Coupled Sylvester Matrix Equations Research Articles

Generalized conjugate direction algorithm for solving general coupled Sylvester matrix equations

In this paper, a generalized conjugate direction algorithm (GCD) is proposed for solving general coupled Sylvester matrix equations. The GCD algorithm is an improved gradient algorithm, which can realize gradient descent by introducing matrices Pj(k) and Tj(k) to construct parameters α(k) and β(k). The matrix Pj(k) and Tj(k) are iterated in a cross way to accelerate the convergence rate. In addition, it is further proved that the algorithm converges to the exact solution in finite iteration steps in the absence of round-off errors if the system is consistent. Also, the sufficient conditions for least squares solutions and the minimum F-norm solutions are obtained. Finally, numerical examples are given to demonstrate the effectiveness of the GCD algorithm.

An efficient approach for enclosing the solution set of the interval coupled Sylvester matrix equations

In this paper, we investigate the interval coupled Sylvester matrix equations which include the well-known (generalized) Sylvester and Lyapunov matrix equations in both real and interval forms. Assuming that certain matrices are simultaneously diagonalizable, we present a fast and efficient approach for enclosing the solution set of the interval coupled Sylvester matrix equations. Our approach, which is a modification of the Krawczyk operator, enables us to reduce the computational complexity considerably. Some numerical tests are given to illustrate the effectiveness of the proposed approach.

On the minimum-norm least squares solution of the complex generalized coupled sylvester matrix equations

By means of the real linear operator, we establish an iterative algorithm for solving a class of complex generalized coupled Sylvester matrix equations. The finite termination of the proposed algorithm is proved. By representing a complex matrix as a larger real matrix, we present a new method to prove that the minimum-norm solution or minimum-norm least squares solution of the complex generalized coupled Sylvester matrix equations can be obtained by an appropriate selection for the initial matrices, which has not been found in the existing work. Numerical experiments on some randomly generated data and practical image restoration problem show that the proposed algorithm is feasible and effective.

Conjugate gradient-based iterative algorithm for solving generalized periodic coupled Sylvester matrix equations

This paper focuses on constructing a conjugate gradient-based (CGB) method to solve the generalized periodic coupled Sylvester matrix equations in complex space. The presented method is developed from a point of conjugate gradient methods. It is proved that the presented method can find the solution of the considered matrix equations within finite iteration steps in the absence of round-off errors by theoretical derivation. Some numerical examples are provided to verify the convergence performance of the presented method, which is superior to some existing numerical algorithms both in iteration steps and computation time.

An iterative algorithm for the generalized centro-symmetric solution of the generalized coupled Sylvester matrix equations AV+BW=EVF+C, MV+NW=GVH+D

In this paper, an iterative algorithm for solving of the generalized coupled Sylvester matrix equations AV+BW=EVF+C, MV+NW=GVH+D over the generalized centro-symmetric matrices (V,W ) is proposed. For any initial generalized centro-symmetric matrices V0 and W0, a generalized centro-symmetric solution (V,W ) is obtained within a finite number of iterations in the absence of round-off errors. Two numerical examples are presented to support the theoretical results where the efficiency and accuracy of the suggested algorithm are shown.

An iterative algorithm for generalized periodic multiple coupled Sylvester matrix equations

The paper is indicated to constructing a modified conjugate gradient iterative (MCG) algorithm to solve the generalized periodic multiple coupled Sylvester matrix equations. It can be proved that the proposed approach can find the solution within finite iteration steps in the absence of round-off errors. Furthermore, we provide a method for choosing the initial matrices to obtain the least Frobenius norm solution of the system. Some numerical examples are illustrated to show the performance of the proposed approach and its superiority over the existing method CG.

