Numerical Methods for a Class of Quadratic Matrix Equations

Quadratic matrix equations arise in many elds of scienti c computing and engineering applications.In this paper,we consider a class of quadratic matrix equations.Under a certain condition,we rst prove the existence of minimal nonnegative solution for this quadratic matrix equation,and then propose some numerical methods for solving it.Convergence analysis and numerical examples are given to verify the theories and the numerical methods of this paper.

AOR Iterative Method for Coupled Lyapunov Matrix Equations

An AOR(Accelerated Over-Relaxation)iterative method is suggested by introducing one more parameter than SOR(Successive Over-Relaxation)method for solving coupled Lyapunov matrix equations(CLMEs)that come from continuous-time Markovian jump linear systems.The proposed algorithm improves the convergence rate,which can be seen from the given illustrative examples.The comprehensive theoretical analysis of convergence and optimal parameter needs further investigation.

On Minimization of Upper Bound for the Convergence Rate of the QHSS Iteration Method

For an upper bound of the spectral radius of the QHSS (quasi Hermitian and skew-Hermitian splitting) iteration matrix which can also bound the contraction factor of the QHSS iteration method,we give its minimum point under the conditions which guarantee that the upper bound is strictly less than one. This provides a good choice of the involved iteration parameters,so that the convergence rate of the QHSS iteration method can be significantly improved.

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].

On the Solutions of the Matrix Equations in Optimal Stochastic Control

In this paper, the matrix algebraic equations involved in the optimal control problem of time-invariant linear Ito stochastic systems, named Riccati- Ito equations in the paper, are investigated. The necessary and sufficient condition for the existence of positive definite solutions of the Riccati- Ito equations is obtained and an iterative solution to the Riccati- Ito equations is also given in the paper thus a complete solution to the basic problem of optimal control of time-invariant linear Ito stochastic systems is then obtained. An example is given at the end of the paper to illustrate the application of the result of the paper.

On the Nonlinear Matrix Equation X＋A~＊f_1（X）A＋B~＊f_2（X）B=Q

In this paper, nonlinear matrix equations of the form X ＋ A＊f1 （X）A ＋ B＊f2 （X）B = Q are discussed. Some necessary and sufficient conditions for the existence of solutions for this equation are derived. It is shown that under some conditions this equation has a unique solution, and an iterative method is proposed to obtain this unique solution. Finally, a numerical example is given to identify the efficiency of the results obtained.

Comparison of Fixed Point Methods and Krylov Subspace Methods Solving Convection-Diffusion Equations

The paper first introduces two-dimensional convection-diffusion equation with boundary value condition, later uses the finite difference method to discretize the equation and analyzes positive definite, diagonally dominant and symmetric properties of the discretization matrix. Finally, the paper uses fixed point methods and Krylov subspace methods to solve the linear system and compare the convergence speed of these two methods.

四元数矩阵方程(A_(1)XB_(1),…,A_(k)XB_(k))=(C_(1),…,C_(k))的极小范数最小二乘Toeplitz解

基于四元数矩阵实表示,结合矩阵H-表示和矩阵半张量积提出一种求解四元数矩阵方程(A_(1)XB_(1),…,A_(k)XB_(k))=(C_(1),…,C_(k))的极小范数最小二乘Toeplitz解的有效方法,给出该四元数矩阵方程存在Toeplitz解的充要条件及通解表达式.给出数值算法并通过算例分别从误差与计算时间两个方面验证该方法的有效性.

求解Lyapunov矩阵方程的梯度优化算法

通过Kronecker积将Lyapunov矩阵方程的求解问题转化为优化问题,利用带有回溯的Barzilai和Borwein梯度迭代算法求解无约束最小化问题,并详细分析了其收敛性.通过数值算例说明所推导算法是有效的.

一类耦合代数Riccati方程的解

耦合代数Riccati方程在控制论中具有广泛的应用,文章研究了一类耦合代数Riccati方程的解。基于M-矩阵和非负矩阵的理论,在一定假设条件下,证明了这类耦合代数Riccati方程存在最小非负解。最后通过数值算例,验证了所得结果的有效性。

一类含时Poisson-Nernst-Planck方程的虚单元计算

针对一类含时Poisson-Nernst-Planck(PNP)方程,为避免在解决实际问题时有限元法中的网格适应性问题,构造了L^(2)投影算子与Gummel迭代相结合的虚单元算法。该算法允许以更简单的方式设计和分析新的格式,可以灵活处理各种网格,对于多边形或多面体单元甚至非凸单元组成的网格剖分都可以很好地处理,使得虚单元法可以适应于任意多边形网格,大大降低了网格的生成难度。给出了虚单元算法在三角形网格、四边形网格、非凸网格下的数值算例。数值实验结果表明,在这3种多边形网格上,L^(2)和H^(1)模的收敛阶分别为二阶和一阶,均达到了最优阶。

