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Description: 最佳矩阵连乘 给定n个矩阵｛A1,A2,…An｝，其中Ai与A i+1是可乘的，i=1，2…，n-1。考察这n个矩阵的连乘积A1A2…An。矩阵A和B可乘的条件是矩阵A的列数等于矩阵B的行数。若A是一个p×q矩阵，B是一个q×r矩阵，则其乘积C=AB是一个p×r矩阵,需要pqr次数乘。
由于矩阵乘法满足结合律，故计算矩阵的连乘积可以有许多不同的计算次序。例如，设3个矩阵{A1,A2,A3}的维数分别为10×100，100×5，和5×50。若按加括号方式((A1A2)A3)计算，3个矩阵连乘积需要的数乘次数为10×100×5+10×5×50=7500。若按加括号方式(A1(A2A3))计算，3个矩阵连乘积总共需要10×5×50+10×100×50=75000次数乘。由此可见，在计算矩阵连乘积时，加括号方式，即计算次序对计算量有很大影响。
矩阵连乘积的最优计算次序问题，即对于给定的相继n个矩阵｛A1,A2,…An｝(其中矩阵Ai的维数为pi-1×p，i=1,2，…，n)，确定计算矩阵连乘积A1,A2,…An的计算次序，使得依此次序计算矩阵连乘积需要的数乘次数最少。
-best matrix continually multiply given n matrix (A1, A2, ... An), Ai and A is a mere i, i = 1, 2 ..., n-1. N explore the link matrix product ... An A1A2. Matrices A and B can either condition is out of the matrix A few matrix B is the number of rows. If A is a p q matrix B is a q-r-matrix, its product C = AB is a p r matrix, the number required by pqr. Because matrix multiplication meet the law of combination, it's even calculated matrix product can be calculated in many different priorities. For example, the matrix-based 3 (A1, A2, A3) dimension of 10 100, 100 5 5 and 50. If bracketed by the way ((A1A2) A3), even three product matrix multiplication in the number of 10 100 10 5 50 = 7,500. If bracketed by the way (A1 (A2A3)), three matrix product even need a total of 10 5 50
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Size: 6148 |
Author: 肿事右 |
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Description: 最佳矩阵连乘 给定n个矩阵｛A1,A2,…An｝，其中Ai与A i+1是可乘的，i=1，2…，n-1。考察这n个矩阵的连乘积A1A2…An。矩阵A和B可乘的条件是矩阵A的列数等于矩阵B的行数。若A是一个p×q矩阵，B是一个q×r矩阵，则其乘积C=AB是一个p×r矩阵,需要pqr次数乘。
由于矩阵乘法满足结合律，故计算矩阵的连乘积可以有许多不同的计算次序。例如，设3个矩阵{A1,A2,A3}的维数分别为10×100，100×5，和5×50。若按加括号方式((A1A2)A3)计算，3个矩阵连乘积需要的数乘次数为10×100×5+10×5×50=7500。若按加括号方式(A1(A2A3))计算，3个矩阵连乘积总共需要10×5×50+10×100×50=75000次数乘。由此可见，在计算矩阵连乘积时，加括号方式，即计算次序对计算量有很大影响。
矩阵连乘积的最优计算次序问题，即对于给定的相继n个矩阵｛A1,A2,…An｝(其中矩阵Ai的维数为pi-1×p，i=1,2，…，n)，确定计算矩阵连乘积A1,A2,…An的计算次序，使得依此次序计算矩阵连乘积需要的数乘次数最少。
-best matrix continually multiply given n matrix (A1, A2, ... An), Ai and A is a mere i, i = 1, 2 ..., n-1. N explore the link matrix product ... An A1A2. Matrices A and B can either condition is out of the matrix A few matrix B is the number of rows. If A is a p q matrix B is a q-r-matrix, its product C = AB is a p r matrix, the number required by pqr. Because matrix multiplication meet the law of combination, it's even calculated matrix product can be calculated in many different priorities. For example, the matrix-based 3 (A1, A2, A3) dimension of 10 100, 100 5 5 and 50. If bracketed by the way ((A1A2) A3), even three product matrix multiplication in the number of 10 100 10 5 50 = 7,500. If bracketed by the way (A1 (A2A3)), three matrix product even need a total of 10 5 50
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Size: 6144 |
Author: 肿事右 |
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Description: qrd_rls_AR_pred.m - use the QR decomposition-based RLS algorithm
to predict complex-valued AR process.-qrd_rls_AR_pred.m- use the QR decomposition-based RLS algorithm
to predict complex-valued AR process.