Least‐squares partially bisymmetric solutions of coupled Sylvester matrix equations accompanied by a prescribed submatrix constraint

The coupled Sylvester matrix equations (CSMEs) appear frequently in various fields of mathematics and engineering such as in control systems and signal processing. In this investigation, we establish and analyze the generalized conjugate directions (GCDs) method for solving the CSMEs over partially bisymmetric matrices X and Y with a prescribed submatrix constraint. We show that the GCD method with the arbitrary initial bisymmetric matrices can compute the least‐squares partially bisymmetric solutions with a prescribed submatrix constraint within a finite number of iterations in the absence of round‐off errors. Numerical examples illustrate the efficiency and simplicity of the GCD method and confirm the theoretical results.

The BCR algorithms for solving the reflexive or anti-reflexive solutions of generalized coupled Sylvester matrix equations

This paper proposes the Lanczos version of the biconjugate residual algorithm to solve the reflexive or anti-reflexive solutions of a class of generalized coupled Sylvester matrix equations. We give a convergence analysis, and by constructing a special form of the initial matrices, the minimum-norm reflexive solutions can be obtained through finite-step iterations without considering rounding errors. At the end of the paper, we provide numerical examples to illustrate the feasibility and effectiveness of this algorithm.

Gradient based approach for generalized discrete-time periodic coupled Sylvester matrix equations

The paper is dedicated to solving the generalized periodic discrete-time coupled Sylvester matrix equation, which is frequently encountered in control theory and applied mathematics. The solvable condition and a iterative algorithm for this equation are presented. The proposed method is developed from a point of least squares method. The rationality of the method is testified by theoretical analysis, which shows that the algorithm can solve the problem within finite number of iterations. The presented approach is numerically reliable and requires less computation. A numerical example illustrates the effectiveness of the raised result.

Two Parameter Iteration Methods for Coupled Sylvester Matrix Equations

Two Parameter Iteration Methods for Coupled Sylvester Matrix Equations

A relaxed gradient based algorithm for solving generalized coupled Sylvester matrix equations

The present work proposes a relaxed gradient based iterative (RGI) algorithm to find the solutions of coupled Sylvester matrix equations AX+YB=C,DX+YE=F. It is proved that the proposed iterative method can obtain the solutions of the coupled Sylvester matrix equations for any initial matrices X0 and Y0. Next the RGI algorithm is extended to the generalized coupled Sylvester matrix equations of the form Ai1X1Bi1+Ai2X2Bi2+⋯+AipXpBip=Ci,(i=1,2,…,p). Then, we compare their convergence rate and find RGI is faster than GI, which has maximum convergence rate, under an appropriative positive number ω and the same convergence factor µ1 and µ2. Finally, a numerical example is included to demonstrate that the introduced iterative algorithm is more efficient than the gradient based iterative (GI) algorithm of (Ding and Chen 2006) in speed, elapsed time and iterative steps.

The eigenvalues range of a class of matrices and some applications in Cauchy–Schwarz inequality and iterative methods

This paper discusses the range of the eigenvalues of a class of matrices. By using the eigenvalues range of a class of matrices, an extension of the inner product type Cauchy–Schwarz inequality is obtained, the convergence proof of the least squares based iterative algorithm for solving the coupled Sylvester matrix equations is given and the best convergence factor is determined. Moreover, by using the eigenvalues range of this class of matrices, an iterative algorithm for solving linear matrix equation is established. Three numerical examples are offered to illustrate the effectiveness of the results suggested in this paper.

Iterative (R, S)-conjugate solutions to the generalised coupled Sylvester matrix equations

ABSTRACTFor given symmetric orthogonal matrices R, S, i.e. RT = R, R2 = I, ST = S, S2 = I, a matrix is termed (R, S)-conjugate matrix if . In this paper, an iterative method is constructed to find the (R, S)-conjugate solutions of the generalised coupled Sylvester matrix equations. The consistency of the considered matrix equations over (R, S)-conjugate matrices is discussed. When the matrix equations have a unique (R, S)-conjugate solution pair, the proposed method is convergent for any initial (R, S)-conjugate matrix pair under a loose restriction on the convergent factor. Moreover, the optimal convergent factor of the presented method is derived. Finally, some numerical examples are given to illustrate the results and effectiveness.

