Backward error analysis for linearizations in heavily damped quadratic eigenvalue problem

A multiscale analysis and computational method based on the Second-Order Two-Scale (SOTS) approach are proposed for the elastic quadratic eigenvalue problems in the periodic composite domain. Two typical quadratic eigenvalue problems with different damping effects are considered, and by the asymptotic expansions of both the eigenfunctions and eigenvalues, the first- and second-order cell functions, the microscale features of this heterogeneous materials are defined successively. Then, the homogenized quadratic eigenvalue problems are derived and the second-order expansions of the eigenfunctions are formed. The eigenvalues are also broadened to the second-order terms by introducing proper auxiliary elastic functions defined in the composite structure, and the nonlinear expressions of the correctors of the eigenvalues are derived. The finite element procedures are established, linearized methods are discussed for solving the quadratic eigenvalue problems and the second-order asymptotic computations are performed. Effectiveness of the asymptotic model is demonstrated by both the qualitative and quantitative comparisons between the computed SOTS approximations and the reference solutions, and the converging behavior of the eigenfunctions are numerically verified. It is also indicated that the second-order correctors are of importance to reconstruct the detailed information of the original eigenfunctions within the micro cells.

Numerical solution of a quadratic eigenvalue problem*1

Model updating for quadratic eigenvalue problems (QEPs) is pro- posed by Friswell, Inman and Pilkey (1998), to incorporate the measured model data into the model which only produces the mass and stifiness matrices, that closely match the experimental model data. In this paper, we consider a numer- ical model for updating QEPs which produces not only the mass and stifiness matrices, but also the damping matrix will be updated. To this end the com- plete set of eigenpairs will be employed.

To solve quadratic eigenvalue problems (QEPs), especially the gyroscopic systems, two methods are proposed: an iterative direct detection method (DDM) of the complex eigenvalues of the original QEP, and a split-linearization method (SLM) for finding the solvent matrix, which results to a standard linear eigenvalue problem (LEP) being solved to compute all eigenvalues by the symmetry extension. Reducing the dimension to one-half, the LEP is recast in a simpler QEP involving the square of the solvent. We set up two new merit functions which are minimized to detect the complex eigenvalues from the original QEP and a simpler QEP. For each eigen-parameter the merit function consists of the Euclidean norm of each derived eigen-equation, whose vector variable is solved from a derived inhomogeneous linear system. Then, the golden section search algorithm is employed to minimize the merit functions and locate the complex eigenvalue as a local minimal point. The results are compared with that computed by the cyclic-reduction-based solvent (CRS) method.

We propose a novel formulation of minimal case solutions for determining the relative pose of perspective and generalized cameras given a partially known rotation, namely, a known axis of rotation. An axis of rotation may be easily obtained by detecting vertical vanishing points with computer vision techniques, or with the aid of sensor measurements from a smart phone. Given a known axis of rotation, our algorithms solve for the angle of rotation around the known axis along with the unknown translation. We formulate these relative pose problems as Quadratic Eigen value Problems which are very simple to construct. We run several experiments on synthetic and real data to compare our methods to the current state-of-the-art algorithms. Our methods provide several advantages over alternatives methods, including efficiency and accuracy, particularly in the presence of image and sensor noise as is often the case for mobile devices.

In several applications in science and engineering, different types of matrix problems emerge from the discretization of partial differential equations. This thesis is devoted to the development of new algorithms to solve this kind of problems. In particular, when the matrices involved are sparse and non-symmetric. The new algorithms are based on the Induced Dimension Reduction method [IDR(s)]. IDR(s) is a Krylov subspace method originally proposed in 2008 to solve systems of linear equations. IDR(s) has received considerable attention due to its stable and fast convergence. It is, therefore, natural to ask if it is possible to extend IDR(s) to solve other matrix problems, and if so, to compare those extensions with other well-established methods. This work aims to answer these questions. The main matrix problems considered in this dissertation are: the standard eigenvalue problem, the quadratic eigenvalue problem, the solution of systems of linear equations, the solution of sequences of systems of linear equations, and linear matrix equations. We focus on examples that arise from the discretization of partial differential equations.

We consider quadratic eigenvalue problems with large and sparse matrices depending on a parameter. Problems of this type occur, for example, in the stability analysis of spatially discretized and p...

In this paper, we investigate the relation between eigenvalues of quadratic characteristic matrix and its companion matrix, and some bounds for the quadratic eigenvalues are firstly obtained. Secondly, we transforms quadratic eigenvalue problem (QEP) into an optimization problem, and figure out its eigenvalues was adopted based on a unified particle swarm optimization (UPSO) with dynamic updating. Numerical examples indicated UPSO method has a faster convergence rate, strong adaptability, higher accuracy and so on, which is a better algorithm for solving QEP.

