Projection and deflation method for partial pole assignment inlinear state feedback

Two projection methods are proposed for partial pole placement in linear control systems. These methods are of interest when the system is very large and only a few of its poles must be assigned. The first method is based on computing an orthonormal basis of the left invariant subspace associated with the eigenvalues to be assigned and then solving a small inverse eigenvalue problem resulting from projecting the initial problem into that subspace. The second method can be regarded as a variant of the Weilandt deflation technique used in eigenvalue methods     

Fast structural optimization with frequency constraints by genetic algorithm using adaptive eigenvalue reanalysis methods

Structural optimization with frequency constraints is highly nonlinear dynamic optimization problems. Genetic algorithm (GA) has greater advantage in global optimization for nonlinear problem than optimality criteria and mathematical programming methods, but it needs more computational time and numerous eigenvalue reanalysis. To speed up the design process, an adaptive eigenvalue reanalysis method for GA-based structural optimization is presented. This reanalysis technique is derived primarily on the Kirsch’s combined approximations method, which is also highly accurate for case of repeated eigenvalues problem. The required number of basis vectors at every generation is adaptively determined and the rules for selecting initial number of basis vectors are given. Numerical examples of truss design are presented to validate the reanalysis-based frequency optimization. The results demonstrate that the adaptive eigenvalue reanalysis affects very slightly the accuracy of the optimal solutions and significantly reduces the computational time involved in the design process of large-scale structures.     

Semiclassical spectral confinement for the sine-Gordon equation

The inverse scattering method for solving the sine-Gordon equation in laboratory coordinates requires the analysis of the Faddeev–Takhtajan eigenvalue problem. This problem is not self-adjoint and the eigenvalues may lie anywhere in the complex plane, so it is of interest to determine conditions on the initial data that restrict where the eigenvalues can be. We establish bounds on the eigenvalues for a broad class of zero-charge initial data that are applicable in the semiclassical or zero-dispersion limit. It is shown that no point off the coordinate axes or turning point curve can be an eigenvalue if the dispersion parameter is sufficiently small.     

Computing Extremal Eigenvalues for Three-Dimensional Photonic Crystals with Wave Vectors Near the Brillouin Zone Center

The band structures of three-dimensional photonic crystals can be determined numerically by solving a sequence of generalized eigenvalue problems. However, not all of the spectral structures of these eigenvalue problems are well-understood, and not all of these eigenvalue problems can be solved efficiently. This article focuses on the eigenvalue problems corresponding to wave vectors that are close to the center of the Brillouin zone of a three dimensional, simple cubic photonic crystal. For these eigenvalue problems, there are (i) many zero eigenvalues, (ii) a couple of near-zero eigenvalues, and (iii) several larger eigenvalues. As the desired eigenvalues are the smallest positive eigenvalues, these particular spectral structures prevent regular eigenvalue solvers from efficiently computing the desired eigenvalues. We study these eigenvalue problems from the perspective of both theory and computation. On the theoretical side, the structures of the null spaces are analyzed to explicitly determine the number of zero eigenvalues of the target eigenvalue problems. On the computational side, the Krylov-Schur and Jacobi-Davidson methods are used to compute the smallest, positive, interior eigenvalues that are of interest. Intensive numerical experiments disclose how the shift values, conditioning numbers, and initial vectors affect the performance of the tested eigenvalue solvers and suggest the most efficient eigenvalue solvers.     

A Numerical Method to Verify the Elliptic Eigenvalue Problems Including a Uniqueness Property

We propose a numerical method to enclose the eigenvalues and eigenfunctions of second-order elliptic operators with local uniqueness. We numerically construct a set containing eigenpairs which satisfies the hypothesis of Banach's fixed point theorem in a certain Sobolev space by using a finite element approximation and constructive error estimates. We then prove the local uniqueness separately of eigenvalues and eigenfunctions. This local uniqueness assures the simplicity of the eigenvalue. Numerical examples are presented. Received: November 2, 1998; revised June 5, 1999     

自洽场方法中广义本征值方程求解及其C++程序设计

本文研究自洽场方法中广义本征值方程求解的算法,并设计相应的C 程序来实现该算法。首先对重叠矩阵进行分解,并将广义本征值方程化为标准的本征值方程,再利用Householder变换将上一步变换所得的矩阵化为对称三对角矩阵,进而用QL方法求解这个三对角矩阵的本征值和本征矢量,从而得到自洽场方法中广义本征值方程的本征值和本征矢量。     

Numerical solution of singular ODE eigenvalue problems in electronic structure computations

We put forward a new method for the solution of eigenvalue problems for (systems of) ordinary differential equations, where our main focus is on eigenvalue problems for singular Schrödinger equations arising for example in electronic structure computations. In most established standard methods, the generation of the starting values for the computation of eigenvalues of higher index is a critical issue. Our approach comprises two stages: First we generate rough approximations by a matrix method, which yields several eigenvalues and associated eigenfunctions simultaneously, albeit with moderate accuracy. In a second stage, these approximations are used as starting values for a collocation method which yields approximations of high accuracy efficiently due to an adaptive mesh selection strategy, and additionally provides reliable error estimates. We successfully apply our method to the solution of the quantum mechanical Kepler, Yukawa and the coupled ODE Stark problems.     

