Automated multi‐level sub‐structuring for fluid–solid interaction problems

The automated multi‐level sub‐structuring (AMLS) method is a powerful technique to determine a large number of eigenpairs with moderate accuracy of huge symmetric and definite eigenvalue problems in structural analysis. This paper is concerned with an adapted version of AMLS for eigenfrequency analysis of fluid–solid interaction systems. Although fluid–solid vibrations are governed by an unsymmetric eigenproblem, the modified AMLS method needs approximately the same computational effort. An error bound related to the eigenvalue approximations is proved. Copyright © 2010 John Wiley & Sons, Ltd.     

Two-sided harmonic subspace extractions for the generalized eigenvalue problem

One crucial step of the solution of large-scale generalized eigenvalue problems with iterative subspace methods, e.g. Arnoldi, Jacobi-Davidson, is a projection of the original large-scale problem onto a low dimensional subspaces. Here we investigate two-sided methods, where approximate eigenvalues together with their right and left eigenvectors of the full-size problem are extracted from the resulting small eigenproblem. The two-sided Ritz-Galerkin projection can be seen as the most basic form of this approach. It usually provides a good convergence towards the extremal eigenvalues of the spectrum. For improving the convergence towards interior eigenvalues, we investigate two approaches based on harmonic subspace extractions for the generalized eigenvalue problem. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

粘性阻尼线性振动系统的复模态特征值问题的一种新的矩阵摄动分析解法

本文提出了对粘性阻尼线性振动系统的复模态二次广义特征值问题进行高效近似求解的一种新的矩阵摄动分析方法,即先将阻尼矩阵分解为比例阻尼部分和非比例阻尼部分之和,并求得系统的比例阻尼实模态特征解;然后以此为初始值,将阻尼矩阵的非比例部分作为对其比例部分的小量修改,利用摄动分析方法简捷地得到系统的复模态特征值问题的近似解.这一新方法适用于振系阻尼分布不十分偏离比例阻尼情况的问题,因此对大阻尼(非过阻尼)振动系统也有效.这是它优于以前提出的基于无阻尼实模态特征解的类似摄动分析方法的重要特点.文中建立了复模态特征值和特征向量的二阶摄动解式,并通过算例证实了其有效性.此外还讨论了利用比例阻尼假定估计阻尼系统固有振动的复特征值的可行性.     

On the eigenproblem for convolution integral equations

Summary This paper concerns the eigenproblem for convolution integral equations whose kernels can be expressed as finite or infinite Fourier transforms of integrable functions. A procedure which closely parallels previous work on displacement integral equations is derived and the problem of existence is treated. Approximations are obtained for both the eigenvalues and the eigenfunctions.The results of this paper are taken from the author's doctoral dissertation at the University of New Mexico. The research was supported by the United States Atomic Energy Commission.     

An Arnoldi Method for Nonlinear Eigenvalue Problems

For the nonlinear eigenvalue problem T()x=0 we propose an iterative projection method for computing a few eigenvalues close to a given parameter. The current search space is expanded by a generalization of the shift-and-invert Arnoldi method. The resulting projected eigenproblems of small dimension are solved by inverse iteration. The method is applied to a rational eigenvalue problem governing damped vibrations of a structure.     

Low and high frequency approximations to eigenvibrations of string with double contrasts

We study eigenvibrations for inhomogeneous string consisting of two parts with strongly contrasting stiffness and mass density. In this work we treat a critical case for the high frequency approximations, namely the case when the order of mass density inhomogeneity is the same as the order of stiffness inhomogeneity, with heavier part being softer. The limit problem for high frequency approximations depends nonlinearly on the spectral parameter. The quantization of the spectral semiaxis is applied in order to get a close approximations of eigenvalues as well as eigenfunctions for the prime problem under perturbation.     

Predicting stochastic characteristics of generalized eigenvalues via a novel sensitivity-based probability density evolution method

This paper proposes a novel numerical method for predicting the probability density function of generalized eigenvalues in the mechanical vibration system with consideration of uncertainties in structural parameters. The eigenproblem of structural vibration is presented by first and the sensitivity of generalized eigenvalues with respect to structural parameters can be derived. The probability density evolution method is then developed to capture the probability density function of generalized eigenvalues considering uncertain material properties. Within the proposed method, the probability density evolution equation for the generalized eigenvalue problem is established accounting for the sensitivity of generalized eigenvalues with respect to structural parameters. A new variable which connects generalized eigenvalues to structural parameters is then introduced to simplify the original probability density evolution equation. Next, the simplified probability density evolution equation is solved by using the finite difference method with total variation diminishing schemes. Finally, the probability density function as well as the second-order statistical quantities of generalized eigenvalues can be predicted. Numerical examples demonstrate that the proposed method yields results consistent with Monte-Carlo simulation method within significantly less computation time and the coefficients of variation of uncertain parameters as well as the total number of them have remarkable effects on stochastic characteristics of generalized eigenvalues.     

