A REGULARIZED CONJUGATE GRADIENT METHOD FOR SYMMETRIC POSITIVE DEFINITE SYSTEM OF LINEAR EQUATIONS

AbstractA class of regularized conjugate gradient methods is presented for solving the large sparse system of linear equations of which the coefficient matrix is an ill-conditioned symmetric positive definite matrix. The convergence properties of these methods are discussed in depth, and the best possible choices of the parameters involved in the new methods are investigated in detail. Numerical computations show that the new methods are more efficient and robust than both classical relaxation methods and classical conjugate direction methods.     

Modulus‐based matrix splitting iteration methods for linear complementarity problems

For the large sparse linear complementarity problems, by reformulating them as implicit fixed‐point equations based on splittings of the system matrices, we establish a class of modulus‐based matrix splitting iteration methods and prove their convergence when the system matrices are positive‐definite matrices and H₊‐matrices. These results naturally present convergence conditions for the symmetric positive‐definite matrices and the M‐matrices. Numerical results show that the modulus‐based relaxation methods are superior to the projected relaxation methods as well as the modified modulus method in computing efficiency. Copyright © 2009 John Wiley & Sons, Ltd.     

A general finite element preconditioning for the conjugate gradient method

Discretizing a symmetric elliptic boundary value problem by a finite element method results in a system of linear equations with a symmetric positive definite coefficient matrix. This system can be solved iteratively by a preconditioned conjugate gradient method. In this paper a preconditioning matrix is proposed that can be constructed for all finite element methods if a mild condition for the node numbering is fulfilled. Such a numbering can be constructed using a variant of the reverse Cuthill-McKee algorithm.     

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…     

Application of a block modified Chebyshev algorithm to the iterative solution of symmetric linear systems with multiple right hand side vectors

Summary. An adaptive Richardson iteration method is described for the solution of large sparse symmetric positive definite linear systems of equations with multiple right-hand side vectors. This scheme ``learns' about the linear system to be solved by computing inner products of residual matrices during the iterations. These inner products are interpreted as block modified moments. A block version of the modified Chebyshev algorithm is presented which yields a block tridiagonal matrix from the block modified moments and the recursion coefficients of the residual polynomials. The eigenvalues of this block tridiagonal matrix define an interval, which determines the choice of relaxation parameters for Richardson iteration. Only minor modifications are necessary in order to obtain a scheme for the solution of symmetric indefinite linear systems with multiple right-hand side vectors. We outline the changes required. Received April 22, 1993     

Technical Note: Some Structural Properties of a Newton-Type Method for Semidefinite Programs

Using the minimum function or the Fischer-Burmeister function, we obtain two reformulations of a semidefinite program as a nonlinear system of equations. Applying a Newton-type method to such a reformulation leads to a linear system of equations which has to be solved at each iteration. We discuss some properties of this linear system and show that the corresponding coefficient matrix is symmetric positive definite for the minimum function approach and positive definite but unsymmetric for the Fischer-Burmeister formulation.     

Shifted skew-symmetric iteration methods for nonsymmetric linear complementarity problems

We present a shifted skew-symmetric iteration method for solving the nonsymmetric positive definite or positive semidefinite linear complementarity problems. This method is based on the symmetric and skew-symmetric splitting of the system matrix, which has been adopted to establish efficient splitting iteration methods for solving the nonsymmetric systems of linear equations. Global convergence of the method is proved, and the corresponding inexact splitting iteration scheme is established and analyzed in detail. Numerical results show that the new methods are feasible and effective for solving large sparse and nonsymmetric linear complementarity problems.     

一类特殊微分代数方程组的性质及应用

一类特殊微分代数方程组的性质及应用顾金生，胡显承（清华大学应用数学系）ＴＨＥＰＲＯＰＥＲＴＩＥＳＯＦＡＫＩＮＤＯＦＤＩＦＦＥＲＥＮＴＩＡＬ／ＡＬＧＥＢＲＡＩＣＥＱＵＡＴＩＯＮＳＡＮＤＴＨＥＩＲＡＰＰＬＩＣＡＴＩＯＮＳ￥ＧｕＪｉｎ－ｓｈｅｎｇ；ＨｕＸｉ...     

