[Retracted] The MGHSS for Solving Continuous Sylvester Equation AX + XB = C

Feng, Yu-Ye; Wu, Qing-Biao; Jing, Xue-Na

doi:https://doi.org/10.1155/2021/6615728

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Volume 2021 | Article ID 6615728 | https://doi.org/10.1155/2021/6615728

[Retracted] The MGHSS for Solving Continuous Sylvester Equation AX + XB = C

Yu-Ye Feng,¹Qing-Biao Wu,¹and Xue-Na Jing²

Academic Editor: Zhihan Lv

Received25 Dec 2020

Revised18 Jan 2021

Accepted24 Jan 2021

Published11 Feb 2021

Abstract

This paper proposes the modified generalization of the HSS (MGHSS) to solve a large and sparse continuous Sylvester equation, improving the efficiency and robustness. The analysis shows that the MGHSS converges to the unique solution of AX + XB = C unconditionally. We also propose an inexact variant of the MGHSS (IMGHSS) and prove its convergence under certain conditions. Numerical experiments verify the efficiency of the proposed methods.

1. Introduction

This paper focuses on solving the continuous Sylvester equation defined as

Firstly, we assume A, B, and C are large and sparse matrices, and , respectively; then, equation (1) is a large sparse equation. Then, we assume that both A and B are positive semidefinite, and at least one of them is positive definite and at least one of A and B is non-Hermitian.

Under the above assumptions, it is sufficient to prove that equation (1) has a unique solution [1]. When and C is Hermitian, the continuous Sylvester equation (1) is a special case of the continuous Lyapunov equation [2]. indicates the conjugate transpose of . The continuous Sylvester equation (1) has numerous applications in many fields, such as in control and system theory [3], signal processing [4] and image restoring [5], and stability of linear systems [6]. Many authors considered such a linear matrix equation problem and concentrated on accelerating the HSS iteration on the continuous Sylvester equation (1) [7–10], which was first proposed in [11].

By using Kronecker Product, equation (1) is rewritten as the following linear equation:where , and and are the columns of and , respectively. represents the identity matrix whose order is m, and is the Kronecker Product. However, when the size of the linear equation (2) is large, it is ill-conditioned to solve it directly.

Before the appearance of the HSS, direct algorithms were usually used to solve the continuous Sylvester equations, such as Hessenberg–Schur and Bartels–Stewart methods [1, 12]. However, they were only applicable to small-sized continuous Sylvester equations. For large and sparse continuous Sylvester equations, iteration methods were used, such as the gradient-based algorithm [13–18]. Such an iteration method has been studied in recent years, taking advantage of the low-rank and sparsity of right-hand C in equation (1).

In 2011, Bai proposed the HSS to solve large sparse continuous Sylvester equation [11]. Since then, many HSS-based iteration methods [19–23] have been widely studied and achieved certain results in solving the continuous Sylvester equation. In the same direction of the research, this paper presents a modified GHSS method to solve the continuous Sylvester equations. Besides, there are numerical research studies which focus on solving complex Sylvester matrix equation with large size, based on the HSS method for solving (1) which is proposed in [11]. Modified HSS (MHSS) iteration method [24] and preconditioned MHSS (PMHSS) method [9] for solving complex Sylvester matrix equation were presented, and then, the generalized MHSS (GMHSS) method [10] is also based on the MHSS iteration method by parameterizing it. In recent years, some neural network methods for time-varying complex Sylvester equation were proposed [25, 26]. Many methods are updated to solve various types of Sylvester equation. In this paper, we focus on solving continuous Sylvester equation with non-Hermitian and positive definite/semidefinite matrices.

Firstly, the Hermitian and skew-Hermitian splitting method in equation (1) is used [27]. Let the Hermitian part of be and the skew-Hermitian part of be .

HSS [2]: the following equations are computed with an initial matrix until , where is converged:where is a constant.

