两种块校正最小二乘问题的代数关系

1 引言 在求解工程问题中,我们常常应用最小二乘方法 min‖Ax-b‖_2,A∈R~(m×n),m≥n (1.1) x∈R~n去得到问题的数值近似解或估计系统的未知参数.我们常常已知(1)的解,而希望求解修改问题     

分块带状矩阵的逆

1引言如果分块矩阵A=(A_(ij))_(n×n)满足A_(ij)=O(j-i＞p且i-j＞q),其中A_(ij)为m阶矩阵,则称A为(p,q)-分块带状矩阵．分块带状矩阵在一些实际问题中经常出现,例如在量子场论中用途很广的非线性Schr(?)dinger方程的差分离散问题,解热传导问题等,都会遇到分块带状矩阵．常见的分块三对角矩阵,分块五对角矩阵都是特殊的分块带状矩阵．采用通常的方法求解分块带状矩阵的逆矩阵时,需要进行O(n~3)次m阶矩阵的运算．本文首先将分块带状矩阵扩充成可逆的分块上(下)三角矩阵,利用其逆矩阵导出了分块带状矩阵的逆矩阵表达式；进而利用所得到的公式分别推导了分块三对角矩阵及分块五对角矩阵的逆矩阵的快速算法,所需运算量为O(n~2)次m阶矩阵的运算．本文的结果扩充了文[1]等关于分块三对角阵求逆的相关结果．     

矩阵特征理论在递归关系上的应用

众所周知 ,求解常系数线性齐次递归关系的方法比较多 .例如 ,差分方法 ,生成母函数法等 .本文说明怎样利用矩阵的理论求解线性递归关系的矩阵法 ,而对线性递归关系非齐次也作了简短讨论 .令f (n) =pf (n -1 ) qf (n -2 )　 (n =2 ,3 ,… ,n) (1 )其中 p,q都是复数域上的数 ,初值 f (0 ) ,f (1 ) ,求 f (n)的通项公式 .下面利用矩阵的工具说明怎样求 f (n) .我们把 (1 )改写成f (n 2 ) =pf (n 1 ) qf (n)　 (n =0 ,1 ,2 ,… ) (2 )根据 f (n 2 ) =pf (n 1 ) qf (n) ,f (n 1 ) =f (n 1 ) ,　即f (n 2 )f (n 1 ) =p　 q1…     

求解矩阵方程组的ABS算法

本文基于一类线性空间(Rn,n)n,n,建立求解( )X=B形式的矩阵方程组的ABS算法.讨论基本的ABS算法和两个特殊的ABS算法及其性质.并将其中的Huang算法用于求解带有各种约束(包括对称和稀疏约束)的拟牛顿方程.     

r－不可分矩阵的本原指数（Ⅱ）

本文给出了 n阶 r-不可分矩阵的本原指数的上界 ,即任 n阶 r—不可分矩阵 A的本原指数 (A)≤n+(r- ) 2r (1≤ r相似文献   

矩阵不等式约束下矩阵方程最小二乘问题的增广Lagrangian方法北大核心CSCD

称X∈R^(m×n)为实(R,S)对称矩阵,若满足X=RXS,其中R∈R^(m×m)和S∈R^(n×n)为非平凡实对合矩阵,即R=R^(-1)≠±I_m,S=S^(-1)≠±I_n.该文将优化理论中求凸集上光滑函数最小值的增广Lagrangian方法应用于求解矩阵不等式约束下实(R,S)对称矩阵最小二乘问题,即给定正整数m,n,p,t,q和矩阵A_i∈R^(m×m),B_i∈R^(n×n)(i=1,2,…,q),C∈R^(m×m),E∈R^(p×m),F∈R^(n×t)和D∈R^(p×t),求实(R,S)对称矩阵X∈R^(m×m)且在满足相容矩阵不等式EXF≥D约束下极小化‖∑_(i=1)~qA_iXB_i-C‖,其中EXF≥D表示矩阵EXF-D非负,‖·‖为Frobenius范数.该文给出求解问题的矩阵形式增广Lagrangian方法的迭代格式,并用数值算例验证该方法是可行且高效的.     

