基于SVD的本征正交分解算法在偏微分方程中的降阶数值模式研究

本文分别论述全矩阵、距平矩阵以及归一化矩阵的奇异正交分解(Singular Value Decomposition,简称SVD)算法的理论基础,推导了任意矩阵的SVD分解过程并且在任意矩阵SVD分解的基础上,给出两种本征正交分解(Proper Orthogonal Decomposition,简称POD)算法,将POD算法与Galerkin投影相结合可以将偏微分方程的高维或者无穷维解投影到POD模态构成的完备空间中进行降阶模拟,进而得到高度近似的低维解,比较用不同阶POD模态降阶前后解的稳定性及精确性.最后给出数值算例分析两种本征正交分解算法的优劣性及适用性.     

基于马尔科夫过程的储备系统稳态性能分析

由于储备系统组成部件在存储期间的失效概率各不相同,当部件状态趋于稳定时,各个状态对系统性能的影响也存在差异。为了识别关键部件及其状态对系统性能的影响程度,本文以重要度为主要指标,应用马尔科夫过程研究储备系统在稳态时的性能变化模式。首先基于综合重要度研究系统性能的变化规律,并结合冷储备系统和温储备系统的状态转移矩阵推导出马尔科夫过程中稳态值的计算方法;其次基于稳态综合重要度获得系统稳态时的性能变化模式;最后以双臂机器人为例,分析部件处于不同状态时对系统性能的影响模式,比较了不同部件综合重要度的变化,验证了提出方法的有效性。     

水平线性互补问题投影迭代法和模系矩阵分裂迭代法的等价性

水平线性互补问题(HLCP)是著名线性互补问题(LCP)的重要推广形式之一,投影迭代法和模系矩阵分裂迭代法是最近提出的求解HLCP两类非常有效的热点方法.本文研究表明,尽管这两类方法导出原理不同,但在一定条件下是等价的.特别地,当模系矩阵分裂迭代法中参数矩阵Ω取为特定的正对角矩阵时,投影Jacobi法、投影Gauss-Seidel法和投影SOR法分别等价于模系Jacobi迭代法、加速的模系Gauss-Seidel迭代法和加速的模系SOR迭代法.此外,对一般的正对角矩阵Ω,本文也研究了两类方法的等价性.最后,通过数值算例验证了本文的理论结果.     

非线性系统动力分析的模态综合技术

各种模态综合方法已广泛应用于线性结构的动力分析,但是,一般都不适用于非线性系统. 本文基于[20][21]提出的方法,将一种模态综合技术推广到非线性系统的动力分析.该法应用于具有连接件耦合的复杂结构系统,以往把连接件简化为线性弹簧和阻尼器.事实上,这些连接件通常具有非线性弹性和非线性阻尼特性.例如,分段线性弹簧、软特性或硬特性弹簧、库伦阻尼、弹塑性滞后阻尼等.但就各部件而言,仍属线性系统.可以通过计算或试验或兼由两者得到一组各部件的独立的自由界面主模态信息,且只保留低阶主模态.通过连接件的非线性耦合力,集合各部件运动方程而建立成总体的非线性振动方程.这样问题就成为缩减了自由度的非线性求解方程,可以达到节省计算机的存贮和运行时间的目的.对于阶次很高的非线性系统,若能缩减足够的自由度,那么问题就可在普通的计算机上得以解决. 由于一般多自由度非线性振动系统的复杂性,一般而言,这种非线性方程很难找到精确解.因此,对于任意激励下系统的瞬态响应,可以采用数值计算方法求解缩减的非线性方程.     

单输入单输出大规模动力系统模型的简化方法

提出的简化单输入单输出大规模动力系统的一种新方法是系统在等式约束最小二乘法的一种推广.这种方法是一种投影方法,其投影依赖于奇异分解和Krylov子空间.通过平移算子,使得降阶模型与原模型的前r+i模准确地匹配,剩余的高阶模利用拉格朗日乘子法进行等式约束最小二乘的形式逼近原模.通过拉格朗日乘子法来求解具有约束条件的最小二乘问题,让推导出来的用于模型简化的投影变换矩阵更为简便.     

构建不确定语言型多属性决策的投影模型

研究不确定语言型多属性决策评价结果与决策者对方案的偏好信息之间存在偏差的问题.通过建立与区间型语言标度对应的术语指标矩阵,及方案综合属性值与决策者主观偏好值之间的投影模型,确定属性的权重,然后运用加权法得到方案的综合属性值,利用已有的可能度矩阵排序公式得到决策方案的排序.构建了一种基于方案综合属性质与决策者主观偏好值之间的投影模型,通过算例对该方法的实用性和有效性进行了证明.     

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考察一类Markov切换时变时滞随机系统的均方指数稳定性. 利用基于Liapunov函数和线性矩阵不等式的方法, 给出了使状态反馈控制系统能克服不确定性和随机干扰, 在均方意义下达到指数稳定的充分条件. 当Markov链遍历所有模态时, 给出了一个独立于Markov链模态集的增益矩阵, 使得状态反馈控制系统均方指数稳定     

解凸约束非线性单调方程组的无导数低存储Broyden族投影法

拟牛顿法是求解非线性方程组的一类有效方法.相较于经典的牛顿法,拟牛顿法不需要计算Jacobian矩阵且仍具有超线性收敛性.本文基于BFGS和DFP的迭代公式,构造了新的充分下降方向.将该搜索方向和投影技术相结合,本文提出了无导数低存储的投影算法求解带凸约束的非线性单调方程组并证明了该算法是全局且R-线性收敛的.最后,将该算法用于求解压缩感知问题.实验结果表明,本文所提出的算法具有良好的计算效率和稳定性.     