HSS for solving continuous Sylvester equations [11]: is computed with an initial matrix through the following equations until satisfies the stopping criterion:where are constants. The iteration matrix for solving continuous Sylvester equation is and , and it converges to the exact solution: and .

GHSS [28]: similar to HSS, the following equations are computed until is converged:where is a constant.

GHSS for solving continuous Sylvester equations [8]: in GHSS, to solve continuous Sylvester equations, , where is computed through the following scheme until satisfies the stopping criterion:where are constants. The iteration matrix is and , , and . The convergence factor is given by the spectral radius of the matrix , bounded by , and the optimal parameter , where and .

As an extension of those iterative schemes, this paper proposes the modified GHSS to solve the continuous Sylvester equations and proves its convergence. Section 2 presents the detailed MGHSS and analyzes its convergence for the continuous Sylvester equation. IMGHSS iteration is described in Section 3. In Section 4, we take two examples into experiments from previous experiments in other HSS-based iteration methods. The results show that the proposed MGHSS is more effective in both the iteration and runtime. Section 5 concludes this paper with several thoughts.

In the remainder of this paper, especially in the proof of the convergence property of MGHSS, we rewrite the continuous Sylvester equation (1) as the linear-vector form firstly. When the vector sequence converges the vector , it is equivalent as the corresponding matrix sequence converges to the corresponding matrix , where the corresponding vector is the concatenated columns of the correspondent matrix .

2. The Modified GHSS Iteration Method

This paper proposes a modified GHSS, and a direct solver can be used to solve each step of the inner iteration.

Firstly, the GHSS splits and into generalized Hermitian and skew-Hermitian parts [8]:where and are the skew-Hermitian part of and , respectively, and and are the Hermitian part of and , respectively.

With matrix splitting and GHSS [8], and are decomposed into two skew-Hermitian matrices: and . Then, and are rewritten:

Accordingly, equation (1) is rewritten in the matrix equation as follows:

It is known that no common eigenvalue exists between and and between and . Thus, there exist unique solutions in both two fixed-point matrix equations. Based on this, the MHSS iteration is conducted to solve equation (1).

MGHSS: is computed with an initial matrix through equation (10). The process stops when satisfies the stopping criterion:

Lemma 1. (see [11]). Let and denote two split of matrix , where . Then, a two-step iteration sequence is defined as follows:where and , is an initial matrix. Then,

This is rewritten as vector form as follows:

Furthermore, when the spectral radius , converges to for all .

Lemma 2. (see [29, 30]). Let , where . When is positive semidefinite and ,

When is positive definite and ,

Theorem 1. Let , wherewhere and are skew-Hermitian matrices and and are symmetric positive semi-definite. The unique solution of (1) obtained by the MGHSS converges to the unique exact solution when either and are symmetric positive definite.

Proof. By using the Kronecker product, we can rewrite (10) as follows:Then, it can reformulated as follows by eliminating :whereBy a similar transformation of the components of the iteration matrix , we obtainDenote and , then and are positive semidefinite, and clearly,From Lemma 2, we haveRespectively, if and are positive definite, the above inequalities are strict. Thus, when either and is symmetric positive definite, we havefor any , completing the proof. Accordingly, the MGHSS unconditionally converges to the exact solution of equation (1).
From the results in Chapter 4 in [31], denote the inner product . We know under the above definitions of , , , , , , and , the following inequalities are satisfied:The upper bound on the spectral radius of the iteration matrix is minimized with the parameter , which is defined as follows:

It indicates that finding the optimal parameter is challenging but necessary because it relies on the spectral information of the selected iteration matrix.

In the following section, the improved MGHSS, the inexact MGHSS (IMGHSS) is introduced.

3. The Inexact MGHSS Method

Unlike MGHSS (Section 3) that solves the two fixed-point equations by direct algorithms, IMGHSS, presented in this section, iteratively solves the two subsystems. Similar to the IHSS [32, 33] for solving linear systems and IHSS for solving Sylvester equation [11], the process of the IMGHSS iteration scheme for solving a continuous Sylvester equation is as follows. Here, we denote as Frobenius norm.