Chapter 6 - Quadratic forms and Eigenvalue problems

Structured eigenvalue methods for the computation of corner singularities in 3D anisotropic elastic structures

The hyperbolic quadratic eigenvalue problem (HQEP) was shown to admit Courant–Fischer type min–max principles in 1955 by Duffin and Cauchy type interlacing inequalities in 2010 by Veselić. It can be regarded as the closest analog (among all kinds of quadratic eigenvalue problem) to the standard Hermitian eigenvalue problem (among all kinds of standard eigenvalue problem). In this paper, we conduct a systematic study on the HQEP both theoretically and numerically. On the theoretical front, we generalize Wielandt–Lidskii type min–max principles and, as a special case, Fan type trace min/max principles and establish Weyl type and Wielandt–Lidskii–Mirsky type perturbation results when an HQEP is perturbed to another HQEP. On the numerical front, we justify the natural generalization of the Rayleigh–Ritz procedure with existing principles and our new optimization principles, and, as consequences of these principles, we extend various current optimization approaches—steepest descent/ascent and nonlinear conjugate gradient type methods for the Hermitian eigenvalue problem—to calculate a few extreme eigenvalues (of both positive and negative type). A detailed convergence analysis is given for the steepest descent/ascent methods. The analysis reveals the intrinsic quantities that control convergence rates and consequently yields ways of constructing effective preconditioners. Numerical examples are presented to demonstrate the proposed theory and algorithms.

In the past decade or so, semi-definite programming (SDP) has emerged as a powerful tool capable of handling a remarkably wide range of problems. This article describes an innovative application of SDP techniques to quadratic inverse eigenvalue problems (QIEPs). The notion of QIEPs is of fundamental importance because its ultimate goal of constructing or updating a vibration system from some observed or desirable dynamical behaviors while respecting some inherent feasibility constraints well suits many engineering applications. Thus far, however, QIEPs have remained challenging both theoretically and computationally due to the great variations of structural constraints that must be addressed. Of notable interest and significance are the uniformity and the simplicity in the SDP formulation that solves effectively many otherwise very difficult QIEPs.

本文中我们对Helmholtz方程透射特征值问题提出一种带位移求逆的算法，此算法可以快速有效地求出任意给定的 σ 附近的几个实特征值及对应的特征向量。首先，我们用连续有限元方法对Helmholtz方程透射特征值问题进行离散，并将离散后的广义特征值问题化为一个二次特征值问题，进而对其进行线性化得到一个新的广义特征值问题。这个新的广义特征值问题排除了没有物理意义的零特征值的干扰，保留了所有的非零特征值。我们还利用位移求逆的技术，求得给定的 σ 附近的几个实特征对。我们所提出的算法对透射特征值问题的折射率没有特别的限制，即折射率可为正或负亦或是任意的实函数。最后的数值算例验证了该算法的有效性。 In this paper, we put forward a shift-and-invert algorithm to solve the transmission eigenvalue problem of Helmholtz equation, which can quickly and efficiently find out the several eigenvalues and the corresponding eigenvectors near arbitrarily given σ. First, we use the continuous finite element method to discrete the transmission eigenvalue problem of Helmholtz equation, and dis-crete the generalized eigenvalue problem into a quadratic eigenvalue problem, and then a new generalized eigenvalue problem is obtained by linearization. The new generalized eigenvalue problem eliminates the distraction of nonphysical zero eigenvalues, and preserves all the nonzero eigenvalues. Further through the use of shift-and-invert technology, we can quickly and efficiently get several real eigenpairs near given σ. The proposed algorithm has no special restrictions to the refractive index of the transmission eigenvalue problems, that is to say, the refractive index can be positive or negative or positive and negative real numbers. The final numerical example verifies the effectiveness of our algorithm.

A computational approach for solving regularized total least squares problems via a sequence of quadratic eigenvalue problems has recently been introduced by Sima, Van Huffel, and Golub. Combining this approach with the nonlinear Arnoldi method and reusing information from all previous quadratic eigenvalue problems, together with an early update of search spaces we arrive at a very efficient method for large regularized total least squares problems. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

This paper presents a new finite element formulation for solving arbitrarily shaped waveguides including lossy inhomogeneous anisotropic media. The materials are characterized by simultaneous [/spl epsi/] and [/spl mu/] full tensors. Complex-mode computation, spurious-mode suppression and the possibility of specifying the frequency as an input parameter are also achieved. The formulation leads to a quadratic eigenvalue problem of dimension N which is transformed into an efficient 2N-dimensional generalized eigensystem with sparse complex matrices. This eigensystem is solved by the subspace method, taking full advantage of the sparsity of the matrices. Permittivity and permeability tensors with some null terms allow an additional reduction from the N-dimensional quadratic eigenvalue problem to a N-dimensional sparse complex generalized eigensystem. The proposed method has been validated by analyzing different lossy, inhomogeneous and anisotropic waveguides. Results show good agreement with previously published data. >