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In this paper, a new iteration method is proposed for solving the complex symmetric linear systems. In theory, we show that the convergence factor or the upper bound of the spectral radius of the iteration matrix of the new method are smaller than that of the PMHSS method proposed by Bai et al. (2011). Moreover, we analyze and compare the parameter-free versions and the spectrum distributions of the preconditioned matrix of the new method and the PMHSS method. Finally, we present some numerical experiments on a few model problems to illustrate the theoretical results and show the effectiveness of our new method. [ABSTRACT FROM AUTHOR]

In this paper we propose an iteration method to solve the multiple constrained least squares matrix problem. We first transform the multiple constrained least squares matrix problems into the multiple constrained matrix optimal approximation problem, and then we use the idea of Dykstra’s algorithm to derive the basic iterative pattern. We observe that we only need to solve multiple single constrained least squares matrix problems at each iteration step of the proposed algorithm. We give a numerical example to illustrate the effectiveness of the proposed method to solve the original problems. Also, we give an example to illustrate that the method proposed by Escalante and Li to solve the single constrained least squares matrix problem is not correct. [ABSTRACT FROM AUTHOR]

In this paper, we consider the positive-definite solution to a system of nonlinear matrix equations,and, whereare two positive constants andare given matrices. We proposed two inversion-free iterative algorithms for obtaining the maximal positive-definite solution of the system. And we prove that the sequences generated by the iterative schemes are monotonically increasing and bounded above. Finally, some numerical examples are given to show the behaviour of the considered algorithms. [ABSTRACT FROM AUTHOR]

The discretetime periodic matrix equations are encountered in periodic state feedback problems and model reduction of periodic descriptor systems. The aim of this paper is to compute the generalized reflexive solutions of the general coupled discretetime periodic matrix equations. We introduce a gradientbased iterative (GI) algorithm for finding the generalized reflexive solutions of the general coupled discrete time periodic matrix equations. It is shown that the introduced GI algorithm always converges to the generalized reflexive solutions for any initial generalized reflexive matrices. Finally, two numerical examples are investigated to confirm the efficiency of GI algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the nonlinear matrix equations X + A * X q A = Q ( 0 < q ≤ 1 ) and X − A * X q A = Q ( q > 1 ) . Necessary and sufficient conditions for the (unique) existence of Hermitian positive definite solutions of these equations are derived. Effective iterative algorithms are also provided to obtain the unique solution of X + A * X q A = Q ( 0 < q ≤ 1 ) and the minimal and maximal Hermitian positive definite solutions of X − A * X q A = Q ( q > 1 ) . Numerical examples are included to illustrate the efficiency of the presented iteration algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the following minimization problem:where,,,andare given. An efficient inequality relaxation technique is presented to relax the matrix inequality constraint so that there is an optimal solution which is(R,S)-symmetric that minimize, and also satisfies the corrected matrix inequality constraint. A hybrid algorithm with convergence analysis is given to solve this problem. Numerical examples show that the algorithm requires less CPU times when compared with some other methods. [ABSTRACT FROM AUTHOR]

In this paper, the Hermitian positive-definite solutions of the matrix equationXs+A*X−tA=Qare considered. New necessary and sufficient conditions for the equation to have a Hermitian positive-definite solution are derived. In particular, whenAis singular, a new estimate of Hermitian positive-definite solutions is obtained. In the end, based on the fixed point theorem, an iterative algorithm for obtaining the positive-definite solutions of the equation withQ=Iis discussed. The error estimations are found. [ABSTRACT FROM AUTHOR]

Abstract: The main aim of this paper is to provide a higher-order convergent iterative method in order to calculate the generalized inverse of a given matrix. We extend the iterative method proposed in Li et al. [W.G. Li, Z. Li, A family of iterative methods for computing the approximate inverse of a square matrix and inner inverse of a non-square matrix, Applied Mathematics and Computation 215 (2010) 3433-3442] to compute the -inverse, generalized inverse -inverse and -inverse. Moreover, we modify the iterative method to compute the generalized inverse of Toeplitz matrices by using the displacement theory. [Copyright &y& Elsevier]

In this paper, we present an iterative algorithm for solving the following coupled Sylvester-transpose matrix equationsover the generalized centro-symmetric matrix group (X1,X2, …,Xq). The solvability of the problem can be determined by the proposed algorithm, automatically. If the coupled Sylvester-transpose matrix equations are consistent over the generalized centro-symmetric matrices, then a generalized centro-symmetric solution group can be obtained within finite iterative steps for any initial generalized centro-symmetric matrix group in the exact arithmetic. Furthermore, it is shown that the least-norm generalized centro-symmetric solution group of the coupled Sylvester-transpose matrix equations can be computed by choosing an appropriate initial iterative matrix group. Moreover, the optimal approximate generalized centro-symmetric solution group to a given arbitrary matrix group (V1,V2, …,Vq) can be derived by finding the least-norm generalized centro-symmetric solution group of a new coupled Sylvester-transpose matrix equations. Finally, some numerical results are given to illustrate the validity and practicability of the theoretical results established in this work. [ABSTRACT FROM PUBLISHER]

