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1.
在共轭梯度思想的启发下,本文给出了迭代算法求解约束矩阵方程AXB+CXD=F的对称解及其最佳逼近.应用迭代算法,矩阵方程AXB+CXD=F的相容性可以在迭代过程中自动判断.当矩阵方程AXB+CXD=F有对称解时,在有限的误差范围内,对任意初始对称矩阵X1,运用迭代算法,经过有限步可得到矩阵方程的对称解;选取合适的初始迭代矩阵,还可以迭代出极小范数对称解.而且,对任意给定的矩阵X0,矩阵方程AXB+CXD=F的最佳逼近对称解可以通过迭代求解新的矩阵方程A(X)B+C(X)D=(F)的极小范数对称解得到.文中的数值例子证实了该算法的有效性.  相似文献   

2.
周海林 《计算数学》2015,37(2):186-198
在共轭梯度思想的启发下,结合线性投影算子,给出迭代算法求解了线性矩阵方程AXB+CYD=E的M对称解[X,Y]及其最佳逼近.当矩阵方程AXB+CYD=E有M对称解时,应用迭代算法,在有限的误差范围内,对任意初始M对称矩阵对[X_,Y_1],经过有限步迭代可得到矩阵方程的M对称解;选取合适的初始迭代矩阵,还可得到极小范数M对称解.而且,对任意给定的矩阵对[X,Y],矩阵方程AXB+CYD=E的最佳逼近可以通过迭代求解新的矩阵方程AXB+CYD=E的极小范数M对称解得到.文中的数值例子证实了该算法的有效性.  相似文献   

3.
杨家稳  孙合明 《数学杂志》2014,34(5):968-976
本文研究了Sylvester复矩阵方程A_1Z+ZB_1=c_1的广义自反最佳逼近解.利用复合最速下降法,提出了一种的迭代算法.不论矩阵方程A_1Z+ZB_1=C_1是否相容,对于任给初始广义自反矩阵Z_0,该算法都可以计算出其广义自反的最佳逼近解.最后,通过两个数值例子,验证了该算法的可行性.  相似文献   

4.
应用共轭梯度法,结合线性投影算子,给出迭代算法求解线性矩阵方程AXB+CXD=F在任意线性子空间上的约束解及其最佳逼近.当矩阵方程AXB+CXD=F有解时,可以证明,所给迭代算法经过有限步迭代可得到矩阵方程的约束解、极小范数解和最佳逼近.数值例子证实了该算法的有效性.  相似文献   

5.
该文建立了求矩阵方程AXB+CXD=F的中心对称最小二乘解的迭代算法.使用该算法不仅可以判断该矩阵方程的中心对称解的存在性,而且无论中心对称解是否存在,都能够在有限步迭代计算之后得到中心对称最小二乘解.选取特殊的初始矩阵时,可求得极小范数中心对称最小二乘解.同时,也能给出指定矩阵的最佳逼近中心对称矩阵.  相似文献   

6.
徐宜营  谢冬秀 《应用数学》2015,28(1):143-148
利用交替投影算法求解矩阵方程AXB=C的广义中心对称解,当矩阵方程AXB=C不相容时,利用Dykstra's交替投影算法来求其广义中心对称解的最佳逼近,数值结果表明该方法是行之有效的.  相似文献   

7.
一类矩阵方程的反中心对称最佳逼近解   总被引:3,自引:0,他引:3  
黄敬频 《大学数学》2005,21(1):68-73
利用矩阵的正交相似变换和广义奇异值分解,讨论了矩阵方程 AXB=C具有反中心对称解的充要条件,得到了解的具体表达式.然后应用Frobenius范数正交矩阵乘积不变性,在该方程的反中心对称解解集合中导出了与给定相同类型矩阵的最佳逼近解的表达式.  相似文献   

8.
研究线性矩阵方程AXB=C在闭凸集合R约束下的数值迭代解法.所考虑的闭凸集合R为(1)有界矩阵集合,(2)Q-正定矩阵集合和(3)矩阵不等式解集合.构造松弛交替投影算法求解上述问题,并用算子理论证明了由该算法生成的序列具有弱收敛性.给出了矩阵方程AXB=C求对称非负解和对称半正定解的数值算例,大量数值实验验证了该算法的可行性和高效性,并说明该算法与交替投影算法和谱投影梯度算法比较在迭代效率上的明显优势.  相似文献   

