Linear and Nonlinear Matrix EquationsView this Special Issue
Iterative Solution to a System of Matrix Equations
An efficient iterative algorithm is presented to solve a system of linear matrix equations , with real matrices and . By this iterative algorithm, the solvability of the system can be determined automatically. When the system is consistent, for any initial matrices and , a solution can be obtained in the absence of roundoff errors, and the least norm solution can be obtained by choosing a special kind of initial matrix. In addition, the unique optimal approximation solutions and to the given matrices and in Frobenius norm can be obtained by finding the least norm solution of a new pair of matrix equations , where , . The given numerical example demonstrates that the iterative algorithm is efficient. Especially, when the numbers of the parameter matrices are large, our algorithm is efficient as well.
Throughout the paper, we denote the set of all real matrix by , the transpose matrix of by , the identity matrix of order by , the Kronecker product of and by , the vector formed by the vertical concatenation of the respective columns of a matrix by , the trace of a matrix by , and the Frobenius norm of a matrix by where .
In this paper, we consider the following two problems.
Problem 1. For the given matrices , , , , , , , , , and , find and such that
Problem 2 is to find the optimal approximation solutions to the given matrices in the solution set of Problem 1. It occurs frequently in experiment design (see, for instance, ). In the recent years, the matrix optimal approximation problem has been studied extensively (e.g., [2–13]).
The research on solving matrix equation pair has been actively ongoing for the last 30 years or more. For instance, Mitra  gave conditions for the existence of a solution and a representation of the general common solution to , . Shinozaki and Sibuya  and van der Woude  discussed conditions for the existence of a common solution to , . Navarra et al.  derived sufficient and necessary conditions for the existence of a common solution to , . Yuan  obtained an analytical expression of the least-squares solutions of , by using the generalized singular value decomposition (GSVD) of matrices. Dehghan and Hajarian  presented some examples to show a motivation for studying the general coupled matrix equations , , and  constructed an iterative algorithm to solve the general coupled matrix equations , . Wang [19, 20] gave the centrosymmetric solution to the system of quaternion matrix equations , . Wang  also solved a system of matrix equations over arbitrary regular rings with identity.
Recently, some finite iterative algorithms have also been developed to solve matrix equations. Ding et al. [22, 23] and Xie et al. [24, 25] studied the iterative solutions of matrix equations and and generalized Sylvester matrix equations and . They presented a gradient based and a least-squares based iterative algorithms for the solution. Li et al. [26, 27] and Zhou et al. [28, 29] considered iterative method for some coupled linear matrix equations. Deng et al.  studied the consistent conditions and the general expressions about the Hermitian solutions of the matrix equations and designed an iterative method for its Hermitian minimum norm solutions. Li and Wu  gave symmetric and skew-antisymmetric solutions to certain matrix equations , over the real quaternion algebra H. For more studies on iterative algorithms on coupled matrix equations, we refer to [3, 10–12, 17, 32–37]. Peng et al.  presented iterative methods to obtain the symmetric solutions of , . Sheng and Chen  presented a finite iterative method; when , is consistent. Liao and Lei  presented an analytical expression of the least-squares solution and an algorithm for , with the minimum norm. Peng et al.  presented an algorithm for the least-squares reflexive solution. Dehghan and Hajarian  presented an iterative algorithm for solving a pair of matrix equations , over generalized centrosymmetric matrices. Cai and Chen  presented an iterative algorithm for the least-squares bisymmetric solutions of the matrix equations , . Yin and Huang  presented an iterative algorithm to solve the least squares generalized reflexive solutions of the matrix equations , .
However, to our knowledge, there has been little information on finding the solutions to the system (1) by iterative algorithm. In this paper, an efficient iterative algorithm is presented to solve the system (1) for any real matrices . The suggested iterative algorithm, automatically determines the solvability of equations pair (1). When the pair of equations is consistent, then, for any initial matrices and , the solution can be obtained in the absence of round errors, and the least norm solution can be obtained by choosing a special kind of initial matrix. In addition, the unique optimal approximation solutions and to the given matrices and in Frobenius norm can be obtained by finding the least norm solution of a new pair of matrix equations , , where , . The given numerical examples demonstrate that our iterative algorithm is efficient. Especially, when the numbers of the parameter matrices are large, our algorithm is efficient as well while the algorithm of  is not convergent. That is, our algorithm has merits of good numerical stability and ease to program.
The rest of this paper is outlined as follows. In Section 2, we first propose an efficient iterative algorithm for solving Problem 1; then we give some properties of this iterative algorithm. We show that the algorithm can obtain a solution group (the least Frobenius norm solution group) for any (special) initial matrix group in the absence of roundoff errors. In Section 3, a numerical example is given to illustrate that our algorithm is quite efficient.
In this section, we present the iterative algorithm for the consistence of the system (1).
Algorithm 3. (1) Input matrices , , , , , , , , , , , and (where , are any initial matrices).
(3) If (), then stop. Otherwise,
(4) Calculate Go to (3).
Lemma 4. In Algorithm 3, the choice of makes reach a minimum and and orthogonal to each other.
Theorem 5. Algorithm 3 is bound to be convergent.
Lemma 6 (see ). Suppose that the consistent system of linear equations has a solution ; then is the least Frobenius norm solution of the system of linear equations.
Theorem 7. Assume that the system (1) is consistent. Let , be initial matrices where , are any initial matrices, or, especially, , ; then the solution generated by Algorithm 3 is the least Frobenius norm solution to (1).
Proof. If (1) is consistent, from , , using Algorithm 3, we have the iterative solution pair of (1) as the following:
We know that (1) is equivalent to the system From (10) and (11) we have where is the column space of matrix *.
Considering Lemma 6, with the initial matrices , , where , are arbitrary, or, especially, and , then the solution pair generated by Algorithm 3 is the least Frobenius norm solution of the matrix equations (1).
Suppose that Problem 1 is consistent. Obviously the solution set of (1) is nonempty. For given matrices pair , , we can write Let , , , and . Then Problem 2 is equivalent to find the least Frobenius norm solution pair of the system which can be obtained using Algorithm 3 with the initial matrix pair , where and are arbitrary, or especially, , , and the solution of the matrix optimal approximation Problem 2 can be represented as , .
3. An Example
In this section, we show a numerical example to illustrate the efficiency of Algorithm 3. All computations are performed by MATLAB 7. For the influence of the error of calculation, we consider the matrix as a zero matrix if .
Example 1. Consider the solution of the linear matrix equations where
In this example, the numbers of the parameter matrices are larger than the numbers of the parameter matrices in the example of . It can be verified that these matrix equations are consistent and have the solution as
This numerical example demonstrates that our algorithm has merits of good numerical stability and ease to program.
This research was supported by the Grants from the Key Project of Scientific Research Innovation Foundation of Shanghai Municipal Education Commission (13ZZ080), the National Natural Science Foundation of China (11171205), the Natural Science Foundation of Shanghai (11ZR1412500), and the Nature Science Foundation of Anhui Provincial Education (ky2008b253, KJ2013A248).
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