求解病态线性方程组的收缩Lanczos方法

Lanczos方法是求解大型线性方程组的常用方法.遗憾的是,在Lanczos过程中通常会发生算法中断或数值不稳定的情况.将给出求解大型对称线性方程组的收缩Lanczos方法,即DLanczos方法.新算法将采用增广子空间技术,在Lanczos过程中向Krylov子空间加入少量绝对值较小的特征值所对应的特征向量进行收缩.数值实验表明,新算法比Lanczos方法收敛速度更快,并且适合求解病态对称线性方程组.     

用方阵乘幂求和法求线性方程组的数值解

介绍了一种新型的,不同于传统的雅克比或高斯塞德尔迭代法的,求解线性方程组的方阵乘幂求和法,并引入了方阵意义上求积分的龙贝格法.该算法成立须以方阵A为实阵,非奇异且主对角元素占优.该法较雅克比或高斯塞德尔迭代的计算量小,特别有助于求解大型线性方程组的问题.     

线性方程组异步并行迭代法的舍入误差分析

本文对求解大型线性方程组的异步并行迭代法进行了浮点运算的舍入误差分析,给出了算法是向前稳定的充分条件.     

非对称稀疏线性方程组的快速外存解法及其在无网格法计算中的应用

针对局部Petrov-Galerkin无网格法(MLPG)等无网格方法的计算所产生的大型非对称稀疏线性方程组,介绍了一种新的直接解法．与一般非对称求解过程不同,该解法从现有的对称正定解法中演变出来,其分解过程在矩阵的上、下三角阵中对称进行．新的矩阵分解算法可以通过修改对称矩阵分解算法的代码来实现,这提供了从对称解法到非对称解法的快捷转换．还针对MLGP法以及有限元法所产生的方程组开发了多块外存算法(multi-blocked out-of-core strategy)来扩大求解规模．测试结果证明该方法大幅度提高了大型非对称稀疏线性方程组的求解速度．     

求解非对称线性方程组Lanczos算法的改进

考虑线性方程组求解问题这里A是大型稀疏、非对称和不定的可逆阵。求解问题(1)的双边Lanczos算法为算法1     

不等式约束优化一个可行序列线性方程组算法

提出了求解非线性不等式约束优化问题的一个可行序列线性方程组算法. 在每次迭代中, 可行下降方向通过求解两个线性方程组产生, 系数矩阵具有较好的稀疏性. 在较为温和的条件下, 算法具有全局收敛性和强收敛性, 数值试验表明算法是有效的.     

并行区间矩阵多分裂AOR算法及其收敛性

本文提出了一类求解大型区间线性方程组的并行区间矩阵多分裂松弛算法,并在系数矩阵是区间H-矩阵的条件下,建立了这类算法的收敛理论。     

不等式约束优化一个新型可行QP-free算法

本文对非线性不等式约束优化问题提出了一个新的可行 QP-free 算法. 新算法保存了现有算法的优点, 并具有以下特性: (1) 算法每次迭代只需求解三个具有相同系数矩阵的线性方程组, 计算量小; (2) 可行下降方向只需通过求解一个线性方程组即可获得, 克服了以往分别求解两个线性方程组获得下降方向和可行方向, 然后再做凸组合的困难;(3) 迭代点均为可行点, 并不要求是严格内点; (4) 算法中采用了试探性线搜索,可以进一步减少计算量; (5) 算法中参数很少,数值试验表明算法具有较好的数值效果和较强的稳定性.     

一个拟极小化左共轭梯度算法及其数值表现

左共轭梯度法是求解大型稀疏线性方程组的一种新兴的Krylov子空间方法.为克服该算法数值表现不稳定、迭代中断的缺点,本文对原方法进行等价变形,得到左共轭梯度方向的另一迭代格式,给出一个拟极小化左共轭梯度算法.数值结果证实了该变形算法与原算法的相关性.     

非Hermite线性方程组的若干预处理迭代算法

非Hermite线性方程组在科学和工程计算中有着重要的理论研究意义和使用价值,因此如何高效求解该类线性方程组,一直是研究者所探索的方向.通过提出一种预处理方法,对非Hermite线性方程组和具有多个右端项的复线性方程组求解的若干迭代算法进行预处理,旨在提高原算法的收敛速度.最后通过数值试验表明,所提出的若干预处理迭代算法与原算法相比较,预处理算法迭代次数大大降低,且收敛速度明显优于原算法.除此之外,广义共轭A-正交残量平方法(GCORS2)的预处理算法与其他算法相比,具有良好的收敛性行为和较好的稳定性.     

Asymptotical Stability in Probability for Stochastic Bilinear Systems with Markovian Switching

This paper deals with asymptotical stability in probability in the large for stochastic bilinear systems. Some new criteria for asymptotical stability of such systems have been established in the inequality of mathematic expectation. A sufficient condition for bilinear stochastic jump systems to be asymptotically stable in probability in the large in Markovian switching laws is derived in a couple of Riccati-like inequalities by introducing a nonlinear state feedback controller. An illustrative example shows the effectiveness of the method.     

