双变量矩阵方程组对称最小二乘解的迭代算法

本文研究了求双变量线性矩阵方程组的对称最小二乘解的问题.利用求解线性代数方程组的共轭梯度法的基本思想,通过对有关矩阵和系数的变形与近似处理,建立了一种迭代算法.拓宽了共轭梯度法的适用范围.算例表明,迭代算法是有效的.     

一类双对称矩阵反问题的最小二乘解

１．问题的提出近年来,对于矩阵反问题ＡＸ＝Ｂ的研究已取得了一系列的结果［１］,获得了解存在的条件,但由于实际问题中Ｘ,Ｂ由实验给出,很难保证满足解存在的条件,因此研究问题的最小二乘解是有实际意义的．本文就结构设计中用到的一类双对称矩阵的最小二乘问题进行探讨．令Ｒ~(ｎ×ｍ)表示所有ｎ×ｍ阶实矩阵集合,Ｒ~n=Ｒ~(n×1)　表示其中秩为ｒ的子集;ＯＲ~（ｎ×ｎ）　表示所有ｎ阶正交阵之集;Ａ~（ ）表示矩阵Ａ的Ｍｏｏｒｅ－Ｐｅｎｒｏｓｅ广义逆;Ｉ_ｋ表示ｋ阶单位阵;||·||表示Fｒｏｂｅｎｉｕｓ范数;表示ＳＲ~(ｎ…     

双反对称矩阵反问题的最小二乘解

1 引 言Rn×m表示所有n×m阶实矩阵集合,Rrn×m表示Rn×m中秩为r的子集;ORn×m表示所有n阶正交阵的集合;A+表示A的Moore-Penrose广义逆;Iκ表示κ阶单位阵;||·||表示Frobenius范数;ASRn×m表示n阶实反对称阵的全体;A＊B表示A与B的Hadamard乘     

矩阵方程组一种异类约束最小二乘解的迭代算法

本文研究了求双矩阵变量线性矩阵方程组(LMEs)的一种异类约束最小二乘解的问题.通过构造等价的LMEs,并修改共轭梯度法的下降方向及其有关系数,建立了一种迭代算法.算例表明,迭代算法是有效的.     

一类对称正交对称矩阵反问题的最小二乘解

1 引言 本文记号R~(n×m),OR~(n×n),A~+,I_k,SR~(n×n),rank(A),||·||,A*B,BSR~(n×n)和ASR~(n×n)参见[1].若无特殊声明文中的P为一给定的矩阵且满足P∈OR~(n×n)和P=P~T. 定义1 设A=(α_(ij))∈R~(n×n).若A满足A=A~T,(PA)~T=PA则称A为n阶对称正交对称矩阵;所有n阶对称正交对称矩阵的全体记为SR_P~n.若A∈R~(n×n)满足A~T=A,(PA)~T=-PA,则称A为n阶对称正交反对称矩阵;所有n阶对称正交反对     

线性流形上矩阵方程BTXB=F的双对称最小二乘解

1引言令R~(n×m)、OR~(n×n)、SR~(n×n)(SR_0~(n×n))分别表示所有n×m阶实矩阵、n阶实正交阵、n阶实对称矩阵(实对称半正定阵)的全体,A~ 表示A的Moore-Penrose广义逆,I_k表示k阶单位矩阵,S_k表示k阶反序单位矩阵。R(A)表示A的列空间,N(A)表示A的零空间,rank(A)表示矩阵A的秩。对A=(a_(ij)),B=(b_(ij))∈R~(n×m),A*B表示A与     

矩阵方程AXA^T＋BYB^T=C的双对称最小二乘解及其最佳逼近

基于共轭梯度法的思想,通过特殊的变形,建立了一类求矩阵方程AXA^T＋BYB^T=C的双对称最小二乘解的迭代算法．对任意的初始双对称矩阵．在没有舍人误差的情况下,经过有限步迭代得到它的双对称最小二乘解;在选取特殊的初始双对称矩阵时,能得到它的的极小范数双对称最小二乘解．另外,给定任意矩阵,利用此方法可得到它的最佳逼近双对称解,数值例子表明,这种方法是有效的．     

矩阵方程A~TXA=D的双对称最小二乘解

１．引 言 本文用 Ｒｎ×ｍ表示全体 ｎ×ｍ实矩阵集合，用 ＳＲｎ×ｎ（ＳＲ０ｎ×ｎ）表示全体 ｎ× ｎ实对称（实对称半正定）矩阵集合，ＯＲｎ×ｎ表示全体 ｎ× ｎ实正交矩阵集合，ＢＳＲｎ×ｎ表示全体ｎ×ｎ双对称实矩阵集合．这里，一个实对称矩阵Ａ＝（ａｉｊ）ｎ×ｎ被称为双对称矩阵，如果对所有的 　　　　　　　 　　　　　　　　　　　　　　　用Ａ×Ｂ表示矩阵 Ａ与 Ｂ的Ｈａｄａｍａｒｄ乘积，Ｉｋ表示 ｋ× ｋ阶单位矩阵，Ｏ表示零矩阵，Ｓｋ＝（ｅｋ，…，ｅ２，ｅ１）∈ Ｒｋ×ｋ，其中ｅｉ表示Ｉｋ的第ｉ列． 矩阵方程…     

广义次对称矩阵反问题的最小二乘解

讨论了广义次对称矩阵反问题的最小二乘解,得到了解的一般表达式,并就该问题的特殊情形:矩阵反问题,得到了可解的充分必要条件及解的通式.此外,证明了最佳逼近问题解的存在唯一性,并给出了其解的具体表达式.     