关于非Hermitian正定线性代数方程组的超松弛HSS方法

本文针对求解大型稀疏非Hermitian正定线性方程组的HSS迭代方法,利用迭代法的松弛技术进行加速,提出了一种具有三个参数的超松弛HSS方法(SAHSS)和不精确的SAHSS方法(ISAHSS),它采用CG和一些Krylov子空间方法作为其内部过程,并研究了SAHSS和ISAHSS方法的收敛性.数值例子验证了新方法的有效性.     

DIAGONALLY COMPENSATED REDUCTION AND MULTISPLITTING OF A SYMMETRIC POSITIVE DEFINITE MATRIX

1　ＩｎｔｒｏｄｕｃｔｉｏｎＬｅｔＡｂｅａｎｎ×ｎｓｙｍｍｅｔｒｉｃｐｏｓｉｔｉｖｅｄｅｆｉｎｉｔｅｍａｔｒｉｘ .ＷｅｃｏｎｓｉｄｅｒｔｈｅｉｔｅｒａｔｉｖｅｓｏｌｕｔｉｏｎｏｆａｌａｒｇｅｌｉｎｅａｒｓｙｓｔｅｍｏｆｅｑｕａｔｉｏｎｓＡｘ=ｂｂｙｔｈｅｐａｒａｌｌｅｌｍｕｌｔｉｓｐｌｉｔｔｉｎｇｍｅｔｈｏｄ .ＴｈｅｍｕｌｔｉｓｐｌｉｔｔｉｎｇｍｅｔｈｏｄｗａｓｉｎｔｒｏｄｕｃｅｄｂｙＯ’ＬｅａｒｙａｎｄＷｈｉｔｅ[9] ａｎｄｆｕｒｔｈｅｒｓｔｕｄｉｅｄｂｙｍａｎｙｏｔｈｅｒａｕｔｈｏｒｓ .Ｂｕｔｔ…     

Hermite正定对称矩阵迹的一些结果(英文)

本文研究了一类Hermite正定矩阵迹的不等式问题.利用文献[2-6]中的结果以及放缩法,获得了Hermite正定矩阵迹的极值定理、杨氏不等式和贝努利不等式,并且将许多初等不等式推广到Hermite正定矩阵迹的情形.     

Minimum residual methods for augmented systems

For large systems of linear equations, iterative methods provide attractive solution techniques. We describe the applicability and convergence of iterative methods of Krylov subspace type for an important class of symmetric and indefinite matrix problems, namely augmented (or KKT) systems. Specifically, we consider preconditioned minimum residual methods and discuss indefinite versus positive definite preconditioning. For a natural choice of starting vector we prove that when the definite and indenfinite preconditioners are related in the obvious way, MINRES (which is applicable in the case of positive definite preconditioning) and full GMRES (which is applicable in the case of indefinite preconditioning) give residual vectors with identical Euclidean norm at each iteration. Moreover, we show that the convergence of both methods is related to a system of normal equations for which the LSQR algorithm can be employed. As a side result, we give a rare example of a non-trivial normal(1) matrix where the corresponding inner product is explicitly known: a conjugate gradient method therefore exists and can be employed in this case. This work was supported by British Council/German Academic Exchange Service Research Collaboration Project 465 and NATO Collaborative Research Grant CRG 960782     

A MODIFIED INVERSE ITERATION FOR A LARGE SPARSE SPD GENERALIZED EIGENPROBLEM

In this paper, an algorithm based on a shifted inverse power iteration for computing generalized eigenvalues with corresponding eigenvectors of a large scale sparse symmetric positive definite matrix pencil is presented. It converges globally with a cubic asymptotic convergence rate, preserves sparsity of the original matrices and is fully parallelizable. The algebraic multilevel itera-tion method (AMLI) is used to improve the efficiency when symmetric positive definite linear equa-tions need to be solved.     

Detecting “dense” columns in interior point methods for linear programs

During the iterations of interior point methods symmetric indefinite systems are decomposed by LD̂L ^T factorization. This step can be performed in a special way where the symmetric indefinite system is transformed to a positive definite one, called the normal equations system. This approach proved to be efficient in most of the cases and numerically reliable, due to the positive definite property. It has been recognized, however, that in case the linear program contains “dense” columns, this approach results in an undesirable fill–in during the computations and the direct factorization of the symmetric indefinite system is more advantageous. The paper describes a new approach to detect cases where the system of normal equations is not preferable for interior point methods and presents a new algorithm for detecting the set of columns which is responsible for the excessive fill–in in the matrix AA ^T . By solving large–scale linear programming problems we demonstrate that our heuristic is reliable in practice. This work was supported in part by the Hungarian Scientific Research Fund OTKA K60480.     