IMGHSS: is an initial matrix. In the IMGHSS algorithm, the solution of equation (1) is derived as the following:while (stopping condition = = false)is approximated to that is solved by the residual of the iteration satisfies :is approximated to that is solved by the residual of the iteration which satisfies :end.

In the scheme of IMGHSS, and are prescribed tolerances that control the accuracy of the inner iterations. In implements, the values of do not necessarily decrease to zero when k increases so long as we choose suitable values of it, and we can also ensure the convergence of the IMGHSS.

In [11], the convergence of the two-step iteration was explored. We analyze the convergence of IMGHSS as follows.

Theorem 2. Let denote an iteration sequence produced by IMGHSS and denote the exact solution of equation (1). Then, under the assumption that the conditions of Theorem 1 are met, it holdswhere the norm is defined as follows:for any matrix , and the constant and are given by , and , where and .
In particular, when , the iteration sequence converges to , where and .

Proof. The IMGHSS can be rewritten in the matrix-vector form with the notations in Theorem 1 and the Kronecker product as follows:where and . is such that the residual satisfies and is such that the residual satisfies .
Equation (34) is the IMGHSS for solving (2), with . Then, based on Theorem 2 in [11], we havewhere the constants are given byand .
For a vector that consists of the concatenated columns of , .
Thus, the following is obtained:The proof is completed.

According to Theorem 2, it is important to choose a suitable value of the tolerance to control the IMGHSS’s convergence. Still, analyzing the optimal tolerances is challenging.

4. Numerical Analysis

The feasibility and efficiency of the MGHSS are verified in several examples in this section. The proposed method was compared with other methods in terms of the number of iteration steps () and the computational time ( [sec]). The numerical analysis was conducted in Matlab on Intel dual-core CPU (2.5 GHz) and 8 GB RAM. Zero matrix was used as an initial guess, and the termination condition is defined as

Example 1. The continuous Sylvester equation (1) with is considered, and the matrices are as follows:, where I represents the identity matrix.
For the practical iteration parameters of all those iteration methods, we take . Also, all the subproblems are exactly solved by the Bartels–Stewart method [3] in each step of the HSS, GHSS, and MGHSS. Tables 1 and 2 compare HSS and MGHSS in solving the continuous Sylvester equation (1) in terms of and . The optimal values of were analyzed in Tables 3 and 4, respectively, for HSS/MGHSS and GHSS/MGHSS.
According to Table 3 and Table 4, as the rank of equation (2) is incremented, and of the HSS, GHSS, and MGHSS are all decreased. In Figure 1, the logarithm versus iteration of the HSS, GHSS, and MGHSS methods () are shown in (a) and (b) when and , respectively. It shows the efficiency of the MGHSS method.

(a)

(b)

Example 2. The continuous Sylvester equation (1) with m = n and the matrices
and , where L is the strictly lower triangular matrix with ones in the lower triangle part and is a problem parameter specified in practical computations.
Table 5 shows that the MGHSS outperforms the GHSS and HSS in solving the continuous Sylvester equation. In Table 6, the continuous Sylvester equation in Example 2 are solved by the IMGHSS and MGHSS iteration methods and the results show that IMGHSS is much better than the MGHSS. Here, we set , and use the ADI method as the inner iteration scheme.

5. Conclusions

HSS-based methods have been widely used to solve the continuous Sylvester equations. In this paper, a modified generalization of the HSS method (MGHSS) is proposed. A preconditioner can also be taken for all of the generalizations of the HSS, although many researchers concentrated on the studies of the relations between parameters and the convergence property of each. Furthermore, we establish the IMGHSS as an efficient solver. The convergence of the MGHSS and IMGHSS were analyzed. Also, the efficiency and robustness of the proposed method were verified in several examples.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 11771393 and 11632015) and Zhejiang Natural Science Foundation (Grant no. LZ14A010002).

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