9.
通过求极值的方法,得到了求矩阵方程AHXA=B与已知矩阵最佳逼近的反H erm ite-自反解的存在的充要条件.  相似文献   

10.
一类矩阵方程的埃尔米特自反最小二乘解   总被引:1,自引:1,他引:0  
利用埃尔米特自反矩阵的表示定理和矩阵的拉直方法,研究了矩阵方程$AX+BY=C$的埃尔米特自反最小二乘问题,进一步,给出了方程在埃尔米特自反矩阵集合中可解的充分必要条件,得到解的一般表达式,最后,对任意给定的一对复矩阵,得到了其相关最佳逼近问题解的表达式.  相似文献   

11.
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.  相似文献   

12.
The consistent conditions and the general expressions about the Hermitian solutions of the linear matrix equations AXB=C and (AX, XB)=(C, D) are studied in depth, where A, B, C and D are given matrices of suitable sizes. The Hermitian minimum F‐norm solutions are obtained for the matrix equations AXB=C and (AX, XB)=(C, D) by Moore–Penrose generalized inverse, respectively. For both matrix equations, we design iterative methods according to the fundamental idea of the classical conjugate direction method for the standard system of linear equations. Numerical results show that these iterative methods are feasible and effective in actual computations of the solutions of the above‐mentioned two matrix equations. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

13.
This paper develops a gradient based and a least squares based iterative algorithms for solving matrix equation AXB + CXTD = F. The basic idea is to decompose the matrix equation (system) under consideration into two subsystems by applying the hierarchical identification principle and to derive the iterative algorithms by extending the iterative methods for solving Ax = b and AXB = F. The analysis shows that when the matrix equation has a unique solution (under the sense of least squares), the iterative solution converges to the exact solution for any initial values. A numerical example verifies the proposed theorems.  相似文献   

14.
An iteration method is constructed to solve the linear matrix equation AXB=C over symmetric X. By this iteration method, the solvability of the equation AXB=C over symmetric X can be determined automatically, when the equation AXB=C is consistent over symmetric X, its solution can be obtained within finite iteration steps, and its least-norm symmetric solution can be obtained by choosing a special kind of initial iteration matrix, furthermore, its optimal approximation solution to a given matrix can be derived by finding the least-norm symmetric solution of a new matrix equation . Finally, numerical examples are given for finding the symmetric solution and the optimal approximation symmetric solution of the matrix equation AXB=C.  相似文献   

15.
In this paper, an iterative algorithm is constructed for solving linear matrix equation AXB = C over generalized centro-symmetric matrix X. We show that, by this algorithm, a solution or the least-norm solution of the matrix equation AXB = C can be obtained within finite iteration steps in the absence of roundoff errors; we also obtain the optimal approximation solution to a given matrix X 0 in the solution set of which. In addition, given numerical examples show that the iterative method is efficient.  相似文献   

16.
The matrix least squares (LS) problem minx ||AXB^T--T||F is trivial and its solution can be simply formulated in terms of the generalized inverse of A and B. Its generalized problem minx1,x2 ||A1X1B1^T + A2X2B2^T - T||F can also be regarded as the constrained LS problem minx=diag(x1,x2) ||AXB^T -T||F with A = [A1, A2] and B = [B1, B2]. The authors transform T to T such that min x1,x2 ||A1X1B1^T+A2X2B2^T -T||F is equivalent to min x=diag(x1 ,x2) ||AXB^T - T||F whose solutions are included in the solution set of unconstrained problem minx ||AXB^T - T||F. So the general solutions of min x1,x2 ||A1X1B^T + A2X2B2^T -T||F are reconstructed by selecting the parameter matrix in that of minx ||AXB^T - T||F.  相似文献   

17.
The purpose of this article is to solve two problems related to solutions of a consistent complex matrix equation AXB = C : (I) the maximal and minimal ranks of solution to AXB = C , and (II) the maximal and minimal ranks of two real matrices X 0 and X 1 in solution X = X 0 + iX 1 to AXB = C . As applications, the maximal and minimal ranks of two real matrices C and D in generalized inverse (A + iB)- = C + iD of a complex matrix A + iB are also examined.  相似文献   

18.
The problem of generating a matrix A with specified eigen‐pair, where A is a symmetric and anti‐persymmetric matrix, is presented. An existence theorem is given and proved. A general expression of such a matrix is provided. We denote the set of such matrices by ??????En. The optimal approximation problem associated with ??????En is discussed, that is: to find the nearest matrix to a given matrix A* by A∈??????En. The existence and uniqueness of the optimal approximation problem is proved and the expression is provided for this nearest matrix. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

19.
In this article we give, in terms of so-called Berezin symbols, some necessary conditions for the solvability of the Riccati equation XAX+XB-CX-D=0XAX+XB-CX-D=0 on the set T{\cal T} of all Toeplitz operators on the Hardy space H2(\Bbb D)H^2({\Bbb D}) .  相似文献   

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