Preconditioning the Matrix Exponential Operator with Applications

The idea of preconditioning is usually associated with solution techniques for solving linear systems or eigenvalue problems. It refers to a general method by which the original system is transformed into one which admits the same solution but which is easier to solve. Following this principle we consider in this paper techniques for preconditioning the matrix exponential operator, e ^A y ₀, using different approximations of the matrix A. These techniques are based on using generalized Runge Kutta type methods. Preconditioners based on the sparsity structure of the matrix, such as diagonal, block diagonal, and least-squares tensor sum approximations are presented. Numerical experiments are reported to compare the quality of the schemes introduced.     

The Streamline–Diffusion Method for Conforming and Nonconforming Finite Elements of Lowest Order Applied to Convection–Diffusion Problems

We consider the streamline-diffusion finite element method with finite elements of lowest order for solving convection-diffusion problems. Our investigations cover both conforming and nonconforming finite element approximations on triangular and quadrilateral meshes. Although the considered finite elements are of the same interpolation order their stability and approximation properties are quite different. We give a detailed overview on the stability and the convergence properties in the L ²- and in the streamline–diffusion norm. Numerical experiments show that often the theoretical predictions on the convergence properties are sharp. Received December 7, 1999; revised October 5, 2000     

Descent Iterations for Improving Approximate Eigenpairs of Polynomial Eigenvalue Problems with General Complex Matrices

In this paper, we consider descent iterations with line search for improving an approximate eigenvalue and a corresponding approximate eigenvector of polynomial eigenvalue problems with general complex matrices, where an approximate eigenpair was obtained by some method. The polynomial eigenvalue problem is written as a system of complex nonlinear equations with nondifferentiable normalized condition. Convergence theorems for iterations are established. Finally, some numerical examples are presented to demonstrate the effectiveness of the iterative methods. Received April 9, 2001; revised October 2, 2001 Published online February 18, 2002     

A numerical projection technique for large-scale eigenvalue problems

We present a new numerical technique to solve large-scale eigenvalue problems. It is based on the projection technique, used in strongly correlated quantum many-body systems, where first an effective approximate model of smaller complexity is constructed by projecting out high energy degrees of freedom and in turn solving the resulting model by some standard eigenvalue solver.Here we introduce a generalization of this idea, where both steps are performed numerically and which in contrast to the standard projection technique converges in principle to the exact eigenvalues. This approach is not just applicable to eigenvalue problems encountered in many-body systems but also in other areas of research that result in large-scale eigenvalue problems for matrices which have, roughly speaking, mostly a pronounced dominant diagonal part. We will present detailed studies of the approach guided by two many-body models.     

Multigrid method for stability problems

The problem of calculating the stability of steady state solutions of differential equations is treated. Leading eigenvalues (i.e., having maximal real part) of large matrices that arise from discretization are to be calculated. An efficient multigrid method for solving these problems is presented. The method begins by obtaining an initial approximation for the dominant subspace on a coarse level using a damped Jacobi relaxation. This proceeds until enough accuracy for the dominant subspace has been obtained. The resulting grid functions are then used as an initial approximation for appropriate eigenvalue problems. These problems are solved first on coarse levels, followed by refinement until a desired accuracy for the eigenvalues has been achieved. The method employs local relaxation on all levels together with a global change on the coarsest level only, which is designed to separate the different eigenfunctions as well as to update their corresponding eigenvalues. Coarsening is done using the FAS formulation in a nonstandard way in which the right-hand side of the coarse grid equations involves unknown parameters to be solved for on the coarse grid. This in particular leads to a new multigrid method for calculating the eigenvalues of symmetric problems. Numerical experiments with a model problem that are presented demonstrate the effectiveness of the method proposed. Using an FMG algorithm a solution to the level of discretization errors is obtained in just a few work units (less than 10), where a work unit is the work involved in one Jacobi relaxation on the finest level.     

A numerical solution of the membrane eigenvalue problem

A conformal transformation method is used for the numerical solution of the membrane eigenvalue problem. The method overcomes, in many cases, the difficulties associated with the computation of the eigenvalues of problems involving curved boundaries and/or boundary singularities and produces accurate numerical approximations. This is achieved by transforming the original problem into another computationally simpler one.     