求解M-矩阵绝对值方程的不动点迭代算法

多种不动点迭代方法被用于求解绝对值方程■。受此启发,本研究建立了一种求解M-矩阵绝对值方程的不动点迭代算法,在适当条件下分析了该方法的收敛性,并通过数值算例验证了该方法的可行性和有效性。

A MODIFIED HSS ITERATION METHOD FOR SOLVING THE COMPLEX LINEAR MATRIX EQUATION AXB = C

In this paper, a modified Hermitian and skew-Hermitian splitting （MHSS） iteration method for solving the complex linear matrix equation AXB = C has been presented. As the theoretical analysis shows, the MHSS iteration method will converge under cer- tain conditions. Each iteration in this method requires the solution of four linear matrix equations with real symmetric positive definite coefficient matrices, although the original coefficient matrices are complex and non-Hermitian. In addition, the optimal parameter of the new iteration method is proposed. Numerical results show that MHSS iteration method is efficient and robust.

MODIFIED BERNOULLI ITERATION METHODS FOR QUADRATIC MATRIX EQUATION

We construct a modified Bernoulli iteration method for solving the quadratic matrix equation AX^2 ＋ BX ＋ C = 0, where A, B and C are square matrices. This method is motivated from the Gauss-Seidel iteration for solving linear systems and the ShermanMorrison-Woodbury formula for updating matrices. Under suitable conditions, we prove the local linear convergence of the new method. An algorithm is presented to find the solution of the quadratic matrix equation and some numerical results are given to show the feasibility and the effectiveness of the algorithm. In addition, we also describe and analyze the block version of the modified Bernoulli iteration method.

关于Leontief产出方程的二级分裂迭代方法

研究Leontief投入产出模型中计算产出向量的迭代方法,基于Leontief产出方程,在矩阵规模很大,直接计算逆矩阵很困难的条件下,通过引入参数并运用二级分裂迭代思想和松弛技术,提出了Leontief产出方程的二级分裂迭代方法,给出了该方法的收敛理论.利用给出的收敛因子的计算方法,讨论了参数的优化选择,数值实例验证了此方法的有效性,表明优化参数能有效提高迭代方法的收敛效率.

Sylvester矩阵方程的改进IO迭代算法

通过对Sylvester矩阵方程的理论分析,可知IO迭代算法中迭代矩阵的谱半径随内迭代次数的增大而减小,更新了IO迭代算法中内迭代次数的选择方法,并证明了该算法收敛性与初始矩阵无关。Sylvester矩阵在满足一些特定条件下,为了进一步提高收敛速度,可通过选择适当的相关参数,使得IO迭代算法有较好的收敛速度且比Smith算法的迭代次数明显减少。

QBD矩阵方程的最小非负解

研究QBD方程的最小非负解,证明当方程的系数矩阵为正则M-矩阵时,方程仍然存在最小非负解,并在此基础上讨论最小非负解的有关性质.最后通过几个例子对文中的理论结果进行验证.

M-矩阵代数Riccati方程的一类简单迭代法

文章研究了M-矩阵代数Riccati方程的数值解法。当方程的系数矩阵为正则奇异M-矩阵时,现有的一些数值方法在计算中存在一定程度的困难。为此提出了一类简单迭代法以求解方程,该方法在每步迭代中只用到矩阵乘法,运算量小且易于实现。理论分析和数值实验表明该方法是可行的,而且在一定情况下有效。

Developing Bi-CG and Bi-CR Methods to Solve Generalized Sylvester-transpose Matrix Equations

The bi-conjugate gradients(Bi-CG)and bi-conjugate residual(Bi-CR)methods are powerful tools for solving nonsymmetric linear systems Ax=b.By using Kronecker product and vectorization operator,this paper develops the Bi-CG and Bi-CR methods for the solution of the generalized Sylvester-transpose matrix equationp i=1(Ai X Bi+Ci XTDi)=E(including Lyapunov,Sylvester and Sylvester-transpose matrix equations as special cases).Numerical results validate that the proposed algorithms are much more efcient than some existing algorithms.

Least-norm and Extremal Ranks of the Least Square Solution to the Quaternion Matrix Equation AXB = C Subject to Two Equations

In this paper, we give the expression of the least square solution of the linear quaternion matrix equation AXB = C subject to a consistent system of quaternion matrix equations D1X = F1, XE2 =F2, and derive the maximal and minimal ranks and the leastnorm of the above mentioned solution. The finding of this paper extends some known results in the literature.