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Size: 1024 |
Author: 丁董 |
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Description: 给定n个矩阵{A1,A2,…,An}，其中Ai与Ai+1是可乘的，i=1,2,…,n-1。考察这n个矩阵的连乘积A1A2…An。由于矩阵乘法满足结合律，故计算矩阵的连乘积可以有许多不同的计算次序，这种计算次序可以用加括号的方式来确定。若一个矩阵连乘积的计算次序完全确定，则可以依此次序反复调用2个矩阵相乘的标准算法（有改进的方法，这里不考虑）计算出矩阵连乘积。若A是一个p×q矩阵，B是一个q×r矩阵，则计算其乘积C=AB的标准算法中，需要进行pqr次数乘。
-Given n matrices (A1, A2, ..., An), which Ai and Ai+ 1 is the multiplicative, i = 1,2, ..., n-1. Visit this link n-matrix product A1A2 ... An. As a result of matrix multiplication to meet the combination of law, it
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Size: 1024 |
Author: 王旺 |
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Description: 一个基于QR分解的并行原-对偶内点算法，给予matlab，读入数据成为xml文件。
-QR decomposition based on parallel the original- dual interior point algorithm, given matlab, read into the data become a xml file.
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Size: 2048 |
Author: ylf |
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Description: 程序中给出了用变形QR方法计算实对称三角阵的全部特征值与相应的向量-Procedures are given deformation QR method with real symmetric triangular matrix of all eigenvalues with the corresponding vector
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Size: 180224 |
Author: 王云峰 |
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Description: 本标准规定了QR码符号的要求。它规定了QR码模式2符号的特征，数据字符编码，符号格式，尺寸特征，纠错规则，参考译码算法，符号质量要求，以及可由用户选择的应用参数，在附录中给出了QR码模式1符号不同于模式2的特性。-This standard sets out the requirements of QR Code symbols. It provides a QR Code symbol Mode 2 characteristics, data character encoding, symbol formats, dimensional characteristics, error correction rules, reference decoding algorithm, symbol quality requirements, as well as by the application of the user to choose parameters, given in the appendix QR Code Mode 1 Mode 2 is different from the symbol properties.
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Size: 2367488 |
Author: eydp |
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Description: QR分解SMI算法的目的正是要避免直接来解线性方程，而是将自相关矩阵分解，并利用Givens旋转实现数据矩阵的QR分解，最终将权矢量的求解问题转化为三角线性方程组的求解问题。-QR decomposition SMI algorithm, whose objective it is to avoid directly to solution of linear equations, but will auto-correlation matrix decomposition, and the use of Givens rotation implementation of the QR decomposition of data matrix, the weight vector will eventually solve the problem into a triangular system of linear equations solving the problem.
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Size: 1024 |
Author: 张亚光 |
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Description: These are the QR/RQ factorization techniques required for the Zero forcing detection technique for MIMO.
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Size: 1024 |
Author: Karim Hamdy |
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Description: The alogrithm employed in this toolbox for determining Lyapunov
exponents is according to the algorithms proposed in
[1] A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano "Determining Lyapunov Exponents from a Time Series," Physica D, Vol. 16, pp. 285-317, 1985.
[2] J. P. Eckmann and D. Ruelle, "Ergodic Theory of Chaos and Strange
Attractors," Rev. Mod. Phys., Vol. 57, pp. 617-656, 1 The algorithm given in [1] is used for first-order systems while the QR-based algorithm proposed in [2] is applied for higher order
systems. by Steve W. K. SIU, July 5, 1998.
-The alogrithm employed in this toolbox for determining Lyapunov
exponents is according to the algorithms proposed in
[1] A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano,
"Determining Lyapunov Exponents from a Time Series," Physica D,
Vol. 16, pp. 285-317, 1985.
[2] J. P. Eckmann and D. Ruelle, "Ergodic Theory of Chaos and Strange
Attractors," Rev. Mod. Phys., Vol. 57, pp. 617-656, 1985.
The algorithm given in [1] is used for first-order systems while
the QR-based algorithm proposed in [2] is applied for higher order
systems.
by Steve W. K. SIU, July 5, 1998.
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Size: 4096 |
Author: LI jian |
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Description: 本程序用带双步位移的QR分解法求一给定矩阵的全部特征值，并对其中的每一个实特征值求相应的特征向量，给出算法的设计方案和全部源程序，计算并输出如下内容：（1）矩阵经过拟上三角化后所得的矩阵；（2）对矩阵进行QR分解后所得的矩阵Q、R和RQ；（3）矩阵的全部特征值；（4）矩阵的相应于实特征值的特征向量。-The program uses two-step displacement with QR decomposition method find all the characteristics of a given matrix value, and each of them find the corresponding real eigenvalue eigenvector algorithm design is given and all source code, calculate and the following: (1) matrix through the post to be obtained on the triangular matrix (2) of the matrix from QR decomposition of the matrix Q, R, and RQ (3) the value of all the characteristics of the matrix (4) the corresponding matrix the real eigenvalue eigenvectors.
