Research Article  Open Access
Hongcai Yin, Huamin Zhang, "Least Squares Based Iterative Algorithm for the Coupled Sylvester Matrix Equations", Mathematical Problems in Engineering, vol. 2014, Article ID 831321, 8 pages, 2014. https://doi.org/10.1155/2014/831321
Least Squares Based Iterative Algorithm for the Coupled Sylvester Matrix Equations
Abstract
By analyzing the eigenvalues of the related matrices, the convergence analysis of the least squares based iteration is given for solving the coupled Sylvester equations and in this paper. The analysis shows that the optimal convergence factor of this iterative algorithm is 1. In addition, the proposed iterative algorithm can solve the generalized Sylvester equation . The analysis demonstrates that if the matrix equation has a unique solution then the least squares based iterative solution converges to the exact solution for any initial values. A numerical example illustrates the effectiveness of the proposed algorithm.
1. Introduction
Matrix equations arise in systems and control, such as Lyapunov matrix equation and Riccati equation. How to solve these matrix equations becomes a major field of the matrix computations [1–4]. Main points of this field are the decompositions and transformations of the matrices, eigenvalues and eigenvectors, and Krylov subspace [5–8]. Other points contain the algorithms and the convergence analysis. The algorithms provided a set of operation steps, and according to these steps it can find a solution of a matrix equation within finite steps in given error bounds [9–11]. The convergence analysis offered more details of an algorithm, and in general, these details indicated some new research areas [12–15].
The direct method and the indirect method are the two main approaches of solving the matrix equations [16–18]. With the development of the requirement of the computation and the matrix theory, the indirect method or iterative method becomes the main approach of solving the matrix equations [19, 20].
Iterative method is active in studying other engineering problems. Particularly, the iterative method can be used to identify systems [21–23] and estimate parameter of systems [24–28]. For example, iterative method is useful in the identification [29–32] and parameter estimation [33–36] of linear and nonlinear systems [37–46].
The Jacobi and GaussSeidel iterative methods were discussed in the literature. By extending the Jacobi and GaussSeidel iterative methods, Ding and his coworkers recently presented a large family of the least squares based iterative methods for solving the matrix equations and [47–49]. It has been proved that these least squares based iterative solutions always converge fast to the exact ones as long as the unique solutions exist. But the range of the convergence factor is still open. Motivated by the importance of this algorithm, we develop a new way to prove the convergence of the least squares based iterative algorithm for the coupled Sylvester matrix equations and . According to the new proof, we obtain the optimal convergence factor of this iterative algorithm and extend the iterative algorithm to solve the generalized Sylvester matrix equation . The algorithm in this paper can be extended to other more general and complex matrix equations [50–52].
The paper is organized as follows. Section 2 gives some preliminaries. Section 3 presents a new proof to the least squares iterative algorithm for solving the coupled Sylvester matrix equations and . Section 4 revises this algorithm to solve the equation . Section 5 gives an example to illustrate the effectiveness of the proposed results. Finally, we offer some concluding remarks in Section 6.
2. Basic Preliminaries
Some symbols and lemmas are introduced first. is the identity matrix of size . is an identity matrix with an appropriate size. denotes the eigenvalue set of matrix . denotes the matrix determinant. denotes the Frobenius norm of and is defined as . For an matrix denotes an dimensional vector formed by represents the Kronecker product of matrices and . A formula involved the vecoperator and the Kronecker product is [53].
If matrix is invertible, then is symmetric and idempotent. To be more specific, we have the following lemma.
Lemma 1. For the symmetric matrix , there exists an orthogonal matrix such that Moreover, .
3. The Coupled Sylvester Matrix Equations
In this section, we will prove the convergence of the least squares iterative algorithm for solving the following coupled Sylvester matrix equations: where , , and are known and are to be determined. By using the hierarchical identification principle, the least squares based iterative algorithm for solving (4) is given by [47] Here, and To initialize the algorithm, we take and as some small real matrices, for example, with being an matrix whose elements are all 1.
Theorem 2. If (4) has unique solutions and , then for any initial values and the iterative solutions and given by iteration in (5) converge to and ; that is, or the error matrices and converge to zero; in this case, the optimal convergence factor is .
Proof. Define two error matrices
Using (4) and (5) gives
Expanding (9) gives
Simplifying (10) gives
Using the formula gives
Set
Using these symbols, a compact form of (12) is
Set
The eigenvalues of inside the unit circle will complete the proof. Let be an eigenvalue of ; we will show that , or, if , then .
Consider the following characteristic polynomial of :
It is not hard to show that by calculation. Since
it follows from the formula
that
On the other hand, since (4) has a unique solution, it follows that the matrix
is invertible. A determinant expansion shows that
So it gives . Since
expanding it gives
Setting , it can be rewritten as
According to Lemma 1, there exists an orthogonal matrix such that
Using (25) can be manipulated to get
If , then there exist the nonzero vectors that satisfy , which can be written as
Since and are orthogonal matrices, it follows that
where and . Then from (28), it gives
Using (26) gives
According to (29) and (30), (27) can be manipulated to get
So it gives
Suppose that , where , . From Schur decomposition theorem, there exists a decomposition , where is an orthogonal matrix, , and is a strictly upper triangular matrix. Then from (23), it gives
It follows that . Since , we obtain , . Thus, .
Next, we determine the optimal convergence factor. From and
one gets . Set . Taking absolute values of these eigenvalues, the optimal convergence factor satisfies
Equation (35) is equivalent to . Solving it gives . The proof is completed.
4. The Generalized Sylvester Matrix Equation AXB + CXD = F
In this section, we use iteration in (5) to solve the generalized Sylvester matrix equation. Consider the following equation: where , , and are given constant matrices and is the unknown matrix to be solved. The following conclusion is obvious.
Equation (36) has a unique solution if and only if In this case, the unique solution is given by .
Setting and , (36) can be equivalently expressed as If then (38) has a unique solution. It is easy to show that if or , then (38) is equivalent to (36). Next, we show that if or , then (39) is equivalent to (37). That is, we have the following determinant result: According to Theorem 2, (38) can be solved by iteration in (5), and from or , (36) can be solved.
5. Example
In this section, an example is offered to illustrate the convergence of the proposed iterative algorithm.
Example 1. Consider the coupled Sylvester matrix equations in the form of (4) with
The unique solution is found to be
Taking as the initial iterative values and using iteration (5) to compute and , the iterative values of and are shown in Table 1 with the relative error
The effect of changing the convergence factor is illustrated in Figure 1.

From Table 1 and Figure 1, we find that the relative error goes to zero with the increasing of the iterative times. This shows that the proposed iterative algorithm is effective. In addition, Figure 1 shows that the optimal convergence factor . This indicates that the result of the optimal convergence suggested in this paper is correct.
6. Conclusions
This paper proved the convergence of the least squares based iterative algorithm of the coupled Sylvester matrix equations and , and the proof determined the range of the convergence factor and the optimal convergence factor. The suggested algorithm can also be used to solve the generalized Sylvester equation . An example indicated that the iterative solution given by the least squares based iterative algorithm converges fast to its exact solution under proper conditions.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgment
This work was supported by the National Natural Science Foundation of China (no. 6110218).
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Copyright
Copyright © 2014 Hongcai Yin and Huamin Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.