Applications of statistical condition estimation to the solution of linear systems

This paper discusses some applications of statistical condition estimation (SCE) to the problem of solving linear systems. Specifically, triangular and bidiagonal matrices are studied in some detail as typical of structured matrices. Such a structure, when properly respected, leads to condition estimates that are much less conservative compared with traditional non‐statistical methods of condition estimation. Some examples of linear systems and Sylvester equations are presented. Vandermonde and Cauchy matrices are also studied as representative of linear systems with large condition numbers that can nonetheless be solved accurately. SCE reflects this. Moreover, SCE when applied to solving very large linear systems by iterative solvers, including conjugate gradient and multigrid methods, performs equally well and various examples are given to illustrate the performance. SCE for solving large linear systems with direct methods, such as methods for semi‐separable structures, are also investigated. In all cases, the advantages of using SCE are manifold: ease of use, efficiency, and reliability. Copyright © 2008 John Wiley & Sons, Ltd.     

Superfast Iterative Solvers for Linear Matrix Equations

Superfast algorithms for solving large systems of linear equations are developed on the basis of an original method for multistep decomposition of a linear multidimensional dynamical system. Examples of analytical synthesis of iterative solvers for matrices of the general form and for large numerical systems of linear algebraic equations are given. For the analytical case, it is shown that convergence occurs at the second iteration.     

基于复杂项目群优化选择的水电经济效益计算

针对目前已运行水电站经济效益计算存在的问题,本文提出了基于大系统分解协调模型选择水电站在不同运行时间的最优等效替代运行位置.考虑目前电力系统中正在逐步推行的两部制电价（容量电价与电量电价）,研究中分别计算了已运行水电站最优等效替代位置的边际容量成本和边际电量成本,并将其分别作为已运行水电站优化运行中各时段（刻）经济效益计算的容量价格与电量价格.以运行水电站经济效益最大作为优化准则,采用动态规划方法对其进行实时调度,从而实现其经济运行调度.文章给出的实例计算表明,本文提出的方法与模型是可行的.     

A relaxation method for solving systems with infinitely many linear inequalities

The problem of finding a feasible solution to a linear inequality system arises in numerous contexts. We consider solving linear semi-infinite inequality systems via an extension of the relaxation method for finite linear inequality systems. The difficulties are discussed and a convergence result is derived under fairly general assumptions on a large class of linear semi-infinite inequality systems.     

Parallel algorithms for solving large linear systems

The solution of linear systems continues to play an important role in scientific computing. The problems to be solved often are of very large size, so that solving them requires large computer resources. To solve these problems, at least supercomputers with large shared memory or massive parallel computer systems with distributed memory are needed.

This paper gives a survey of research on parallel implementation of various direct methods to solve dense linear systems. In particular are considered: Gaussian elimination, Gauss-Jordan elimination and a variant due to Huard (1979), and an algorithm due to Enright (1978), designed in relation to solving (stiff) ODEs, such that stepsize and other method parameters can easily be varied.

Some theoretical results are mentioned, including a new result on error analysis of Huard's algorithm. Moreover, practical considerations and results of experiments on supercomputers and on a distributed-memory computer system are presented.     

Circulant-block preconditioners for solving ordinary differential equations

Boundary value methods for solving ordinary differential equations require the solution of non-symmetric, large and sparse linear systems. In this paper, these systems are solved by using the generalized minimal residual (GMRES) method. A circulant-block preconditioner is proposed to speed up the convergence rate of the GMRES method. Theoretical and practical arguments are given to show that this preconditioner is more efficient than some other circulant-type preconditioners in some cases. A class of waveform relaxation methods is also proposed to solve the linear systems.     

The linear span of the frequency hopping sequences in optimal sets

Frequency hopping (FH) sequences are needed in FH code division multiple access (CDMA) systems. Recently some new constructions of optimal sets of FH sequences were presented. For the anti-jamming purpose, FH sequences are required to have a large linear span. The objective of this paper is to determine both the linear spans and the minimal polynomials of the FH sequences in these optimal sets. Furthermore, the linear spans of the transformed FH sequences by applying a power permutation are also investigated. If the power is chosen properly, the linear span could be very large compared to the length of the FH sequences.     

A normalized sparse linear equations solver

Normalized factorization procedures for the solution of large sparse linear finite element systems have been recently introduced in [3]. In these procedures the large sparse symmetric coefficient matrix of irregular structure is factorized exactly to yield a normalized direct solution method. Additionally, approximate factorization procedures yield implicit iterative methods for the finite difference or finite element solution. The numerical implementation of these algorithms is presented here and FORTRAN subroutines for the efficient solution of the resulting large sparse symmetric linear systems of algebraic equations are given.     

Applying approximate LU-factorizations as preconditioners in eight iterative methods for solving systems of linear algebraic equations

Many problems arising in different fields of science and engineering can be reduced, by applying some appropriate discretization, either to a system of linear algebraic equations or to a sequence of such systems. The solution of a system of linear algebraic equations is very often the most time-consuming part of the computational process during the treatment of the original problem, because these systems can be very large (containing up to many millions of equations). It is, therefore, important to select fast, robust and reliable methods for their solution, also in the case where fast modern computers are available. Since the coefficient matrices of the systems are normally sparse (i.e. most of their elements are zeros), the first requirement is to efficiently exploit the sparsity. However, this is normally not sufficient when the systems are very large. The computation of preconditioners based on approximate LU-factorizations and their use in the efforts to increase further the efficiency of the calculations will be discussed in this paper. Computational experiments based on comprehensive comparisons of many numerical results that are obtained by using ten well-known methods for solving systems of linear algebraic equations (the direct Gaussian elimination and nine iterative methods) will be reported. Most of the considered methods are preconditioned Krylov subspace algorithms.