzm5658
數值線性代數的Matlab應用程序包 共13個程序函數，每個程序函數有相應的例子函數一一對應，以*Example.m命名 程序名稱 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 豪斯霍爾德QR因子分解 ZXEC.m 最小二乘擬合 polynomial interpolant 最小二乘插值多項式 NCLU.m LU因子分解 Gaussian elimination 不選主元素的高斯消元 PALU.m LU因子分解 partial pivoting Gaussian elimination 部分選主元的高斯消元 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 冪迭代 Jacobi.m 求特征值 Jacobi iteration 按標準行方式次序的雅可比算法 Anld.m 求上Hessenberg Arnoldi Iteration 阿諾爾迪迭代 zuisu.m 解線性方程組 Steepest descent 最速下降法 CG.m 解線性方程組 Gradients 共軛梯度 BCG.m 解線性方程組 Biconjugate Gradients 雙共軛梯度
上傳時間： 2016-05-17
上傳用戶：小鵬