关于求解带约束秩亏线性回归方程组最小二乘解的一个算法

1 引 言 考虑带线性约束的秩亏线性回归模型: Y=Xβ+ε, Hβ=c, (M_1) ε～N(0,σ~2V), V≥0,及带线性约束和非负性约束的秩亏线性回归模型:     

广义耦合Sylvester矩阵方程的对称-反对称最小二乘解

本文主要研究极小残差问题‖(A1XB1+C1YD1A2XB2+C2YD2)-(M1M2)‖=min关于X对称-Y反对称解的迭代算法.本文首先给出等价于极小残差问题的规范方程,然后,提出求解此规范方程的对称-反对称解的迭代算法.在不考虑舍入误差的情况下,任取一个初始的对称-反对称矩阵对(X0,Y0),该算法都可以在有限步内求得该极小残差问题的对称-反对称解.最后讨论该问题的极小范数对称-反对称解.     

The Nearest Bisymmetric Solutions of Linear Matrix Equations

The necessary and sufficient conditions for the existence of and the expressions for the bisymmetric solutions of the matrix equations (Ⅰ)A1X1B1 A2X2B2 ^… AkXkBk=D,(Ⅱ)A1XB1 A2XB2 … AkXBk=D and (Ⅲ) (A1XB1,A2XB2,…,AkXBk)=(D1,D2,…,Dk) are derived by using Kronecker product and Moore-Penrose generalized inverse of matrices. In addition, in corresponding solution set of the matrix equations, the explicit expression of the nearest matrix to a given matrix in the Frobenius norm is given. Numerical methods and numerical experiments of finding the neaxest solutions axe also provided.     

求矩阵方程组的反对称最小二乘解的递推算法

讨论了矩阵方程组A_1XB_1=D_1,A_2XB_2=D_2反对称最小二乘解的递推算法,该算法不仅能够用于计算反对称最小二乘解,而且在选取特殊的初始矩阵时,算法能够求出矩阵方程组的极小范数反对称最小二乘解,以及对给定的矩阵进行最佳逼近的反对称解.     

Least Squares Approximate and Least Norm Solutions of Paired Singular Equations

Paired operators T = A₁P + A₂Q on a HILBERT space are studied where P is a projector, P + Q = I, and the coefficients are linear invertible operators. The MOORE -PENROSE inverse of T can be obtained explicitly from a factorization of the coefficients, which is equivalent to the normal solvability of T and occurs in numerous applications. As an example, systems of singular integral equations of CAUCHY type are analysized in detail.     

A New Algorithm for Constrained Matrix Least Squares Approximations

This paper considers the problem of approximating a given symmetric matrix by a symmetric matrix with a prescribed spectrum so that the Frobenius norm of the matrix difference is minimized. By the introduction of a variable search direction, a new convergent algorithm for solving the problem is derived, which is guaranteed to be convergent and is capable of achieving a fast rate of convergence. It is shown that the set of fixed points of the proposed algorithm coincides with the set of equilibrium points of the original double bracket equation. A numerical example is presented to demonstrate superior performance of the proposed algorithm over a standard double bracket algorithm.     

A Non-Conforming Least Squares Spectral Element Formulation for Oseen Equations with Applications to Navier-Stokes Equations

In this article, we propose a non-conforming exponentially accurate least-squares spectral element method for Oseen equations in primitive variable formulation that is applicable to both two- and three-dimensional domains. First-order reformulation is avoided, and the condition number is controlled by a suitable preconditioner for velocity components and pressure variable. A preconditioned conjugate gradient method is used to obtain the solution. Navier-Stokes equations in primitive variable formulation have been solved by solving a sequence of Oseen type iterations. For numerical test cases, similar order convergence has been achieved for all Reynolds number cases at the cost of higher iteration number for higher Reynolds number.     

The Solutions of the Coupled Einstein-Maxwell Equations and Dilaton Equations

In this paper, we consider extremely charged static perfect fluid distributions with a dilaton field in the framework of general relativity. By using calculus of variations, we establish the existence theorem for the solutions of this important gravitational system. We show that there is a continuous family of smooth solutions realizing asymptotically flat space metrics.     

Indefinite Least Squares Problem with Quadratic Constraint and Its Condition Numbers

In this paper,we consider the indefinite least squares problem with quadratic constraint and its condition numbers.The conditions under which the problem has the unique solution are first presented.Then,the normwise,mixed,and componentwise condition numbers for solution and residual of this problem are derived.Numerical example is also provided to illustrate these results.     

Lp-Solutions of Vector Refinement Equations with General Dilation Matrix

The purpose of this paper is to investigate the solutions of refinement equations of the form ψ（x）∑α∈Z α（α）ψ（Mx-α）,x∈R, where the vector of functions ψ = （ψ1,..., ψr）^T is in （Lp（R^n））^r, 0 〈 p≤∞, α（α）, α ∈ Z^n, is a finitely supported sequence of r × r matrices called the refinement mask, and M is an s × s integer matrix such that limn→∞M^-n=0, In this article, we characterize the existence of an Lp=solution of the refinement equation for 0〈 p ≤∞, Our characterizations are based on the p-norm joint spectral radius.     

The Inverse Problem of Centrosymmetric Matrices with a Submatrix Constraint

By using Moore-Penrose generalized inverse and the general singular value decomposition of matrices, this paper establishes the necessary and sufficient conditions for the existence of and the expressions for the centrosymmetric solutions with a submatrix constraint of matrix inverse problem AX = B. In addition, in the solution set of corresponding problem, the expression of the optimal approximation solution to a given matrix is derived.