求解病态线性方程组的模拟退火算法

病态方程组的条件数较大,当输入数据有微小扰动或计算过程中的舍入误差都可能引起输出数据的很大扰动,使得解严重失真,因此求解此类方程组是相当困难的.本文尝试使用模拟退火算法来求解病态线性方程组,得到了较好的结果,并与传统的求解方法作了简单的比较.     

泊松方程离散系统的病态原理和通用预条件子CSCD

1引言泊松方程的数值求解问题,通常转化为如下离散系统一一线性方程组的求解问题[1],Ax=b(1.1)大规模求解时,方程组的病态(高条件数)问题凸显,并且求解规模越大,该方程组的条件数也越大,病态越严重[2],是影响求解效率和精度的瓶颈因素,因此,在大规模求解过程中,使用预处理技术来降低方程组的条件数,减少病态,是成功求解的关键.     

关于线性代数方程的一种迭代解法的收敛性问题

用迭代法求解线性代数方程组,已有大量的文献与专著,例如[4、6、7]。最常用的是逐次超松弛,及其种种变形。但是,许多情况表明这些方法并非完全令人满意的,特别对病态线性代数方程组,即方程组的系数矩阵有大的条件数,用这些方法求解时,收敛得相当慢。 [1]对求解病态常微分方程初值问题构造了一种恒稳格式。从线性代数方程组的解,等价于某一常微分方程组初值问题的稳态解,这一事实出发,从而构造了一种新的求解线性代数方程组的迭代解法。[1、2]某些计算实例表明,此迭代法特别适合于求解病态线性     

三维泊松方程边值问题3次Lagrange有限元方程的病态结构和最优预条件子

正1引言在科学计算和工程应用中,偏微分方程大规模数值求解问题通常转化为病态(高条件数)的大规模稀疏线性方程组的求解问题,其条件数(病态)经常随着问题规模的增加而增加[1],成为影响求解效率和精度的瓶颈因素,因此,在求解之前,使用预处理技术来减少方程组的病态,成为提高求解效率和精度的必要措施.所谓"预处理技术"是指在求解方程组     

条件数与方程组扰动理论一些问题的探讨

以问答方式,针对数值分析教材中关于线代数方程组扰动理论的若干问题进行了探讨,如条件数与方程组有何关系,条件数大是否意味着方程组一定病态,是否存在条件数大但不病态的问题,扰动估计式的上界何时达到等,并结合实例对这些概念和问题进行了阐述.     

改进的预处理共轭斜量法及其在工程有限元分析中的应用

本文就预处理共轭斜量法(PCCG法)给出了两个具有理论和实际意义的定理,它们分别讨论了迭代解的定性性质和迭代矩阵的构造原则.作者提出了新的非M-矩阵的不完全LU分解技术和迭代矩阵的构造方法.用此改进的PCCG法,对病态问题和大型三维有限元问题进行了计算并与其他方法作了对比,分析了PCCG法在求解病态方程组时的反常现象.计算结果表明本文建议的方法是求解大型有限元方程组和病态方程组的一种十分有效的方法.     

线性方程组数值解的有效位数判定

J.H.Wilkinson指出:“……对一个计算解的误差建立可靠的界,这个界对病态方程组的精密的解来说也不是悲观的,这决不是一件简单的事”。至于需要准确指出计算解有几位有效数字,通常对于较良态的方程组也未必可能;而对于病态方程组就更加困难。 再者,人们分析过许多算法,指出某算法较之某另一算法的数值稳定性强(例如,线性方程组用QR分解来求解较之用部分选主元的Gauss消去法求解数值稳定性强),但是,就一个具体的方程组而言,用数值稳定性较强的算法得到的解,是否一定优于用数     

MATLAB中大型线性方程组的非定常迭代法

科学研究和大型工程设计中很多问题以非线性数学模型来描述,而这些数学模型求解常常归结为各种大型线性方程组的求解,因而能否有效地求解大型线性方程组,特别是病态的方程组,是非常关键的.本文介绍了MATLAB中求解大型线性方程组常用的非定常迭代法,并以GMRES算法为例介绍了算法的数学描述.     

病态线性方程组的判定方法

针对用条件数来衡量方程组的性态将随阶数增大而变得异常困难这一问题,分析了病态线性方程组产生的原因,提出了一种判定方法,探讨了对一定精度要求的解的可允许扰动的数量级,实例证明了这种方法的有效性.     

求解病态问题的一种改进的Tikhonov正则化：⑴正则化方法的建立

对于带有右扰动数据的第一类紧算子方程的病态问题。本文应用正则化子建立了一类新的正则化求解方法,称之为改进的Ｔｉｋｏｎｏｖ正则化;通过适当选取２正则参数,证明了正则解具有最优的渐近收敛阶,与通常的Ｔｉｋｈｏｎｏｖ正则化相比,这种改进的正则化可使正则解取到足够高的最优渐近阶。     

A comparison of the Newton-Krylov method with high order Newton-like methods to solve nonlinear systems

We compare the CPU time and error estimates of some variants of Newton method of the third and fourth-order convergence with those of the Newton-Krylov method used to solve systems of nonlinear equations. By expanding some numerical experiments we show that the use of Newton-Krylov method is better in the cost and accuracy points of view than the use of other high order Newton-like methods when the system is sparse and its size is large.     