解等式约束加权线性最小二乘问题的矩阵校正方法

1 引言 在实际应用中常会提出解等式约束加权线性最小二乘问题 min(b_2-A_2x)~TW(b_2-A_2x) x∈R~n (1) s.t.A_1x=b_1,其中A_1∈R~(p×n),A~2∈R(q×n),b_1∈R~p,b_2∈R~q,W∈R(q×q)为对称正定矩阵. 对于问题(1),目前已有多种数值求解方法,如Paige利用(1)的对偶公式给出了一个向后稳定的数值方法.Gulliksson和Wedin利用加权QR分解技巧给出了解(1)的一个直接解法.作者利用广义Cholesky分解构造了解(1)的矩阵分解方法.     

最大公因数闭集上幂矩阵的行列式整除性

设S={x1,…,xn)是由n个不同正整数组成的最大公因数闭集,我们证明: (1)如果n≤3,则对(?)ε∈Z+,有det(S)nε整除det[S]nε;(2)如果maxxi∈S{xi}<12, 则对(?)ε∈Z+,有det(S)nε整除det[S]nε;(3)如果maxx∈S{R(x)}≤1,其中R(x)是x 在S中的最大型因子集,则对(?)ε∈Z+,有det(S)nε整除det[S]nε．     

标准Jacobi矩阵的混合型特征反问题

0 引言 本文讨论如下标准形式的Jacobi矩阵 其中a_i>0(i=1,2,…,n),b_i>0(i=1,2,…,n-1)。 对于Jacobi矩阵(对称三对角矩阵)的特征反问题,已有的成果[1],基本上集中在由两组频谱或两个特征对(指特征值及相应的特征向量)构造Jacobi矩阵的元素这样两类问题上,习惯上称之为频谱型或特征向量型反问题。本文提出且求解了第三类型——混合型特征反问题。即由一组频谱数据和一个特征向量构造矩阵元素的问题: 问题Ⅰ 给定正数λ~(1),λ~(2),…,λ~(n)和实向量x=(x_1,x_2,…,x_n)~T,其中x_1=1。构造一个标准形式的Jacobi矩阵J,使其第k阶顺序主子阵恰以λ~(k)(k=1,2,…,n)为其特征值。且(λ~(n),x)为其特征对。 问题Ⅱ 给定正数0<λ_1~(n)<λ_1~(n-1)<…<λ_1~(1)和正向量x=(x_1,x_2,…,x_n),其中x_=,x_k>0(k=2,…,n),构造一个标准形式的Jacobi矩阵J,使其第K阶顺序主子阵恰以λ_1~(k)为其最小特征值,而(λ~(n),x)为J的特征对。 问题Ⅲ 给定n个实数0<λ_1)<λ_2<…<λ_n和m个实数λ~(1),λ~(2),…,λ~(m)及m维向量x=(x_1,…,x_m)~T。构造n阶标准形式的Jaeobi矩阵J,使其第K阶顺序主子阵恰以λ~(k)(k=1,2,…,m)为其特征值,而(λ~(m),x)为第m阶顺序主子阵的特征对,且λ_k(k=1,2,…,n)为J的特征值。这里系大于或等     

一个矩阵不等式的修正及应用

设R(C)为实(复)数域,H~(n×n)为n×n的Hermitian矩阵的集合。当A(∈C~(n×n))的特征值皆为实数时,如不特殊说明,约定A的特征值满足λ_1(A)≥…≥λ_n(A)。文[1]有如下不等式, 令A=B=[(?)],知(1)式一般不成立,(1)式是[1]将[2]的关于奇异值不等式     

求分块三对角矩阵和分块周期三对角矩阵逆矩阵的快速算法

给出了分块三对角矩阵逆矩阵的快速算法,并利用所给算法得到了求分块周期三对角矩阵逆矩阵的快速算法.最后通过算例表示算法的有效性.     

求解块三对角方程组的新算法

根据块三对角矩阵的特殊分解,给出了求解块三对角方程组的新算法.该算法含有可以选择的参数矩阵,适当选择这些参数矩阵,可以使得计算精度较著名的追赶法高,甚至当追赶法失效时,由该算法仍可得到一定精度的解.     

