A new solution to the generalized Sylvester matrix equation

This note deals with the problem of solving the generalized Sylvester matrix equation AV-EVF=BW, with F being an arbitrary matrix, and provides complete general parametric expressions for the matrices V and W satisfying this equation. The primary feature of this solution is that the matrix F does not need to be in any canonical form, and may be even unknown a priori. The results provide great convenience to the computation and analysis of the solutions to this class of equations, and can perform important functions in many analysis and design problems in control systems theory.     

Unified parametrization for the solutions to the polynomial diophantine matrix equation and the generalized Sylvester matrix equation

The polynomial Diophantine matrix equation and the generalized Sylvester matrix equation are important for controller design in frequency domain linear system theory and time domain linear system theory, respectively. By using the so-called generalized Sylvester mapping, right coprime factorization and Bezout identity associated with certain polynomial matrices, we present in this note a unified parametrization for the solutions to both of these two classes of matrix equations. Moreover, it is shown that solutions to the generalized Sylvester matrix equation can be obtained if solutions to the Diophantine matrix equation are available. The results disclose a relationship between the polynomial Diophantine matrix equation and generalized Sylvester matrix equation that are respectively studied and used in frequency domain linear system theory and time domain linear system theory.     

On the generalized Sylvester mapping and matrix equations

General parametric solution to a family of generalized Sylvester matrix equations arising in linear system theory is presented by using the so-called generalized Sylvester mapping which has some elegant properties. The solution consists of some polynomial matrices satisfying certain conditions and a parametric matrix representing the degree of freedom in the solution. The results provide great convenience to the computation and analysis of the solutions to this family of equations, and can perform important functions in many analysis and design problems in linear system theory. It is also expected that this so-called generalized Sylvester mapping tool may have some other applications in control system theory.     

On the solution of a rational matrix equation arising in G-networks

We consider the problem of solving a rational matrix equation arising in the solution of G-networks. We propose and analyze two numerical methods: a fixed point iteration and the Newton–Raphson method. The fixed point iteration is shown to be globally convergent with linear convergence rate, while the Newton method is shown to have a local convergence, with quadratic convergence rate. Numerical experiments show the effectiveness of the proposed methods.     

The interval Sylvester equation

In this paper a necessary and sufficient condition for the existence of a solution for the interval Sylvester equation is given. A modified Oettli's inequality is derived to characterize the solution. Many direct methods for solving the equation are suggested and compared to each other. These methods are based on different techniques such as simulation, linear programming, correspondence between an interval Sylvester equation and an interval linear system as well as sensitivity analysis. An iterative technique for solving the interval Sylvester equation is provided with special conditions to guarantee the convergence. The square root of an interval matrix is calculated as an application to solving interval Sylvester equations.     

Calculating fuzzy inverse matrix using fuzzy linear equation system

Linear equation systems play a very important role in engineering, mathematics, statistics and other disciplines. Fuzzifying either parameters or variables or both in these systems has been one of the research areas in the fuzzy literature since these kinds of systems are encountered in many applications. These systems are generally called fuzzy linear equations. Various types of these models have been examined for a decade. The solution procedures of these systems depend on different methods such as extension principle and interval arithmetic. Also, the method which is often used in computing inverse of a matrix in real case could be extended to fuzzy case, which employs linear equation system and identity matrix. For this purpose, we propose a new method which includes some new definitions which are fuzzy zero number, fuzzy one number and fuzzy identity matrix. Based on these definitions, direct computation of fuzzy inverse matrix is done using fuzzy arithmetic and fuzzy equation system. Actually, this simply extends the notion used in real case to fuzzy case. Calculation is realized with two different settings. While the first one is called direct numerical solution, the other is obtained by choice of decision maker. It is noted that the uniqueness of the calculated fuzzy inverse matrix is not guaranteed.     

On the solution of a nonlinear matrix equation for MIMO symmetric realizations

A result concerning a particular solution of a nonlinear solution matrix equation reported in a previous paper [2] and interesting for studying SISO systems, is extended to MIMO symmetric realizations. For such a class of systems other important SISO results can be generalized.     

On the solution of a matrix equation in the theory of Luenberger observers

An explicit solution is presented for a matrix equation involved in the design of a Luenberger observer to realize an acceptable approximation to a desired state-variable feedback lawu = u_{0} + Kxfor the systemdx/dt = Ax + Bu, y = Dx.     

On the eigenvalue-eigenvector method for solution of the stationary discrete matrix Riccati equation

The purpose of this correspondence is to point out that certain numerical problems encountered in the solution of the stationary discrete matrix Riccati equation by the eigenvalue-eigenvector method of Vanghan [1] can be avoided by a simple reformulation.     

Solving the generalized Sylvester matrix equation AV +BW = VF via Kronecker map

This note considers the solution to the generalized Sylvester matrix equation AV ＋ BW = VF with F being an arbitrary matrix, where V and W are the matrices to be determined. With the help of the Kronecker map, an explicit parametric solution to this matrix equation is established. The proposed solution possesses a very simple and neat form, and allows the matrix F to be undetermined.     

On global Hessenberg based methods for solving Sylvester matrix equations

In the first part of this paper, we investigate the use of Hessenberg-based methods for solving the Sylvester matrix equation $AX+XB=C$. To achieve this goal, the Sylvester form of the global generalized Hessenberg process is presented. Using this process, different methods based on a Petrov–Galerkin or on a minimal norm condition are derived. In the second part, we focus on the SGl-CMRH method which is based on the Sylvester form of the Hessenberg process with pivoting strategy combined with a minimal norm condition. In order to accelerate the SGl-CMRH method, a preconditioned framework of this method is also considered. It includes both fixed and flexible variants of the SGl-CMRH method. Moreover, the connection between the flexible preconditioned SGl-CMRH method and the fixed one is studied and some upper bounds for the residual norm are obtained. In particular, application of the obtained theoretical results is investigated for the special case of solving linear systems of equations with several right-hand sides. Finally, some numerical experiments are given in order to evaluate the effectiveness of the proposed methods.     

Solving complex Sylvester matrix equation by accelerated double-step scale splitting (ADSS) method

Accelerated double-step scale splitting (ADSS) approach is an efficient and fast method for solving a class of large complex symmetric linear equations which has been recently presented by parameterized DSS method. In this paper, we will apply ADSS scheme for solving complex Sylvester matrix equation. It will be proved analytically that the ADSS iteration method is faster than the DSS iteration method. Moreover, we minimize the upper bound of the spectral radius of iteration matrix. Finally, some test problems will be given and results will be reported to support the theoretical claims.

     

On a fuzzy difference equation

Difference equations arise in the modeling of many interesting problems. “Measurements” of data or specified information for an underlying problem may be imprecise or only partially specified. This motivates us to initiate a study of “fuzzy difference equations”. In this paper, we formulate and solve a given difference equation in the fuzzy setting and give a general method for dealing with any second-order difference equation. This work is motivated by considering an important problem in computer science, namely, the polyphase merging problem     

The numerical solution of the matrix Riccati differential equation

A numerically stable and fast computational method is given for the solution of the matrix Ricatti differential equation with finite terminal time.