计算部分奇异值分解的隐式重新启动的双对角化Lanczos方法和精化的双对角化Lanczos方法

The singular value decomposition problem is mathematically equivalent to the eigenproblem of an argumented matrix. Golub et al. give a bidiagonalization Lanczos method for computing a number of largest or smallest singular values and corresponding singular vertors, but the method may encounter some convergence problems. In this paper we analyse the convergence of the method and show why it may fail to converge. To correct this possible nonconvergence, we propose a refined bidiagonalization Lanczos method and apply the implicitly restarting technique to it, and we then present an implicitly restarted bidiagonalization Lanczos algorithm(IRBL) and an implicitly restarted refined bidiagonalization Lanczos algorithm (IRRBL). A new implicitly restarting scheme and a reliable and efficient algorithm for computing refined shifts are developed for this special structure eigenproblem.Theoretical analysis and numerical experiments show that IRRBL performs much better than IRBL.     

添加近似误差的重新启动的simpler GMRES方法

本文给出了重新启动的LGMRES方法的一种代价更小的实现方式.这种做法基于消除以下减慢收敛速度的现象:重新启动的simpler GMRES的每次循环结束时得到的残向量经常交替方向,与重新启动的GMRES的情形类似.这种新的变形的方法的优点是它比重新启动的LGMRES所需要的计算量要少.大量的例子表明该方法计算速度更快.     

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.     

解信赖域子问题的隐式分段折线算法

在Hessian矩阵正定的前提下,建立了一种最优曲线的微分方程模型.针对此微分方程模型,构造了一条隐式分段折线,从而提出了一种求解信赖域子问题的隐式分段折线算法,并且分析和证明了隐式分段折线路径的合理性.数值结果表明新算法是有效且可行的.     

计算大规模矩阵最大最小奇异值和奇异向量的两个精化Lanczos算法

1.引言 在科学工程计算中经常需要计算大规模矩阵的少数最大或最小的奇异值及其所对应的奇异子空间。例如图像处理中要计算矩阵端部奇异值之比作为图像的分辨率,诸如此类的问题还存在于最小二乘问题、控制理论、量子化学中等等。然而大多实际问题中的矩阵是大型稀疏矩阵,且需要的是矩阵的部分奇异对。如果计算A的完全奇异值分解(SVD),则运算量和存储量极大,甚至不可能。因此必须寻求其它有效可靠的算法。 假设A的SVD为     

最小连通顶点覆盖问题的降阶回溯算法

本文从最小连通顶点覆盖问题的求解算法出发,提出一种基于该问题本身的数学性质的降阶回溯算法来求解。通过基于问题的数学性质来设计精确算法,不仅能够克服使用启发式算法求解该问题在一般情形下都无法求得最优解的缺点,也改善了该问题使用传统精确算法时最坏时间复杂度高的缺点。本文首先研究该问题的数学性质,部分数学性质可成批确定某些顶点在或不在最小连通顶点覆盖集中,从而降低该问题的规模,提高精确算法的求解速度。其次,在数学性质的基础上,设计出上下界子算法、降阶子算法、回溯子算法来求解该问题的最优解。最后,时间复杂度分析以及无线网络设计的实例分析表明,该算法不仅能求得该问题的最优解,且相对一般精确算法,本文算法的时间复杂度更低。     

Banach空间中的隐式集值变分包含的一种扰动算法

在Banach空间中研究了一类新的变分包含——隐式集值变分包含问题，得到了隐式变分包含解的等价性与存在性命题及其解的扰动算法，推广、改进了国内外近期获得的一些结果．     

基于Lanczos双A-正交的一种修正的QMR算法

本文研究了基于Lanczos双正交过程的拟极小残量法(QMR).将QMR算法中的Lanczos双正交过程用Lanczos双A-正交过程代替,利用该算法得到的近似解与最后一个基向量的线性组合来作为新的近似解,使新近似解的残差范数满足一个一维极小化问题,从而得到一种基于Lanczos双A-正交的修正的QMR算法.数值试验表明,对于某些大型线性稀疏方程组,新算法比QMR算法收敛快得多.     

基于降阶模型和数据驱动的动态结构数字孪生方法北大核心CSCD

针对受动载荷作用的结构,提出了一种基于降阶模型库和机器学习数据驱动的数字孪生构建方法.首先根据物理结构服役过程中可能出现的损伤状态,采用有限单元法建立高保真有限元模型.其次采用Krylov子空间模型降阶方法对模型进行降阶,建立了物理结构各种状态下的降阶模型,形成模型库.最后利用随机森林机器学习算法训练获得模型选择器,通过物理结构上传感器的数据推断当前物理结构的状态,驱动数字孪生体跟随物理结构一同演化.设计制作了一个框架结构物理模型,模拟了结构不同位置损伤及不同损伤程度,验证了提出的数字孪生构建方法.     