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Size: 303104 |
Author: abler |
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Description: QR分解法是三种将矩阵分解的方式之一。这种方式，把矩阵分解成一个正交矩阵与一个上三角矩阵的积。QR 分解经常用来解线性最小二乘法问题。QR 分解也是特定特征值算法即QR算法的基础。-QR decomposition are the three ways of decomposition of the matrix. In this way, the matrix decomposition into an orthogonal matrix and an upper triangular matrices. QR decomposition is often used for solving linear least squares problems. QR decomposition algorithm is given that QR eigenvalue algorithms.
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Size: 1024 |
Author: 徐默涵 |
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Description: 、对给定的若干组数据，按照指定的阶数，根据最小二乘原理分别利用正规方程方法
和QR 分解方法进行多项式拟合。
2、对给定的若干组数据，求经过这些点的插值多项式。-Given set of data, according to a specified order, according to the principle of least squares normal equations
Polynomial fitting and the QR decomposition method.
Given set of data, find the interpolating polynomial through these points.
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Size: 668672 |
Author: fzk |
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Description: Gram-Schmidt QR分解
Householder QR 分解
Given-Rotation QR 分解
Fast Given-Rotation QR分解
Upper Hessenberg矩阵
-Gram-Schmidt QR decomposition Householder QR decomposition Given-Rotation QR decomposition Fast Given-Rotation QR decomposition Upper Hessenberg matrix
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Size: 4096 |
Author: 骆晓林 |
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Description: 1、对给定的若干组数据，按照指定的阶数，根据最小二乘原理分别利用正规方程方法和QR 分解方法进行多项式拟合。
2、对给定的若干组数据，求经过这些点的插值多项式。
-1, for a given number of sets of data, according to the order specified in accordance with the principle of least squares methods are the use of the normal equations and QR decomposition method of polynomial fitting. 2, for a given number of sets of data, seeking through polynomial interpolation of these points.
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Size: 10240 |
Author: 林宇 |
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Description: Description
给定n个输入输出对，用给定的m次多项式拟合输入输出关系。当n大于多项式阶数m时，化为超定方程求解问题。这里采用最小二乘方法求解。问题建模如下：
1
化为矩阵形式：
2
其中
3
对上式求导，易得
4
利用对X的QR分解可以有效地降低上述运算的复杂度，并提高精度。请完成推导，并据此设计算法计算参数a*。
Input Description
第一行输入n和m。此后每行依次输入一组 。均为浮点数。
Output Description
将计算得到的多项式参数 由低阶到高阶逐行输出。只需保留整数部分。-Description Given n input-output pairs, with a given m-order polynomial fitting the input-output relationship. When n is greater than the polynomial order m, into overdetermined equations to solve the problem. Using the least squares method to solve here. MODELING as follows: 1 into a matrix form: 2 wherein Formula seek three pairs of the guide, and easy to get 4 X' s use of QR decomposition can effectively reduce the arithmetic complexity, and improve accuracy. Please complete derivation, and accordingly design algorithm parameters a*. Input Description The first line of input n and m. Then every line in turn enter a group. They are floating point numbers. Output Description The polynomial parameters calculated by the low-level to high-end progressive output. Simply reserved integer part.
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Size: 1024 |
Author: mingren |
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Description: part1用三次样条插值的三弯矩法
part2求非线性方程及方程组的根
part3用龙贝格和高斯法求积分
part4比较三次样条求导法与数值积分求导法
part5用指定方法求给定方程组的解
part6研究解线性方程方程组的迭代法收敛速度
part7求非线性方程及方程组的根
part8用QR算法求矩阵特征值
part8求非线性方程及方程组的根-Three moment Method part2 part1 cubic spline interpolation with nonlinear equations and Equations root part3 Derivation and numerical integration using Romberg and Gauss quadrature part4 Comparative Cubic Spline Derivation required by the specified method part5 method for the iterative convergence solution part6 linear equations of equations given study seeking roots equations part7 part8 nonlinear equations for the set of equations and matrix eigenvalue part8 seeking roots and nonlinear equations using QR algorithm equations
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Size: 8192 |
Author: zhlicun |
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Description: Givens rotation and Householder reflection
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Size: 179200 |
Author: kiang
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