Efficient iterative solutions to general coupled matrix equations

Linear matrix equations are encountered in many systems and control applications.In this paper,we consider the general coupled matrix equations（including the generalized coupled Sylvester matrix equations as a special case）l t=1EstYtFst = Gs,s = 1,2,···,l over the generalized reflexive matrix group（Y1,Y2,···,Yl）.We derive an efcient gradient-iterative（GI） algorithm for fnding the generalized reflexive solution group of the general coupled matrix equations.Convergence analysis indicates that the algorithm always converges to the generalized reflexive solution group for any initial generalized reflexive matrix group（Y1（1）,Y2（1）,···,Yl（1））.Finally,numerical results are presented to test and illustrate the performance of the algorithm in terms of convergence,accuracy as well as the efciency.     

Gradient based iterative solutions for general linear matrix equations

In this paper, we present a gradient based iterative algorithm for solving general linear matrix equations by extending the Jacobi iteration and by applying the hierarchical identification principle. Convergence analysis indicates that the iterative solutions always converge fast to the exact solutions for any initial values and small condition numbers of the associated matrices. Two numerical examples are provided to show that the proposed algorithm is effective.     

Convergence characterisation of an iterative algorithm for periodic Lyapunov matrix equations

This paper is concerned with convergence characterisation of an iterative algorithm for a class of reverse discrete periodic Lyapunov matrix equation associated with discrete-time linear periodic systems. Firstly, a simple necessary condition is given for this algorithm to be convergent. Then, a necessary and sufficient condition is presented for the convergence of the algorithm in terms of the roots of polynomial equations. In addition, with the aid of the necessary condition explicit expressions of the optimal parameter such that the algorithm has the fastest convergence rate are provided for two special cases. The advantage of the proposed approaches is illustrated by numerical examples.     

Weighted steepest descent method for solving matrix equations

This paper describes iterative methods for solving the general linear matrix equation including the well-known Lyapunov matrix equation, Sylvester matrix equation and some related matrix equations encountered in control system theory, as special cases. We develop the methods from the optimization point of view in the sense that the iterative algorithms are constructed to solve some optimization problems whose solutions are closely related to the unique solution to the linear matrix equation. Actually, two optimization problems are considered and, therefore, two iterative algorithms are proposed to solve the linear matrix equation. To solve the two optimization problems, the steepest descent method is adopted. By means of the so-called weighted inner product that is defined and studied in this paper, the convergence properties of the algorithms are analysed. It is shown that the algorithms converge at least linearly for arbitrary initial conditions. The proposed approaches are expected to be numerically reliable as only matrix manipulation is required. Numerical examples show the effectiveness of the proposed algorithms.     

Iterative algorithm for minimal norm least squares solution to general linear matrix equations

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.     

An iterative algorithm for solving a class of matrix equations

In this paper, an iterative algorithm is presented to solve the Sylvester and Lyapunov matrix equations. By this iterative algorithm, for any initial matrix X1, a solution X＊ can be obtained within finite iteration steps in the absence of roundoff errors. Some examples illustrate that this algorithm is very efficient and better than that of [ 1 ] and [2].     

Quasi gradient-based inversion-free iterative algorithm for solving a class of the nonlinear matrix equations

Inspired by the gradient-based and inversion-free iterations, a new quasi gradient-based inversion-free iterative algorithm is proposed for solving the nonlinear matrix equation $X+A^{\mathrm{T}}{X}^{?n}A=I$. The convergence proof of the suggested algorithm is given. Several matrix norm inequalities are established to depict the convergence properties of this algorithm. Three numerical examples are given to illustrate the effectiveness of the suggested algorithms.     

改进的求解线性方程组的并行Arnoldi方法

以Galerkin原理为基础,提出了求解循环块三对角线性方程组的并行算法。根据系数矩阵的稀疏性,选取适当的子空间的基,使算法不但不会发生中断,并从理论上证明了当系数矩阵对称正定时,该并行算法收敛。最后,在HP rx2600集群上进行的数值实验结果表明,该算法的并行效率很高,理论和实际计算相一致。     

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.     

带状线性方程组的并行交替方向算法

提出了分布式存储环境下求解带状线性方程组的并行交替方向迭代算法。充分利用系数矩阵的结构特点,给出了在系数矩阵分别为Hermite正定矩阵和M-矩阵时算法的充分条件,并针对采用的分裂方式,讨论了参数的收敛范围,最后在HPrx2600集群系统上进行了数值计算,结果表明实算与理论相一致,算法简便可行且具有良好的并行性。     

基于组合优化的线性含错方程组的求解方法

提出了基于组合优化的求解二元域线性含错方程组的方法,建立了数学模型,并使用局部搜索算法和模拟退火算法进行求解。实验结果表明利用组合优化方法求解线性含错方程组是一种可行而有效的办法。     

New iterative method for solving non-linear equations with fourth-order convergence

In this work, we introduce an extension of the classical Newton's method for solving non-linear equations. This method is free from second derivative. Similar to Newton's method, the proposed method will only require function and first derivative evaluations. The order of convergence of the introduced method for a simple root is four. Numerical results show that the new method can be of practical interest.     

A third-order convergent iterative method for solving non-linear equations

In this paper, we present and analyse a new predictor-corrector iterative method for solving non-linear single variable equations. The convergence analysis establishes that the new method is cubically convergent. Numerical tests show that the method is comparable with the well-known existing methods and in many cases gives better results.     

A finite-time convergent dynamic system for solving online simultaneous linear equations

A new dynamic system is proposed and investigated for solving online simultaneous linear equations. Compared with the gradient-based dynamic system and the recently proposed Zhang dynamic system, the proposed dynamic system can achieve superior convergence performance (i.e. finite-time convergence) and thus is called the finite-time convergent dynamic system. In addition, the upper bound of the convergence time is derived analytically with the error bound being zero theoretically. Simulation results further indicate that the proposed dynamic system is much more efficient than the existing dynamic systems.     

Gradient-based iterative algorithms for generalized coupled Sylvester-conjugate matrix equations

By applying the hierarchical identification principle, the gradient-based iterative algorithm is suggested to solve a class of complex matrix equations. With the real representation of a complex matrix as a tool, the sufficient and necessary conditions for the convergence factor are determined to guarantee that the iterative solutions given by the proposed algorithm converge to the exact solution for any initial matrices. Also, we solve the problem which is proposed by Wu et al. (2010). Finally, some numerical examples are provided to illustrate the effectiveness of the proposed algorithms and testify the conclusions suggested in this paper.