BCR method for solving generalized coupled Sylvester equations over centrosymmetric or anti-centrosymmetric matrix

This paper introduces another version of biconjugate residual method (BCR) for solving the generalized coupled Sylvester matrix equations over centrosymmetric or anti-centrosymmetric matrix. We prove this version of BCR algorithm can find the centrosymmetric solution group of the generalized coupled matrix equations for any initial matrix group within finite steps in the absence of round-off errors. Furthermore, a method is provided for choosing the initial matrices to obtain the least norm solution of the problem. At last, some numerical examples are provided to illustrate the efficiency and validity of methods we have proposed.

On the convergence of conjugate direction algorithm for solving coupled Sylvester matrix equations

In this work, we obtain the matrix form of the conjugate direction (MCD) method for solving the coupled Sylvester matrix equations (CSMEs)\(\left\{ \begin{array}{ll} \sum _{i=1}^s A_iXB_i=C, &{} \hbox {} \\ \sum _{j=1}^t D_jXE_j=F, &{} \hbox {} \end{array}\right. \)which are defined in the domain of real numbers. We prove that the MCD method converges to the solution of the CSMEs for any initial guess within a finite number of iterations in the absence of round-off errors. Also we show that the MCD method can find the least Frobenius norm solution of the CSMEs with special initial guess. Finally three numerical examples show that the MCD method is efficient to solve some matrix equations.

Conjugate gradient least squares algorithm for solving the generalized coupled Sylvester matrix equations

This paper discusses the conjugate gradient least squares algorithm for solving the generalized coupled Sylvester matrix equations ∑j=1qAijXjBij=Fi, i=1,2,…,p. We prove that if this system is consistent then the iterative solution converges to the exact solution and if this system is inconsistent then the iterative solution converges to the least squares solution within the finite iteration steps in the absence of the roundoff errors. Also by setting the initial iterative value properly we prove that the iterative solution converges to the least squares and minimum-norm solution.

A System of Periodic Discrete-time Coupled Sylvester Quaternion Matrix Equations

We in this paper derive necessary and sufficient conditions for the system of the periodic discrete-time coupled Sylvester matrix equations [Formula: see text] over the quaternion algebra to be consistent in terms of ranks and generalized inverses of the coefficient matrices. We also give an expression of the general solution to the system when it is solvable. The findings of this paper generalize some known results in the literature.

Iterative Hermitian R-conjugate solutions to general coupled sylvester matrix equations

For a given symmetric orthogonal matrix R, i.e., RT = R, R2 = I, a matrix A ? Cnxn is termed Hermitian R-conjugate matrix if A = AH, RAR = ?. In this paper, an iterative method is constructed for finding the Hermitian R-conjugate solutions of general coupled Sylvester matrix equations. Convergence analysis shows that when the considered matrix equations have a unique solution group then the proposed method is always convergent for any initial Hermitian R-conjugate matrix group under a loose restriction on the convergent factor. Furthermore, the optimal convergent factor is derived. Finally, two numerical examples are given to demonstrate the theoretical results and effectiveness.

A finite iterative algorithm for solving the complex generalized coupled Sylvester matrix equations by using the linear operators

By introducing the real linear operator, this paper offers a finite iterative algorithm for solving the complex generalized coupled Sylvester matrix equations. The properties of this algorithm are discussed and the finite convergence of this algorithm is proven. This algorithm unifies some finite iterative algorithms proposed by the earlier papers. Two numerical examples are offered to illustrate the effectiveness of the proposed algorithm.

Finding solutions for periodic discrete-time generalized coupled Sylvester matrix equations via the generalized BCR method

The periodic discrete-time matrix equations have wide applications in stability theory, control theory and perturbation analysis. In this work, the biconjugate residual algorithm is generalized to construct a matrix iterative method to solve the periodic discrete-time generalized coupled Sylvester matrix equations [Formula: see text] The constructed method is shown to be convergent in a finite number of iterations in the absence of round-off errors. By comparing with other similar methods in practical computation, we give numerical results to demonstrate the accuracy and the numerical superiority of the constructed method.