基于EM算法隐Markov模型参数估计

描述最大似然参数估计问题,介绍如何用EM算法求解最大似然参数估计.首先给出EM算法的抽象形式,然后介绍EM算法的一个应用:求隐Markov模型中的参数估计.用EM算法推导出隐Markov模型中参数的迭代公式.     

Rational Lanczos approximations to the matrix square root and related functions

We consider restricted rational Lanczos approximations to matrix functions representable by some integral forms. A convergence analysis that stresses the effectiveness of the proposed method is developed. Error estimates are derived. Numerical experiments are presented. Copyright © 2008 John Wiley & Sons, Ltd.     

Restarted FOM Augmented with Ritz Vectors for Shifted Linear Systems

The restarted FOM method presented by Simoncini[7]according to the natural collinearity of all residuals is an efficient method for solving shifted systems,which generates the same Krylov subspace when the shifts are handled simultaneously.However,restarting slows down the convergence.We present a practical method for solving the shifted systems by adding some Ritz vectors into the Krylov subspace to form an augmented Krylov subspace. Numerical experiments illustrate that the augmented FOM approach(restarted version)can converge more quickly than the restarted FOM method.     

Restarted block Lanczos bidiagonalization methods

The problem of computing a few of the largest or smallest singular values and associated singular vectors of a large matrix arises in many applications. This paper describes restarted block Lanczos bidiagonalization methods based on augmentation of Ritz vectors or harmonic Ritz vectors by block Krylov subspaces. Research supported in part by NSF grant DMS-0107858, NSF grant DMS-0311786, and an OBR Research Challenge Grant.     

Implicitly restarted projection algorithm for solving optimization problems

An algorithm for solving the problem of minimizing a quadratic function subject to ellipsoidal constraints is introduced. This algorithm is based on the impHcitly restarted Lanczos method to construct a basis for the Krylov subspace in conjunction with a model trust region strategy to choose the step. The trial step is computed on the small dimensional subspace that lies inside the trust region.

One of the main advantages of this algorithm is the way that the Krylov subspace is terminated. We introduce a terminationcondition that allows the gradient to be decreased on that subspace.

A convergence theory for this algorithm is presented. It is shown that this algorithm is globally convergent and it shouldcope quite well with large scale minimization problems. This theory is sufficiently general that it holds for any algorithm that projects the problem on a lower dimensional subspace.     

Restarted Full Orthogonalization Method for Shifted Linear Systems

Restarted GMRES is known to be inefficient for solving shifted systems when the shifts are handled simultaneously. Variants have been proposed to enhance its performance. We show that another restarted method, restarted Full Orthogonalization Method (FOM), can effectively be employed. The total number of iterations of restarted FOM applied to all shifted systems simultaneously is the same as that obtained by applying restarted FOM to the shifted system with slowest convergence rate, while the computational cost grows only sub-linearly with the number of shifts. Numerical experiments are reported.     

Model reduction of state space systems via an implicitly restarted Lanczos method

The nonsymmetric Lanczos method has recently received significant attention as a model reduction technique for large-scale systems. Unfortunately, the Lanczos method may produce an unstable partial realization for a given, stable system. To remedy this situation, unexpensive implicit restarts are developed which can be employed to stabilize the Lanczos generated model.This work was supported in part by ARPA (US Army ORA4466.01), by ARPA (Grant 60NANB2D1272), by the Department of Energy (Contract DE-FG0f-91ER25103) and by the National Science Foundation (Grants CCR-9209349 and CCR-9120008).     

Shift‐invert Lanczos method for the symmetric positive semidefinite Toeplitz matrix exponential

The Lanczos method with shift‐invert technique is exploited to approximate the symmetric positive semidefinite Toeplitz matrix exponential. The complexity is lowered by the Gohberg–Semencul formula and the fast Fourier transform. Application to the numerical solution of an integral equation is studied. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

An Implicitly Restarted Block Arnoldi Method in a Vector-Wise Fashion

In this paper, we develop an implicitly restarted block Arnoldi algorithm in a vector-wise fashion. The vector-wise construction greatly simplifies both the detection of necessary deflation and the actual deflation itself, so it is preferable to the block-wise construction. The numerical experiment shows that our algorithm is effective.     

A review of formal orthogonality in Lanczos-based methods

Krylov subspace methods and their variants are presently the favorite iterative methods for solving a system of linear equations. Although it is a purely linear algebra problem, it can be tackled by the theory of formal orthogonal polynomials. This theory helps to understand the origin of the algorithms for the implementation of Krylov subspace methods and, moreover, the use of formal orthogonal polynomials brings a major simplification in the treatment of some numerical problems related to these algorithms. This paper reviews this approach in the case of Lanczos method and its variants, the novelty being the introduction of a preconditioner.