On certain two-sided analogues of Newton’s method for solving nonlinear eigenvalue problems

Iterative algorithms for finding two-sided approximations to the eigenvalues of nonlinear algebraic eigenvalue problems are examined. These algorithms use an efficient numerical procedure for calculating the first and second derivatives of the determinant of the problem. Computational aspects of this procedure as applied to finding all the eigenvalues from a given complex-plane domain in a nonlinear eigenvalue problem are analyzed. The efficiency of the algorithms is demonstrated using some model problems.     

多重广义特征值的局部性态

本文给出了含参向量的矩阵多重广义特征值的方向导数，推广了文〔１〕的结果，所得结论对于结构优化和控制系统设计有一定意义。     

Die Konvergenz des Rand- und Eigenwertproblems linearer gewöhnlicher Differenzengleichungen

Summary In this paper the convergence of finite difference approximations for the general eigenvalue and boundary value problem of ordinary differential equations is proved under the condition of consistency and stability. The eigenvalues are shown to converge preserving multiplicity. Estimates are given for the rate of convergence of difference quotients and eigenvalues.     

An algorithm for numerical determination of the structure of a general matrix

An algorithm, proposed by V. N. Kublanovskaya, for solving the complete eigenvalue problem of a degenerate (that is defective and/or derogatory) matrix, is studied theoretically and numerically. It uses successiveQR-factorizations to determine annihilated subspaces.An adaptation of the algorithm is developed which, applied to a matrix with a very ill-conditioned eigenproblem, computes a degenerate matrix. The difference between these matrices is small, measured in the spectral norm. The degenerate matrix will appear in a standard form, whose eigenvalues and principal vectors can be computed in a numerically stable manner.Numerical examples are given.     

A divide and conquer method for unitary and orthogonal eigenproblems

Summary LetH ^{n xn} be a unitary upper Hessenberg matrix whose eigenvalues, and possibly also eigenvectors, are to be determined. We describe how this eigenproblem can be solved by a divide and conquer method, in which the matrixH is split into two smaller unitary upper Hessenberg matricesH ₁ andH ₂ by a rank-one modification ofH. The eigenproblems forH ₁ andH ₂ can be solved independently, and the solutions of these smaller eigenproblems define a rational function, whose zeros on the unit circle are the eigenvalues ofH. The eigenvector ofH can be determined from the eigenvalues ofH and the eigenvectors ofH ₁ andH ₂. The outlined splitting of unitary upper Hessenberg matrices into smaller such matrices is carried out recursively. This gives rise to a divide and conquer method that is suitable for implementation on a parallel computer.WhenH ^{n xn} is orthogonal, the divide and conquer scheme simplifies and is described separately. Our interest in the orthogonal eigenproblem stems from applications in signal processing. Numerical examples for the orthogonal eigenproblem conclude the paper.Research supported in part by the NSF under Grant DMS-8704196 and by funds administered by the Naval Postgraduate School Research Council     

有限长周期结构的密集特征值

基于单胞结构的特征值问题,给出了有限长周期结构特征值分布范围的估计,基于固体物理中的能带理论,给出了一维有限长周期结构特征值分布范围的更精细估计．通过分析有限长周期结构特征值的分布范围,阐述了密集特征值出现的原因．分析结果表明,对于有限长周期结构,结构的单胞数目越大,其特征值分布会越密集．数值算例验证了该文的结论．     

Eigenvalue problem for a regular linear pencil of matrices close to singular ones

The solution is examined of the eigenvalue problem (1) for a regular linear pencil of matrices A and B of which at least one is close to being singular. Two groups of algorithms are proposed for solving (1). Both groups of algorithms work in the situation when the eigenvalues of the original pencil can be separated into groups of eigenvalues large and small in absolute value. The algorithms reveal this situation. The algorithms of the first group permit the passage from the original pencil to a pencil strictly equivalent to it, which in form is close to a quasitriangular pencil (or coincides with a quasitriangular one in case at least one of the pencil's matrices is singular). The eigenvalues of the diagonal blocks of the pencil constructed yield approximations to the eigenvalues of problem (1). If the approximations obtained are refined by the Newton method, using the normalized decomposition of auxiliary constructed matrices, then both the eigenvalues of (1) as well as all the linearly independent eigenvectors corresponding to them can be found. The algorithms of the second group permit the passage from the original pencil to a strictly equivalent pencil representable as a sum of two singular pencils whose null spaces are mutually perpendicular; next, with the aid of an iteration process based on the use of perturbation theory, these algorithms permit the finding of the eigenvalues of pencil (1), small (large) in absolute value, and the eigenvectors corresponding to them. Ill-conditioned regular pencils close to singular ones also are examined. For them an algorithm is suggested which permits the ill conditioning to be revealed and permits approximations to the stable (to perturbations in the original data) eigenvalues of the pencil to be obtained.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 90, pp. 63–82, 1979.     