矩阵束最佳逼近问题的交替投影法

研究给定矩阵束的最佳逼近问题,这类问题出现在同时修正有限元模型质量矩阵和刚度矩阵的无阻尼结构系统.以矩阵束修正量的F-范数为目标函数,以待修正矩阵束应具有的性质,如满足特征方程、对称半正定性和稀疏性作为约束条件,形成带约束的矩阵束最佳逼近问题.基于交替投影方法,提出了求解矩阵束最佳逼近问题的一个数值方法.数值结果显示了新方法的有效性.     

同时考虑故障相关和变故障率的可修系统可靠性计算

基于Copula相关性理论,考虑可修系统零部件工作寿命、故障部件修复时间之间的正相关性,且将零件工作寿命、修复时间放宽到一般连续分布,而不局限于指数分布.提出微时间差t→t+△t内系统一步状态转移矩阵概念,进而演算出状态转移密度矩阵,经系统状态方程,分别给出了任意时刻t单部件、串联型、二不同单元和一修理工组成的并联可修系统的可用度和稳态可用度计算模型.通过算例,说明该理论方法的可行性.     

A-posteriori residual bounds for Arnoldi’s methods for nonsymmetric eigenvalue problems

Convergence of the implicitly restarted Arnoldi (IRA) method for nonsymmetric eigenvalue problems has often been studied by deriving bounds for the angle between a desired eigenvector and the Krylov projection subspace. Bounds for residual norms of approximate eigenvectors have been less studied and this paper derives a new a-posteriori residual bound for nonsymmetric matrices with simple eigenvalues. The residual vector is shown to be a linear combination of exact eigenvectors and a residual bound is obtained as the sum of the magnitudes of the coefficients of the eigenvectors. We numerically illustrate that the convergence of the residual norm to zero is governed by a scalar term, namely the last element of the wanted eigenvector of the projected matrix. Both cases of convergence and non-convergence are illustrated and this validates our theoretical results. We derive an analogous result for implicitly restarted refined Arnoldi (IRRA) and for this algorithm, we numerically illustrate that convergence is governed by two scalar terms appearing in the linear combination which drives the residual norm to zero. We provide a set of numerical results that validate the residual bounds for both variants of Arnoldi methods.     

线性方程组的多维投影算法

本文分析了求解线性方程组的一维投影算法即最小剩余法。定义了长轴陷阱及陷阱深度，用它们刻划了该算法迭代过程中锯齿现象的几何特征。本文给出了基于残差序列的避开长轴陷阱的扰动技巧，即多维投影算法。数值试验表明，投影算法要优于现在流行的主要算法。     

Biconjugate direction methods in Krylov subspaces

The paper addresses the orthogonal and variational properties of a family of iterative algorithms in Krylov subspaces for solving the systems of linear algebraic equations (SLAE) with sparse nonsymmetric matrices. There are proposed and studied a biconjugate residual method, squared biconjugate residual method, and stabilized conjugate residual method. Some results of numerical experiments are given for a series of model problems as well.     

Orthogonal arrays obtained by repeating-column difference matrices

In this paper, by using the repeating-column difference matrices and orthogonal decompositions of projection matrices, we propose a new general approach to construct asymmetrical orthogonal arrays. As an application of the method, some new orthogonal arrays with run sizes 72 and 96 are constructed.     

一类正交投影矩阵及其相关正交表

本文给出了一类正交投影矩阵及其相关的强度2正交表.使用这些正交投影矩阵和正交表,我们提供了一种构造正交表的方法,并且构造了一些混合水平正交表.     

A projection method to solve linear systems in tensor format

In this paper, we propose a method for the numerical solution of linear systems of equations in low rank tensor format. Such systems may arise from the discretisation of PDEs in high dimensions, but our method is not limited to this type of application. We present an iterative scheme, which is based on the projection of the residual to a low dimensional subspace. The subspace is spanned by vectors in low rank tensor format which—similarly to Krylov subspace methods—stem from the subsequent (approximate) application of the given matrix to the residual. All calculations are performed in hierarchical Tucker format, which allows for applications in high dimensions. The mode size dependency is treated by a multilevel method. We present numerical examples that include high‐dimensional convection–diffusion equations.Copyright © 2012 John Wiley & Sons, Ltd.     

A projection method and Kronecker product preconditioner for solving Sylvester tensor equations

The preconditioned iterative solvers for solving Sylvester tensor equations are considered in this paper.By fully exploiting the structure of the tensor equation,we propose a projection method based on the tensor format,which needs less flops and storage than the standard projection method.The structure of the coefficient matrices of the tensor equation is used to design the nearest Kronecker product(NKP) preconditioner,which is easy to construct and is able to accelerate the convergence of the iterative solver.Numerical experiments are presented to show good performance of the approaches.     

On the convergence of row-modification algorithm for matrix projections

This paper proposes an algorithm for matrix minimum-distance projection, with respect to a metric induced from an inner product that is the sum of inner products of column vectors, onto the collection of all matrices with their rows restricted in closed convex sets. This algorithm produces a sequence of matrices by modifying a matrix row by row, over and over again. It is shown that the sequence is convergent, and it converges to the desired projection. The implementation of the algorithm for multivariate isotonic regressions and numerical examples are also presented in the paper.     

用Levenberg-Marquardt类的投影收缩方法解运输问题

For solving linear variational inequalities (LVI), the projection and contraction method of Levenberg-Marquardt type needs less iterations than an elementary projection and contraction method. However, the method of Levenberg-Marquardt type has to calculate the inverse of a matrix and hence it is unsuitable for large problems. In this paper, using the special structure of the constraint matrix, we present a PC method of Levenberg-Marquardt type for LVI arising from transportation problem without calculating any inverse matrices.Several computational experiments are presentded to indicate that the methods is good for solving the transportation problem.