Least-Squares Solution with the Minimum-Norm for the Matrix Equation A^TXB ＋ B^TX^TA = D and Its Applications

An efficient method based on the projection theorem,the generalized singular value decompositionand the canonical correlation decomposition is presented to find the least-squares solution with the minimum-norm for the matrix equation A~TXB B~TX~TA=D.Analytical solution to the matrix equation is also derived.Furthermore,we apply this result to determine the least-squares symmetric and sub-antisymmetric solution ofthe matrix equation C~TXC=D with minimum-norm.Finally,some numerical results are reported to supportthe theories established in this paper.     

矩阵方程A1X1B1＋A2X2B2＋…＋AlXlBl=C的中心对称解及其最佳逼近

设矩阵X=(xij)∈R ,如果xij=xn+1-i,n+1-j(i,j=1,2,…,n),则称X是中心对称矩阵.该文构造了一种迭代法求矩阵方程A1X1B1+A2X2B2+…+AlXlBl=C的中心对称解组(其中[X1,X2,…,Xl]是实矩阵组).当矩阵方程相容时,对任意初始的中心对称矩阵组[X1(0),X2(0),…,Xl(0)],在没有舍入误差的情况下,经过有限步迭代,得到它的一个中心对称解组,并且,通过选择一种特殊的中心对称矩阵组,得到它的最小范数中心对称解组.另外,给定中心对称矩阵组[X1,X2,…,X1],通过求矩阵方程A1X1B1+A2X2B2+…+AlXlBl=C(其中G=C-A1X1B1-A2X2B2-…-AlXlBl)的中心对称解组,得到它的最佳逼近中心对称解组.实例表明这种方法是有效的.     

On The Maximal-Like Solution of Matrix Equation X ＋A^＊X^-2A=I^＊

In this paper,we study several iterative methods for finding the maximal-like solution of the matrix equation X A~*X~(-2)A=I,and deduce some properties of the maximal-like solution with these methods.     

矩阵方程A^TXB＋B^TX^TA=D的极小范数最小二乘解的迭代解法

1 引言 设R^m×n表示m×n实矩阵的全体，A^T表示矩阵A的转置，R（A）和N（A）分别表示矩阵A的值域和零空间，A^＋表示矩阵A的Moore—Penrose广义逆，A×B表示矩阵A与B的Kronecker乘积，     

Comments on “Finite iterative algorithms for the reflexive and anti-reflexive solutions of the matrix equation A1X1B1+A2X2B2=C”

This note points out some technical problems in the proofs of some lemmas in the above-mentioned paper, and presents their corresponding corrections. Nevertheless, the main results of that paper are still true.     

矩阵方程组$AX=B,XC=D$的Hermitian自反(反Hermitian自反)最小二乘解及其最佳逼近

In this paper,the Hermitian reflexive（Anti-Hermitian reflexive）least-squares so-lutions of matrix equations（AX = B,XC = D）are considered.With special properties of partitioned matrices and Hermitian reflexive（Anti-Hermitian reflexive）matrices,the general expression of the solution is obtained.Moreover,the related optimal approximation problem to a given matrix over the solution set is considered.     

Fast enclosure for the minimum norm least squares solution of the matrix equation AXB = C

Fast algorithms for enclosing the minimum norm least squares solution of the matrix equation AXB = C are proposed. To develop these algorithms, theories for obtaining error bounds of numerical solutions are established. The error bounds obtained by these algorithms are verified in the sense that all the possible rounding errors have been taken into account. Techniques for accelerating the enclosure and obtaining smaller error bounds are introduced. Numerical results show the properties of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解

本文研究了矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解问题.利用矩阵的Kronecker积和Moore-Penrose广义逆方法,得到了矩阵方程AXB+CY D=E的三对角中心对称极小范数最小二乘解的表达式.     

四元数矩阵方程(AXB,CXD)=(E,F)的最小范数最小二乘Hermitian解

提出了研究四元数矩阵方程(AXB, CXD)=(E, F)的最小范数最小二乘Hermitian解的一个有效方法.首先应用四元数矩阵的实表示矩阵以及实表示矩阵的特殊结构,把四元数矩阵方程转化为相应的实矩阵方程,然后求出四元数矩阵方程(AXB, CXD)=(E, F)的最小二乘Hermitian解集,进而得到其最小范数最小二乘Hermitian解.所得到的结果只涉及实矩阵,相应的算法只涉及实运算,因此非常有效.最后的两个数值例子也说明了这一点.     

Developing the CGLS algorithm for the least squares solutions of the general coupled matrix equations

In the present paper, we consider the minimum norm solutions of the general least squares problem By developing the conjugate gradient least square (CGLS) method, we construct an efficient iterative method to solve this problem. The constructed iterative method can compute the solution group of the problem within a finite number of iterations in the absence of roundoff errors. Also it is shown that the method is stable and robust. Finally, by some numerical experiments, we demonstrate that the iterative method is effective and efficient. Copyright © 2013 John Wiley & Sons, Ltd.     

Least‐squares solutions of matrix inverse problem for bi‐symmetric matrices with a submatrix constraint

An n × n real matrix A = (a_ij)_{n × n} is called bi‐symmetric matrix if A is both symmetric and per‐symmetric, that is, a_ij = a_ji and a_ij = a_n+1?1,n+1?i (i, j = 1, 2,..., n). This paper is mainly concerned with finding the least‐squares bi‐symmetric solutions of matrix inverse problem AX = B with a submatrix constraint, where X and B are given matrices of suitable sizes. Moreover, in the corresponding solution set, the analytical expression of the optimal approximation solution to a given matrix A^* is derived. A direct method for finding the optimal approximation solution is described in detail, and three numerical examples are provided to show the validity of our algorithm. Copyright © 2007 John Wiley & Sons, Ltd.     

On the Diophantine System a^2 ＋ b^2 = c^3 and a^x ＋ b^y = c^z for b is an Odd Prime

Let a, b and c be fixed coprime positive integers. In this paper we prove that if a^2 ＋ b^2 = c^3 and b is an odd prime, then the equation a^x ＋ b^y = c^z has only the positive integer solution （x, y, z） = （2,2,3）.     

An efficient iterative method for solving the matrix equation AXB + CYD = E

This paper presents an iterative method for solving the matrix equation AXB + CYD = E with real matrices X and Y. By this iterative method, the solvability of the matrix equation can be determined automatically. And when the matrix equation is consistent, then, for any initial matrix pair [X₀, Y₀], a solution pair can be obtained within finite iteration steps in the absence of round‐off errors, and the least norm solution pair can be obtained by choosing a special kind of initial matrix pair. Furthermore, the optimal approximation solution pair to a given matrix pair [X?, ?] in a Frobenius norm can be obtained by finding the least norm solution pair of a new matrix equation AX?B + C?D = ?, where ? = E ? AX?B ? C?D. The given numerical examples show that the iterative method is efficient. Copyright © 2005 John Wiley & Sons, Ltd.