Finite Element Methods for Eigenvalue Problems on a Rectangle with (Semi-) Periodic Boundary Conditions on a Pair of Adjacent Sides

This paper deals with a class of elliptic differential eigenvalue problems (EVPs) of second order on a rectangular domain Ω⊂ℝ², with periodic or semi-periodic boundary conditions (BCs) on two adjacent sides of Ω. On the remaining sides, classical Dirichlet or Robin type BCs are imposed. First, we pass to a proper variational formulation, which is shown to fit into the framework of abstract EVPs for strongly coercive, bounded and symmetric bilinear forms in Hilbert spaces. Next, the variational EVP serves as the starting point for finite element approximations. We consider finite element methods (FEMs) without and with numerical quadrature, both with triangular and with rectangular meshes. The aim of the paper is to show that well-known error estimates, established for finite element approximations of elliptic EVPs with classical BCs, remain valid for the present type of EVPs, including the case of multiple exact eigenvalues. Finally, the analysis is illustrated by a non-trivial numerical example, the exact eigenpairs of which can be determined. Received March 2, 1999; revised July 8, 1999     

Parameterized ABD preconditioning technique for time-periodic convection–diffusion problems

In this paper, a parameterized additive block diagonal (PABD) preconditioning technique is present for solving the nine-point approximations of the time-periodic convection–diffusion problems. The explicit expressions for the eigenvalues and eigenvectors of the corresponding preconditioned matrices are presented. The range of the optimal parameters is derived. Numerical experiments show that the generalized minimal residual method preconditioned by the PABD preconditioner with the experimental optimal parameters or some special values is effective for a wide range of problem sizes.     

一种基于互信息的特征跃迁示例学习法

给出一种能够接受特征及变化的示例学习方法。该方法是对ID3方法的一种改进,传统ID3方法是基于持征值的学习,训练示例是若干组静态特征值,其局限性在于不能理解和记忆特征的变化信息,尤其没考虑特征间的动态相关。改进后的方能学习动态特征,接受的训练示例是特征值在一定间隔内的初值和终值,从中获取特征值及其在指定间隔的跃迁,该方法能够学习数据动态趋势,尤其能够挖掘出特征间动态相关。通过若干例子测试,该方法适用于具有多元动态相关特征问题的分类。     

A projection method for the numerical solution of linear systems in separable stiff differential equations

This paper deals with the efficient implementation of implicit methods for solving stiff ODEs, in the case of Jacobians with separable sets of eigenvalues. For solving the linear systems required we propose a method which is particularly suitable when the large eigenvalues of the Jacobian matrix are few and well separated from the small ones. It is based on a combination of an initial iterative procedure, which reduces the components of the vector error along to the nondominant directions of J and a projection Krylov method which reduces the components of the vector error along to the directions corresponding to the large eigenvalues. The technique solves accurately and cheaply the linear systems in the Newton's method, and computes the number of stiff eigenvalues of J when this information is not explicitly available. Numerical results are given as well as comparisons with the LSODE code.     

On a quadratic eigenproblem occurring in regularized total least squares

A computational approach for solving regularized total least squares problems via a sequence of quadratic eigenvalue problems has recently been proposed. Taking advantage of a variational characterization of real eigenvalues of nonlinear eigenproblems the existence of a real right-most eigenvalue for each quadratic eigenvalue problem in the sequence is proven. For large problems the approach is improved considerably utilizing information from the previous quadratic problems and early updates in a nonlinear Arnoldi method.     

Prüfer method for periodic and semi-periodic sturm-liouville eigenvalue problems ∗

In reference [19], the authors developed a shooting algorithm for Sturm-Liouville eigenvalue problems associated with periodic and semi-periodic boundary conditions. The technique is based on the application of the Floquet theory, and it has proven to be efficient for computing eigenvalues. However, the performance of this technique depends upon the choice of the starting eigenvalues. In the present paper, we continue our study and employ the Prüfer method. An attractive property of this method is that eigenvalues can usually be accurately computed even when no information on the eigenvalue distribution is provided. Sufficient conditions for convergence, error bounds and a procedure to improve the stability are discussed. Some numerical examples are given to illustrate the effectiveness of the proposed method.     

Solution of generalized non-singular Sturm–Liouville systems via an approximation method in a Hilbert space

The approximation method in a Hilbert space is applied to the analysis of a generalized non-singular Sturm-Liouville eigenvalue problem. The variable coefficients in the system equation and the solution are approximated by a suitable linear independent basis, the expansion coefficients are explicitly expressed as a backward recursive formula and the eigenvalues are obtained by solving an algebraical characteristic equation. The computation is simple and straightforward.