二次四元数系统正定解的迭代法

二次四元数系统XAX?BX=P是离散型Lyapunov方程正定解反问题的推广形式.本文在四元数体上讨论它的正定解存在性及迭代求解方法.利用等价二次方程的系数矩阵的极大极小特征值,获得其正定解的存在区间,并针对系数矩阵的不同情况构建出三种收敛的迭代格式.同时根据每种迭代的特点,给出了迭代初始矩阵的选取方法.最后通过四元数矩阵复算子实现Matlab环境下求解.数值算例验证了所给方法的有效及可行性.     

离散空间分数阶非线性薛定谔方程的MHSS型迭代方法

利用隐式守恒型差分格式来离散空间分数阶非线性薛定谔方程,可得到一个离散线性方程组.该离散线性方程组的系数矩阵为一个纯虚数复标量矩阵、一个对角矩阵与一个对称Toeplitz矩阵之和.基于此,本文提出了用一种\textit{修正的埃尔米特和反埃尔米特分裂}(MHSS)型迭代方法来求解此离散线性方程组.理论分析表明,MHSS型迭代方法是无条件收敛的.数值实验也说明了该方法是可行且有效的.     

四元数体上一类矩阵方程解的数值方法

建立了求解四元数体上严格对角占优矩阵方程AX=B的QJ和QSOR迭代方法,并利用四元数矩阵的右特征值最大模刻画出迭代的收敛性,给出参数的取值范围;最后运用四元数矩阵的复表示运算保结构的特性,把这两种迭代等价地转化到复数域上,从而实现了该系统的数值求解.     

一类弱奇异边值问题的大范围收敛算法

该文研究如下的弱奇异边值问题: (p(x)y')'=f(x, y),0b₀g(x), 0≤b₀<1, 边值条件为y(0)=A,αy(1)+β y'(1)=γ 或y'(0)=0,αy(1)+βy'(1)=γ (R.K.Pandey 和 Arvind K.Singh 给出了一种求解此问题的二阶有限差分方法^[1]. 在再生核空间中讨论方程解的存在性, 给出一种新的迭代算法,这种迭代算法是大范围收敛的. 给出数值算例并与R. K. Pandey 和Arvind K.Singh 给出的方法进行比较说明该文方法的有效性.     

M-矩阵线性互补问题模系多分裂迭代方法的收敛性

首先证明了M-矩阵的H-相容分裂都是正则分裂,反之不成立.这表明对于M-矩阵而言,其正则分裂包含H-相容分裂.然后针对系数矩阵为M-矩阵的线性互补问题,建立了两个收敛定理:一是模系多分裂迭代方法关于正则分裂的收敛定理;二是模系二级多分裂迭代方法关于外迭代为正则分裂和内迭代为弱正则分裂的收敛定理.     

Z-矩阵的预条件方法

通过对方程组Ax=b的系数矩阵施行初等行变换，该文提出了解线性方程组Ax=b的一种新的预条件Gauss Seidel迭代方法，理论上证明了新的预条件Gauss Seidel迭代方法较经典的Gauss Seidel迭代法收敛速度快. 该文提出的新预条件方法推广了文［1-2］中提出的预条件方法，具体的数值例子说明了新预条件方法的有效性.     

求解一般界约束优化问题的积极集信赖域方法

本文提出了一种求解一般界约束优化问题的新方法. 每步迭代分为两个阶段. 在第一阶段, 从 当前迭代点x_k 出发, 沿着经过仿射变换后的梯度步, 得到试探点x_k¹, 记录下它的积极集. 这里用到的仿射变换矩阵不仅依赖于变量到边界的距离, 还依赖于当前迭代点的梯度以及该步迭代中的信赖域半 径. 在第二阶段, 从x_k¹ 出发, 通过在积极约束的零空间里面求解一个信赖域子问题得到新的试探点. 然后判断是否接受这个试探点作为下一个迭代点. 文中证明了算法的全局收敛性, 并且迭代点列的每 个聚点都是一阶稳定点. 文中还对国际著名的CUTEr 算例库中所有的界约束优化问题进行了测试. 数值结果表明我们的方法是有效的, 并且可以与L-BFGS-B 方法相媲美.     

求解一类分块二阶线性方程组的QHSS迭代方法

本文将QHSS迭代方法运用于求解一类分块二阶线性方程组. 通过适当地放宽QHSS迭代方法的收敛性条件,我们给出了用QHSS迭代方法求解一类分块二阶线性方程组的具体迭代格式,并证明了当系数矩阵中的(1,1)块对称半正定时该QHSS迭代方法的收敛性.我们还用数值实验验证了QHSS迭代方法的可行性和有效性.     

求解2×2块对称不定线性方程组迭代法的收敛性分析(英文)

本文研究求解系数矩阵为2×2块对称不定矩阵时的线性方程组,提出了一种新的分裂迭代法,并通过研究迭代矩阵的谱半径,详细讨论了新方法的收敛性.最后,我们也讨论了预条件矩阵特征根的几条性质.     