Nonlinear inversion of a band-limited Fourier transform

We consider the problem of reconstructing a compactly supported function with singularities either from values of its Fourier transform available only in a bounded interval or from a limited number of its Fourier coefficients. Our results are based on several observations and algorithms in [G. Beylkin, L. Monzón, On approximation of functions by exponential sums, Appl. Comput. Harmon. Anal. 19 (1) (2005) 17–48]. We avoid both the Gibbs phenomenon and the use of windows or filtering by constructing approximations to the available Fourier data via a short sum of decaying exponentials. Using these exponentials, we extrapolate the Fourier data to the whole real line and, on taking the inverse Fourier transform, obtain an efficient rational representation in the spatial domain. An important feature of this rational representation is that the positions of its poles indicate location of singularities of the function. We consider these representations in the absence of noise and discuss the impact of adding white noise to the Fourier data. We also compare our results with those obtained by other techniques. As an example of application, we consider our approach in the context of the kernel polynomial method for estimating density of states (eigenvalues) of Hermitian operators. We briefly consider the related problem of approximation by rational functions and provide numerical examples using our approach.     

A Multilevel Correction Method for Steklov Eigenvalue Problem by Nonconforming Finite Element Methods

In this paper, a multilevel correction scheme is proposed to solve the Steklov eigenvalue problem by nonconforming finite element methods. With this new scheme, the accuracy of eigenpair approximations can be improved after each correction step which only needs to solve a source problem on finer finite element space and an Steklov eigenvalue problem on the coarsest finite element space. This correction scheme can increase the overall efficiency of solving eigenvalue problems by the nonconforming finite element method. Furthermore, as same as the direct eigenvalue solving by nonconforming finite element methods, this multilevel correction method can also produce the lower-bound approximations of the eigenvalues.     

Jacobi-davidson type methods for generalized eigenproblems and polynomial eigenproblems

In this paper we will show how the Jacobi-Davidson iterative method can be used to solve generalized eigenproblems. Similar ideas as for the standard eigenproblem are used, but the projections, that are required to reduce the given problem to a small manageable size, need more attention. We show that by proper choices for the projection operators quadratic convergence can be achieved. The advantage of our approach is that none of the involved operators needs to be inverted. It turns out that similar projections can be used for the iterative approximation of selected eigenvalues and eigenvectors of polynomial eigenvalue equations. This approach has already been used with great success for the solution of quadratic eigenproblems associated with acoustic problems.Our friend Albert died on November 12, 1995     

A posteriori error control for finite element approximations of elliptic eigenvalue problems

We develop a new approach to a posteriori error estimation for Galerkin finite element approximations of symmetric and nonsymmetric elliptic eigenvalue problems. The idea is to embed the eigenvalue approximation into the general framework of Galerkin methods for nonlinear variational equations. In this context residual-based a posteriori error representations are available with explicitly given remainder terms. The careful evaluation of these error representations for the concrete situation of an eigenvalue problem results in a posteriori error estimates for the approximations of eigenvalues as well as eigenfunctions. These suggest local error indicators that are used in the mesh refinement process.     

A numerical solution of the constrained weighted energy problem

A numerical algorithm is presented to solve the constrained weighted energy problem from potential theory. As one of the possible applications of this algorithm, we study the convergence properties of the rational Lanczos iteration method for the symmetric eigenvalue problem. The constrained weighted energy problem characterizes the region containing those eigenvalues that are well approximated by the Ritz values. The region depends on the distribution of the eigenvalues, on the distribution of the poles, and on the ratio between the size of the matrix and the number of iterations. Our algorithm gives the possibility of finding the boundary of this region in an effective way.We give numerical examples for different distributions of poles and eigenvalues and compare the results of our algorithm with the convergence behavior of the explicitly performed rational Lanczos algorithm.     

Projected equation methods for approximate solution of large linear systems

We consider linear systems of equations and solution approximations derived by projection on a low-dimensional subspace. We propose stochastic iterative algorithms, based on simulation, which converge to the approximate solution and are suitable for very large-dimensional problems. The algorithms are extensions of recent approximate dynamic programming methods, known as temporal difference methods, which solve a projected form of Bellman’s equation by using simulation-based approximations to this equation, or by using a projected value iteration method.