Jianli Zhao

Real structure-preserving algorithms of Householder based transformations for quaternion matrices

In this paper, we survey three different forms of Householder based transformations for quaternion matrices in the literature, and propose a new form of quaternion Householder based transformation. We propose real structure-preserving algorithms of these Householder based transformations, which make the procedure computationally more flexible and efficient. We compare the computation counts and assignment numbers of these algorithms. We also compare the effectiveness of these real structure-preserving algorithms applying to the quaternion QRD and the quaternion SVD.All these four real structure-preserving algorithms are more efficient, comparing to the algorithms which apply Quaternion Toolbox using quaternion arithmetics, or algorithms which directly performs real Householder transformations on the real representation of a quaternion matrix. Among these four real structure-preserving algorithms, the most efficient ones are based on quaternion Householder reflection, and new proposed Householder based transformation.

Closed-form solutions to the nonhomogeneous Yakubovich-transpose matrix equation☆

Abstract The nonhomogeneous Yakubovich-transpose matrix equation X − A X T B = C Y + R , which contains the well-known Kalman–Yakubovich-transpose matrix equation and general discrete Lyapunov-transpose matrix equation as special cases, has many important applications in control system theory. This study presents two methods to obtain the closed-form solutions of the nonhomogeneous Yakubovich-transpose matrix equation. Moreover, the equivalent forms of the solutions are provided and one of the solutions is established with the controllability matrix, the observability matrix and symmetric operator matrix.

A Real Representation Method for Solving Yakubovich--Conjugate Quaternion Matrix Equation

A new approach is presented for obtaining the solutions to Yakubovich--conjugate quaternion matrix equation based on the real representation of a quaternion matrix. Compared to the existing results, there are no requirements on the coefficient matrix . The closed form solution is established and the equivalent form of solution is given for this Yakubovich--conjugate quaternion matrix equation. Moreover, the existence of solution to complex conjugate matrix equation is also characterized and the solution is derived in an explicit form by means of real representation of a complex matrix. Actually, Yakubovich-conjugate matrix equation over complex field is a special case of Yakubovich--conjugate quaternion matrix equation . Numerical example shows the effectiveness of the proposed results.

The minimal norm least squares Hermitian solution of the complex matrix equation AXB+CXD=E

Abstract In this paper, by applying the real representations of complex matrices, the particular structure of the real representations and the Moore–Penrose generalized inverse, we obtain the explicit expression of the minimal norm least squares Hermitian solution of the complex matrix equation A X B + C X D = E . And we also derive the minimal norm least squares Hermitian solution of the complex matrix equation A X B = E . Our proposed formulas only involve real matrices, and therefore are more effective and portable than those reported in Yuan and Liao (2014). The corresponding algorithms only perform real arithmetic which also consider the particular structure of the real representations of complex matrices. Two numerical examples are provided to demonstrate the effectiveness of our algorithms.

Special least squares solutions of the quaternion matrix equation A X B + C X D = E

In this paper, by using the real representations of quaternion matrices, the particular structure of the real representations of quaternion matrices, the Kronecker product of matrices and the Moore-Penrose generalized inverse, we obtain the expressions of the minimal norm least squares solution, the pure imaginary least squares solution, and the real least squares solution for the quaternion matrix equation A X B + C X D = E , respectively. Our resulting formulas only involve real matrices, and therefore are simpler than those reported in Yuan (2014). The corresponding algorithms only perform real arithmetic which also consider the particular structure of the real representations of quaternion matrices, and therefore are very efficient and portable. Numerical examples are provided to illustrate the efficiency of our algorithms.

On structure-oriented hybrid two-stage iteration methods for the large and sparse blocked system of linear equations

In this paper, we first present a class of structure-oriented hybrid two-stage iteration methods for solving the large and sparse blocked system of linear equations, as well as the saddle point problem as a special case. And the new methods converge to the solution under suitable restrictions, for instance, when the coefficient matrix is positive stable matrix generally. Numerical experiments for a model generalized saddle point problem are given, and the results show that our new methods are feasible and efficient, and converge faster than the Classical Uzawa Method.

On the power method for quaternion right eigenvalue problem

Abstract In this paper, we study the power method of the right eigenvalue problem of a quaternion matrix A . If A is Hermitian, we propose the power method that is a direct generalization of that of complex Hermitian matrix. When A is non-Hermitian, by applying the properties of quaternion right eigenvalues, we propose the power method for computing the standard right eigenvalue with the maximum norm and the associated eigenvector. We also briefly discuss the inverse power method and shift inverse power method for the both cases. The real structure-preserving algorithm of the power method in the two cases are also proposed, and numerical examples are provided to illustrate the efficiency of the proposed power method and inverse power method.

An efficient method for special least squares solution of the complex matrix equation (AXB,CXD)=(E,F)

Abstract In this paper, we propose an efficient method for special least squares solution of the complex matrix equation ( A X B , C X D ) = ( E , F ) . By using the real representation matrices of complex matrices, the particular structure of the real representation matrices, the Moore–Penrose generalized inverse and the Kronecker product, we obtain the explicit expression of the minimal norm least squares Hermitian solution of the complex matrix equation ( A X B , C X D ) = ( E , F ) , which was studied by a product of matrices and vectors in Wang et al. (2016). Our resulting formulas only involve real matrices, and the corresponding algorithm only performs real arithmetic. Therefore our proposed method is more effective and portable. Finally, we give three numerical examples to illustrate the effectiveness of our proposed method.