低能电子与NⅣ共振散射的R矩阵计算

本文用R矩阵方法精确地计算了N IV的2s²¹S^e,2s2p³P^o和2p²³P^e,¹D^e,¹S^e靶态间的低能电子碰撞激发的碰撞强度和速率系数,并将这些结果与其他理论和实验结果进行了比较。     

Bi-parameter incremental unknowns ADI iterative methods for elliptic problems

Bi-parameter incremental unknowns (IU) alternating directional implicit (ADI) iterative methods are proposed for solving elliptic problems. Condition numbers of the coefficient matrices for these iterative schemes are carefully estimated. Theoretical analysis shows that the condition numbers are reduced significantly by IU method, and the iterative sequences produced by the bi-parameter incremental unknowns ADI methods converge to the unique solution of the linear system if the two parameters belong to a given parameter region. Numerical examples are presented to illustrate the correctness of the theoretical analysis and the effectiveness of the bi-parameter incremental unknowns ADI methods.     

Convergence acceleration of preconditioned conjugate gradient solver based on error vector sampling for a sequence of linear systems

In this article, we focus on solving a sequence of linear systems that have identical (or similar) coefficient matrices. For this type of problem, we investigate subspace correction (SC) and deflation methods, which use an auxiliary matrix (subspace) to accelerate the convergence of the iterative method. In practical simulations, these acceleration methods typically work well when the range of the auxiliary matrix contains eigenspaces corresponding to small eigenvalues of the coefficient matrix. We develop a new algebraic auxiliary matrix construction method based on error vector sampling in which eigenvectors with small eigenvalues are efficiently identified in the solution process. We use the generated auxiliary matrix for convergence acceleration in the following solution step. Numerical tests confirm that both SC and deflation methods with the auxiliary matrix can accelerate the solution process of the iterative solver. Furthermore, we examine the applicability of our technique to the estimation of the condition number of the coefficient matrix. We also present the algorithm of the preconditioned conjugate gradient method with condition number estimation.     

Iterative algorithms for solving a class of complex conjugate and transpose matrix equations

This paper is concerned with iterative solutions to a class of complex matrix equations, which include some previously investigated matrix equations as special cases. By applying the hierarchical identification principle, an iterative algorithm is constructed to solve this class of matrix equations. A sufficient condition is presented to guarantee that the proposed algorithm is convergent for an arbitrary initial matrix with a real representation of a complex matrix as tools. By using some properties of the real representation, a convergence condition that is easier to compute is also given in terms of original coefficient matrices. A numerical example is employed to illustrate the effectiveness of the proposed methods.     

Iterative solutions to the extended Sylvester-conjugate matrix equations

This paper is concerned with iterative solutions to a class of complex matrix equations. By applying the hierarchical identification principle, an iterative algorithm is constructed to solve this class of complex matrix equations. The range of the convergence factor is given to guarantee that the proposed algorithm is convergent for arbitrary initial matrix by applying a real representation of a complex matrix as a tool. By using some properties of the real representation, a sufficient convergence condition that is easier to compute is also given by original coefficient matrices. Two numerical examples are given to illustrate the effectiveness of the proposed methods.     

Monotone Convergence of Iterative Methods for Singular Linear Systems

In this paper, monotonicity of iterative methods for solving general solvable singularly systems is discussed. The monotonicity results given by Berman, Plemmons, and Semal are generalized to singular systems. It is shown that for an iterative method introduced by a nonnegative splitting of the coefficient matrix there exist some initial guesses such that the iterative sequence converges towards a solution of the system from below or from above. The monotonicity of the block Gauss-Seidel method for solving a p-cyclic system and Markov chain is considered.     

混合元解重调和方程的条件数

考虑重调和方程第一边值问题Ω是R~2中的有界多边形区域。根据[1,381—424],问题可转化为 找(u,φ)∈H~1(Ω)×H_0~1(Ω),使成立     

The monotonicity of two-stage iterative methods

Two-stage iterative methods for the solution of linear systems are analyzed when the coefficient matrix is Hermitian positive definite. Comparison theorems, based on the number of inner iterations performed, are given.     

关于PSD迭代法收敛的充分必要性定理

本文在线性方程组系数矩阵A为相容次序矩阵及A的Jacobi迭代矩阵的特征值μ_j均为实数且μ_j~2<1的条件下,得出了PSD迭代法收敛的充分必要性定理,并由此而得到了一个易于判别的PSD法收敛性定理。     

A CLASS OF NEW PARALLEL HYBRID ALGEBRAIC MULTILEVEL ITERATIONS

1. IntroductionConsider the large sparse system of linear equationsAx = b, (1.1)where, for a fixed positive integer cr, A e L(R") is a symmetric positive definite (SPD) matrir,having the bloCked formx,b E R" are the uDknwn and the known vectors, respectively, having the correspondingblocked formsni(ni S n, i = 1, 2,', a) are a given positthe integers, satisfying Z ni = n. This systemi= 1of linear equations often arises in sultable finite element discretizations of many secondorderseifad…