In this paper, we present an iterative method to compute the symmetric solutions of the matrix inequalityAX≥Bin least-squares sense. The presented iteration method can be also used to compute the symmetric solutions of the matrix equationAX=Band the consistent matrix inequalityAX≥B. Numerical experiments are given to illustrate the usefulness of the proposed approach. [ABSTRACT FROM PUBLISHER]

In this paper, combining the equivalent form of the unified coupled algebraic Riccati equation (UCARE) with the eigenvalue inequalities of a matrix's sum and product, using the properties of an M-matrix and its inverse matrix, we offer new lower and upper matrix bounds for the solution of the UCARE. Furthermore, applying the derived lower and upper matrix bounds and a fixed-point theorem, an existence uniqueness condition of the solution of the UCARE is proposed. Then, we propose a new fixed-point iterative algorithm for the solution of the UCARE. Finally, we present a corresponding numerical example to demonstrate the effectiveness of our results. [ABSTRACT FROM PUBLISHER]

Abstract: This paper studies the solvability, existence of unique solution, closed-form solution and numerical solution of matrix equation with and where is the unknown. It is proven that the solvability of these equations is equivalent to the solvability of some auxiliary standard Stein equations in the form of where the dimensions of the coefficient matrices and are the same as those of the original equation. Closed-form solutions of equation can then be obtained by utilizing standard results on the standard Stein equation. On the other hand, some generalized Stein iterations and accelerated Stein iterations are proposed to obtain numerical solutions of equation . Necessary and sufficient conditions are established to guarantee the convergence of the iterations. [Copyright &y& Elsevier]

In this paper, an iterative method is presented to solve the following constrained minimum Frobenius norm residual problem: [image omitted] where [image omitted] is a linear operator from Rm×n onto [image omitted] , [image omitted] , [image omitted] is a linear self-conjugate involution operator. By this method, for any initial matrix [image omitted] , a solution can be obtained in finite iteration steps in the absence of roundoff errors. The least norm solution can be derived when an appropriate initial matrix is chosen. In addition, the optimal approximation solution in the solution set of the above problem to a given matrix can also be derived by this method. Several numerical examples are given to show the efficiency of the proposed iterative method. [ABSTRACT FROM AUTHOR]

In this paper, an iterative method is presented for finding the bisymmetric solutions of a pair of consistent matrix equations A1XB1=C1, A2XB2=C2, by which a bisymmetric solution can be obtained in finite iteration steps in the absence of round-off errors. Moreover, the solution with least Frobenius norm can be obtained by choosing a special kind of initial matrix. In the solution set of the matrix equations, the optimal approximation bisymmetric solution to a given matrix can also be derived by this iterative method. The efficiency of the proposed algorithm is shown by some numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with minimal norm least squares solution to general linear matrix equations including the well-known Lyapunov matrix equation and Sylvester matrix equation as special cases. Two iterative algorithms are proposed to solve this problem. The first method is based on the gradient search principle for solving optimization problem and the second one can be regarded as its dual form. For both algorithms, necessary and sufficient conditions guaranteeing the convergence of the algorithms are presented. The optimal step sizes such that the convergence rates of the algorithms are maximized are established in terms of the singular values of some coefficient matrix. It is believed that the proposed methods can perform important functions in many analysis and design problems in systems theory. [ABSTRACT FROM AUTHOR]

This paper is concerned with numerical solutions to general linear matrix equations including the well-known Lyapunov matrix equation and Sylvester matrix equation as special cases. Gradient based iterative algorithm is proposed to approximate the exact solution. A necessary and sufficient condition guaranteeing the convergence of the algorithm is presented. A sufficient condition that is easy to compute is also given. The optimal convergence factor such that the convergence rate of the algorithm is maximized is established. The proposed approach not only gives a complete understanding on gradient based iterative algorithm for solving linear matrix equations, but can also be served as a bridge between linear system theory and numerical computing. Numerical example shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the nonlinear matrix equation is investigated. Based on the fixed-point theory, the existence and the uniqueness of the positive definite solution are studied. An effective iterative method to obtain the unique positive definite solution is established given . In addition, some computable estimates of the unique positive definite solution are derived. Finally, numerical examples are given to illustrate the effectiveness of the algorithm and the perturbation estimates. [Copyright &y& Elsevier]