特殊五对角与七对角对称正定矩阵的一类反问题

针对线性代数方程组Ax=b,利用矩阵分解的思想,构造一类特殊五对角与七对角对称正定阵的矩阵分解,获得这类矩阵反问题解存在的充要条件和通解表达式.最后,给出了具体算法与数值算例.     

Symmetric Gaussian elimination for cauchy-type matrices with application to positive definite Toeplitz matrices

The problem of solving linear equations with a Toeplitz matrix appears in many applications. Often is positive definite but ill-conditioned with many small eigenvalues. In this case fast and superfast algorithms may show a very poor behavior or even break down. In recent papers the transformation of a Toeplitz matrix into a Cauchy-type matrix is proposed. The resulting new linear equations can be solved in operations using standard pivoting strategies which leads to very stable fast methods also for ill-conditioned systems. The basic tool is the formulation of Gaussian elimination for matrices with low displacement rank. In this paper, we will transform a Hermitian Toeplitz matrix into a Cauchy-type matrix by applying the Fourier transform. We will prove some useful properties of and formulate a symmetric Gaussian elimination algorithm for positive definite . Using the symmetry and persymmetry of we can reduce the total costs of this algorithm compared with unsymmetric Gaussian elimination. For complex Hermitian , the complexity of the new algorithm is then nearly the same as for the Schur algorithm. Furthermore, it is possible to include some strategies for ill-conditioned positive definite matrices that are well-known in optimization. Numerical examples show that this new algorithm is fast and reliable. Received March 24, 1995 / Revised version received December 13, 1995     

On the sublinear and superlinear rate of convergence of conjugate gradient methods

The convergence of conjugate gradient methods for solving systems of linear equations with a symmetric positive definite matrix takes typically place in three phases, with a sublinear, linear and superlinear convergence rate, respectively. This behaviour can be explained using various generalized condition numbers which depend on all eigenvalues and on the initial error vector and using annihilating polynomials for the extreme eigenvalues. The analysis also indicates that it can be most efficient to use different preconditioners in different phases.     

m个对角元有正增量的对称正定方程组的解

1　引　　言某些问题的数值求解要作迭代计算 ,每次迭代需求解一个系数矩阵仅有少量变化的线性方程组 .如何减少求解该方程组的计算量 ,便成为提高总体计算效率的关键之一 .这类问题往往在一些优化问题的求解过程中遇到[1] ,因此值得研究 .为此考虑如下的问题Ⅰ .问题Ⅰ　设某问题的数值求解过程要作迭代计算 ,每次迭代需求解一个线性方程组(Ａ+Ｄ)Ｘ =ｂ ( 1 .1 )其中Ａ为ｎ阶对称正定矩阵 ,ｂ为已知向量 ,Ｄ =ｄｉａｇ(ｄ1,ｄ2 ,… ,ｄｎ) ,( 1 .2 )且Ｄ的对角元ｄｉｋ>0 ,ｋ =1 ,2 ,… ,ｍ ,1≤ｉ1<ｉ2 <… <ｉｍ ≤ｎ ,ｄｉｋ及其位置和…     

边界约束二次规划问题的分解方法

１．引言本文考虑如下边界约束的二次规划问题：其中ＱＥ＊"""是对称的,Ｃ,人。Ｅ＊"是给定的常数向量,且Ｚ＜。这类问题经常出现在偏微分方程,离散化的连续时间最优控制问题、线性约束的最小二乘问题、工程设计、或作为非线性规划方法中的序列子问题．因此具有特殊的重要性．本文提出求解问题（１．１）的分解方法．它类似求解线性代数方程组的选代法,它是对Ｑ进行正则分裂【对即把Ｑ分裂为两个矩阵之和,Ｑ＝Ｎ十片而这两个矩阵之差（Ｎ一则是对称正定的．在每次迭代中用一个易于求解的矩阵Ｎ替代Ｑ进行计算一新的二次规划问题．在适…