一类弱非线性方程组的Picard-MHSS迭代方法

修正的Hermite/反Hermite分裂(MHSS)迭代方法是一类求解大型稀疏复对称线性代数方程组的无条件收敛的迭代算法.基于非线性代数方程组的特殊结构和性质,我们选取Picard迭代为外迭代方法,MHSS迭代作为内迭代方法,构造了求解大型稀疏弱非线性代数方程组的Picard-MHSS和非线性MHSS-like方法.这两类方法的优点是不需要在每次迭代时均精确计算和存储Jacobi矩阵,仅需要在迭代过程中求解两个常系数实对称正定子线性方程组.除此之外,在一定条件下,给出了两类方法的局部收敛性定理.数值结果证明了这两类方法是可行、有效和稳健的.     

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.     

Optimal subsampling for least absolute relative error estimators with massive data

Due to the limitation of computational resources, traditional statistical methods are no longer applicable to large data sets. Subsampling is a popular method which can significantly reduce computational burden. This paper considers a subsampling strategy based on the least absolute relative error in the multiplicative model for massive data. In addition, we employ the random weighting and the least squares methods to handle the problem that the asymptotic covariance of the estimator is difficult to be estimated directly. Moreover, the comparison among the least absolute relative error, least absolute deviation and least squares under the optimal subsampling strategy are given in simulation studies and real examples.     

MULTILEVEL AUGMENTATION METHODS FOR SOLVING OPERATOR EQUATIONS

We introduce multilevel augmentation methods for solving operator equations based on direct sum decompositions of the range space of the operator and the solution space of the operator equation and a matrix splitting scheme. We establish a general setting for the analysis of these methods, showing that the methods yield approximate solutions of the same convergence order as the best approximation from the subspace. These augmentation methods allow us to develop fast, accurate and stable nonconventional numerical algorithms for solving operator equations. In particular, for second kind equations, special splitting techniques are proposed to develop such algorithms. These algorithms are then applied to solve the linear systems resulting from matrix compression schemes using wavelet-like functions for solving Fredholm integral equations of the second kind. For this special case, a complete analysis for computational complexity and convergence order is presented. Numerical examples are included to demonstra     

Krylov eigenvalue strategy using the FEAST algorithm with inexact system solves

The FEAST eigenvalue algorithm is a subspace iteration algorithm that uses contour integration to obtain the eigenvectors of a matrix for the eigenvalues that are located in any user‐defined region in the complex plane. By computing small numbers of eigenvalues in specific regions of the complex plane, FEAST is able to naturally parallelize the solution of eigenvalue problems by solving for multiple eigenpairs simultaneously. The traditional FEAST algorithm is implemented by directly solving collections of shifted linear systems of equations; in this paper, we describe a variation of the FEAST algorithm that uses iterative Krylov subspace algorithms for solving the shifted linear systems inexactly. We show that this iterative FEAST algorithm (which we call IFEAST) is mathematically equivalent to a block Krylov subspace method for solving eigenvalue problems. By using Krylov subspaces indirectly through solving shifted linear systems, rather than directly using them in projecting the eigenvalue problem, it becomes possible to use IFEAST to solve eigenvalue problems using very large dimension Krylov subspaces without ever having to store a basis for those subspaces. IFEAST thus combines the flexibility and power of Krylov methods, requiring only matrix–vector multiplication for solving eigenvalue problems, with the natural parallelism of the traditional FEAST algorithm. We discuss the relationship between IFEAST and more traditional Krylov methods and provide numerical examples illustrating its behavior.     

On HSS-based iteration methods for weakly nonlinear systems

Based on separable property of the linear and the nonlinear terms and on the Hermitian and skew-Hermitian splitting of the coefficient matrix, we present the Picard-HSS and the nonlinear HSS-like iteration methods for solving a class of large scale systems of weakly nonlinear equations. The advantage of these methods over the Newton and the Newton-HSS iteration methods is that they do not require explicit construction and accurate computation of the Jacobian matrix, and only need to solve linear sub-systems of constant coefficient matrices. Hence, computational workloads and computer memory may be saved in actual implementations. Under suitable conditions, we establish local convergence theorems for both Picard-HSS and nonlinear HSS-like iteration methods. Numerical implementations show that both Picard-HSS and nonlinear HSS-like iteration methods are feasible, effective, and robust nonlinear solvers for this class of large scale systems of weakly nonlinear equations.     

A UNIFIED A POSTERIORI ERROR ANALYSIS FOR DISCONTINUOUS GALERKIN APPROXIMATIONS OF REACTIVE TRANSPORT EQUATIONS

Four primal discontinuous Galerkin methods are applied to solve reactive transportproblems, namely, Oden-Babuska-Baumann DG (OBB-DG), non-symmetric interior penaltyGalerkin (NIPG), symmetric interior penalty Galerkin (SIPG), and incomplete interiorpenalty Galerkin (IIPG). A unified a posteriori residual-type error estimation is derivedexplicitly for these methods. From the computed solution and given data, explicit esti-mators can be computed efficiently and directly, which can be used as error indicators foradaptation. Unlike in the reference [10], we obtain the error estimators in L~2 (L~2) norm byusing duality techniques instead of in L~2 (H~1) norm.     

广义Pareto分布的广义有偏概率加权矩估计方法

广义Pareto分布(GPD)是统计分析中一个极为重要的分布,被广泛应用于金融、保险、水文及气象等领域.传统的参数估计方法如极大似然估计、矩估计及概率加权矩估计方法等已被广泛应用,但使用中存在一定的局限性.虽然提出很多改进方法如广义概率加权矩估计、L矩和LH矩法等,但都是研究完全样本的估计问题,而在水文及气象等应用领域常出现截尾样本.本文基于概率加权矩理论,利用截尾样本对三参数GPD提出一种应用范围广且简单易行的参数估计方法,可有效减弱异常值的影响.首先求解出具有较高精度的形状参数的参数估计,其次得出位置参数及尺度参数的参数估计.通过Monte Carlo模拟说明该方法估计精度较高.