Sensitivity analysis of the largest dependent eigenvalue functions of eigensystems

This paper presents and discusses the first order sensitivity of the largest eigenvalue functions of eigensystems. The analysis is based on investigating the entries of the matrices associated with the finite element formulations. These entries are continuously differentiable functions, but the dependent eigenvalue functions may be continuous nondifferentiable. The main emphasis of this contribution is the derivation and the investigation of this analysis under the assumption that the generalized dependent eigenvalue functions are continuous nondifferentiable.     

勒让德方程本征值的确定

大部分数学物理方法教材直接给出了勒让德方程本征值的表达式,比较突兀,学生也难以理解.为消除学生在此处的学习障碍,直接从勒让德方程的一般形式入手,通过常点邻域的级数解法,推导出勒让德方程本征值的表示形式.     

一类具有转移条件的向量Sturm-Liouville问题的特征值

考虑了一类具有转移条件的向量Sturm-Liouville问题的特征值及其重数问题.首先构造了与问题相关的新内积和基本解,得到特征值的充要条件.在此基础上证明了二维情况下,问题特征值的代数重数与几何重数相等.     

A new type of full multigrid method for the elasticity eigenvalue problem

This paper introduces a new type of full multigrid method for the elasticity eigenvalue problem. The main idea is to avoid solving large scale elasticity eigenvalue problem directly by transforming the solution of the elasticity eigenvalue problem into a series of solutions of linear boundary value problems defined on a multilevel finite element space sequence and some small scale elasticity eigenvalue problems defined on the coarsest correction space. The involved linear boundary value problems will be solved by performing some multigrid iterations. Besides, some efficient techniques such as parallel computing and adaptive mesh refinement can also be absorbed in our algorithm. The efficiency and validity of the multigrid methods are verified by several numerical experiments.     

基于信息压缩矩阵特征值映射的时变系统UD分解辨识算法

在系统辨识领域遗忘因子UD分解算法(一种通过对系统数据矩阵进行UD分解的在线辨识算法)具有对时变系统阶次和参数同步估计的优异性能,但传统的遗忘策略不能从根本上解决信息压缩矩阵数据过饱和问题,为了拓展现有UD分解算法在时变系统的适用范围,同时针对数据空间分布不均匀性,提出一种基于信息压缩矩阵特征值映射的UD分解辨识算法....     

三维区域上Q_1~(rot)元的渐近展开及外推

本文研究了正方体区域上Q₁^rot非协调元渐近展开式.利用林群、吕涛等提出的有限元误差渐近展开法,获得了正方体区域上Q₁^rot非协调元特征值的误差渐近展开式.理论分析和数值实验结果表明三维Q₁^rot非协调元特征值外推公式是有效的,可以把特征值的精度从二阶提高到四阶.     

非正曲率流形及其子流形上有界区域的特征值

本文研究了完备单连通具有非正曲率黎曼流形及其子流形上有界区域的特征值问题.利用广义Hessian比较定理,获得了局部特征值的下界估计式,将McKean[2]的定理在局部上推广到了非正曲率的情形.     

扭化Dirac算子的特征值估计和常值长度的调和1-形式(英文)

对带有非平凡常值长度调和1-形式的紧致旋流形,本文证明了扭化Dirac算子的第一个正特征值的下界.     

Bounding the spectrum of large Hermitian matrices

Estimating upper bounds of the spectrum of large Hermitian matrices has long been a problem with both theoretical and practical significance. Algorithms that can compute tight upper bounds with minimum computational cost will have applications in a variety of areas. We present a practical algorithm that exploits k-step Lanczos iteration with a safeguard step. The k is generally very small, say 5-8, regardless of the large dimension of the matrices. This makes the Lanczos iteration economical. The safeguard step can be realized with marginal cost by utilizing the theoretical bounds developed in this paper. The bounds establish the theoretical validity of a previous bound estimator that has been successfully used in various applications. Moreover, we improve the bound estimator which can now provide tighter upper bounds with negligible additional cost.