Article

# Linear restriction problem of Hermitian reflexive matrices and its approximation

College of Mathematics and Econometrics, Hunan University, Changsha 410082, PR China

Applied Mathematics and Computation (Impact Factor: 1.55). 06/2008; 200(1):341-351. DOI: 10.1016/j.amc.2007.11.020 Source: DBLP

**ABSTRACT**

In this paper, we consider a linear restriction problem of Hermitian reflexive matrices and its approximation. By using the properties and structure of Hermitian reflexive matrices and the special properties of reflexive vectors and anti-reflexive vectors, we convert the linear restriction problem to an equivalence problem trickily, which is a special feature of this paper and is a different method from other articles. Then we solve this problem completely and also obtain its optimal approximate solution. Moreover, an algorithm provided for it and the numerical examples show that the algorithm is feasible.

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**ABSTRACT:**In this paper, The Hermitian reflexive solutions and the anti-Hermitian reflexive solutions of matrix equations AX = B, XC = D are considered. With special properties of partitioned matrices and Hermitian reflexive (anti-Hermitian reflexive) matrices, the necessary and sufficient conditions for the solvability and the general expression of the solution are obtained. Moreover, the related optimal approximation problem to a given matrix over the solution set is considered.Energy Procedia 12/2012; 17:1591–1597. DOI:10.1016/j.egypro.2012.02.286 - [Show abstract] [Hide abstract]

**ABSTRACT:**Let P and Q be two generalized reflection matrices, i.e, P=PH, P2=I and Q=QH, Q2=I. An n×n matrix A is said to be generalized reflexive (generalized anti-reflexive) with respect to the matrix pair (P;Q) if A=PAQ (A=-PAQ). It is obvious that any n×m matrix is also a generalized reflexive with respect to the matrix pair (In;Im). By extending the conjugate gradient least square (CGLS) approach, the present paper treats two iterative algorithms to solve the system of matrix equationsF1(X)=A1,F2(X)=A2, ⋮⋮⋮Fm(X)=Am,(including the Sylvester and Lyapunov matrix equations as special cases) over the generalized reflexive and anti-reflexive matrices, where F1,F2,⋯,Fm are the linear operators from Cn×n onto Cri×si and Ai∈Cri×si for i=1,2,⋯,m. When this system is consistent over the generalized reflexive (generalized anti-reflexive) matrix, it is proved that the first (second) iterative algorithm converges to a generalized reflexive (generalized anti-reflexive) solution for any initial generalized reflexive (generalized anti-reflexive) matrix. Also the first (second) iterative algorithm can obtain the the least Frobenius norm generalized reflexive (generalized anti-reflexive) solution for special initial generalized reflexive (generalized anti-reflexive) matrix. Furthermore, the optimal approximation generalized reflexive (generalized anti-reflexive) solution to a given generalized reflexive (generalized anti-reflexive) matrix can be derived by finding the least Frobenius norm generalized reflexive (generalized anti-reflexive) solution of a new system of matrix equations. Finally, we test the proposed iterative algorithms and show their effectiveness using numerical examples.Linear Algebra and its Applications 12/2012; 437(11):2793–2812. DOI:10.1016/j.laa.2012.07.004 · 0.94 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**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.Mathematical Methods in the Applied Sciences 11/2014; 37(17). DOI:10.1002/mma.3017 · 0.92 Impact Factor

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