块循环矩阵方程组的新算法

1　基本概念形如 A=a1 a2 … a Na N a1 … a N- 1?彙?廰2 a3 … a1的矩阵称为由 a1 ,a2 ,… ,a N 生成的循环矩阵 .力学和工程中的轴对称结构的计算产生上述循环矩阵 [2 - 3] .以循环矩阵A为系数矩阵的方程组 ,称为循环矩阵方程组 .已有的求解循环矩阵方程组的办法主要是各种迭代法 ,如递推法及 SOR,SSOR,SAOR超松弛迭代法[2 - 6] 等 .定义 1　形如A =A1 A2 … ANAN A1… AN- 1?彙?廇2 A3… A1　 (Ai,i =1 ,2 ,… ,N为 m阶矩阵 )的矩阵称为由 A1 ,A2 ,… ,AN 生成的块循环矩阵 .定义 2　系数矩阵 A为块循环矩阵的方程组AX …     

Non‐recursive solution of sparse block Hessenberg systems

A double‐phase algorithm, based on the block recursive LU decomposition, has been recently proposed to solve block Hessenberg systems with sparsity properties. In the first phase the information related to the factorization of A and required to solve the system, is computed and stored. The solution of the system is then computed in the second phase. In the present paper the algorithm is modified: the two phases are merged into a one‐phase version having the same computational cost and allowing a saving of storage. Moreover, the corresponding non‐recursive version of the new algorithm is presented, which is especially suitable to solve infinite systems where the coefficient matrix dimension is not a priori fixed and a subsequent size enlargement technique is used. A special version of the algorithm, well suited to deal with block Hessenberg matrices having also a block band structure, is presented. Theoretical asymptotic bounds on the computational costs are proved. They indicate that, under suitable sparsity conditions, the proposed algorithms outperform Gaussian elimination. Numerical experiments have been carried out, showing the effectiveness of the algorithms when the size of the system is of practical interest. Copyright © 2004 John Wiley & Sons, Ltd.     

Efficient computation of the extreme solutions of

We propose a new quadratically convergent algorithm, having a low computational cost per step and good numerical stability properties, which allows the simultaneous approximation of the extreme solutions of the matrix equations and . The algorithm is based on the cyclic reduction method.

     

矩阵广义对角化的标准形

定义了标准循环分块对角矩阵的概念,给出了矩阵广义对角化的标准形及其算法.     

关于K-分块循环矩阵及其对角化问题的讨论

给出了K-分块循环矩阵的概念,并探讨了K-分块循环矩阵的相似类及其对角化问题.     

Orthogonal similarity transformation into block‐semiseparable matrices of semiseparability rank k

Very recently, an algorithm, which reduces any symmetric matrix into a semiseparable one of semi‐ separability rank 1 by similar orthogonality transformations, has been proposed by Vandebril, Van Barel and Mastronardi. Partial execution of this algorithm computes a semiseparable matrix whose eigenvalues are the Ritz‐values obtained by the Lanczos' process applied to the original matrix. Also a kind of nested subspace iteration is performed at each step. In this paper, we generalize the above results and propose an algorithm to reduce any symmetric matrix into a similar block‐semiseparable one of semiseparability rank k, with k ∈ ?, by orthogonal similarity transformations. Also in this case partial execution of the algorithm computes a block‐semiseparable matrix whose eigenvalues are the Ritz‐values obtained by the block‐Lanczos' process with k starting vectors, applied to the original matrix. Subspace iteration is performed at each step as well. Copyright © 2005 John Wiley & Sons, Ltd.     

Queueing models,block hessenberg matrices and the method of neuts

We introduce a numerical method to compute the stationary probability vector of queueing models whose infinitesimal generator is of block Hessenberg form. It is shown that the stationary probability vector is equal to the first column of the inverse of the coefficient matrix. Furthermore, it is shown that the first column of the inverse of an upper (or lower) Hessenberg matrix may be obtained in a relatively small number of operations. Together, these results allow us to define a powerful algorithm for solving certain queueing models. The efficiency of this algorithm is discussed and a comparison with the method of Neuts is undertaken. A relationship with the method of Gaussian elimination is established and used to develop some stability results.This work was supported in part by NSF Grant MCS-83-00438.     

A scalable parallel factorization of finite element matrices with distributed Schur complements

We consider the parallel factorization of sparse finite element matrices on distributed memory machines. Our method is based on a nested dissection approach combined with a cyclic re‐distribution of the interface Schur complements. We present a detailed definition of the parallel method, and the well‐posedness and the complexity of the algorithm are analyzed. A lean and transparent functional interface to existing finite element software is defined, and the performance is demonstrated for several representative examples. Copyright © 2016 John Wiley & Sons, Ltd.