Left and Right Inverse Eigenpairs Problem of Orthogonal Matrices

In this paper, the left and right inverse eigenpairs problem of orthogonal matrices and its optimal approximation solution are considered. Based on the special properties of eigenvalue and the special relations of left and right eigenpairs for orthogonal matrices, we find the equivalent problem, and derive the necessary and sufficient conditions for the solvability of the problem and its general solutions. With the properties of continuous function in bounded closed set, the optimal approximate solution is obtained. In addition, an algorithm to obtain the optimal approximation and numerical example are provided.

On Finding the Smallest Generalized Eigenpair Using Markov Chain Monte Carlo Algorithm

This paper proposes a new technique based on inverse Markov chain Monte Carlo algorithm for finding the smallest generalized eigenpair of the large scale matrices. Some numerical examples show that the proposed method is efficient.

Finding the Maximal Eigenpair for a Large, Dense, Symmetric Matrix based on Mufa Chen's Algorithm

A hybrid method is presented for determining maximal eigenvalue and its eigenvector(called eigenpair)of a large,dense,symmetric matrix.Many problems require finding only a small part of the eigenpairs,and some require only the maximal one.In a series of papers,efficient algorithms have been developed by Mufa Chen for computing the maximal eigenpairs of tridiagonal matrices with positive off-diagonal elements.The key idea is to explicitly construet effective initial guess of the maximal eigenpair and then to employ a self-closed iterative algorithm.In this paper we will extend Mufa Chen's algorithm to find maximal eigenpair for a large scale,dense,symmetric matrix.Our strategy is to first convert the underlying matrix into the tridiagonal form by using similarity transformations.We then handle the cases that prevent us from applying Chen's algorithm directly,e.g.,the cases with zero or negative super-or sub-diagonal elements.Serval numerical experiments are carried out to demonstrate the efficiency of the proposed hybrid method.

Second-order sensitivity of eigenpairs in multiple parameter structures

This paper presents methods for computing a second-order sensitivity matrix and the Hessian matrix of eigenvalues and eigenvectors of multiple parameter structures. Second-order perturbations of eigenvalues and eigenvectors are transformed into multiple parameter forms,and the second-order perturbation sensitivity matrices of eigenvalues and eigenvectors are developed.With these formulations,the efficient methods based on the second-order Taylor expansion and second-order perturbation are obtained to estimate changes of eigenvalues and eigenvectors when the design parameters are changed. The presented method avoids direct differential operation,and thus reduces difficulty for computing the second-order sensitivity matrices of eigenpairs.A numerical example is given to demonstrate application and accuracy of the proposed method.

Development of powerful algorithm for maximal eigenpair

Based on a series of recent papers, a powerful algorithm is reformulated for computing the maximal eigenpair of self-adjoint complex tridiagonal matrices. In parallel, the same problem in a particular case for computing the sub-maximal eigenpair is also introduced. The key ideas for each critical improvement are explained. To illustrate the present algorithm and compare it with the related algorithms, more than 10 examples are included.

Improved global algorithms for maximal eigenpair

This paper is a continuation of our previous paper[Front.Math.China,2017,12(5):10231043]where global algorithms for computing the maximal cigcnpair were introduced in a rather general setup.The efficiency of the global algorithms is improved in this paper in terms of a good use of power iteration and two quasi-symmetric techniques.Finally,the new algorithms are applied to Hua’s economic optimization model.

Computing top eigenpairs of Hermitizable matrix

The top eigenpairs at the title mean the maximal, the submaximal, or a few of the subsequent eigenpairs of an Hermitizable matrix. Restricting on top ones is to handle with the matrices having large scale, for which only little is known up to now. This is different from some mature algorithms, that are clearly limited only to medium-sized matrix for calculating full spectrum. It is hoped that a combination of this paper with the earlier works, to be seen soon, may provide some effective algorithms for computing the spectrum in practice, especially for matrix mechanics.

Efficient algorithm for principal eigenpair of discrete p-Laplacian

This paper is a continuation of the author＇s previous papers [Front. Math. China, 2016, 11（6）： 1379-1418; 2017, 12（5）： 1023-1043], where the linear case was studied. A shifted inverse iteration algorithm is introduced, as an acceleration of the inverse iteration which is often used in the non-linear context （the p-Laplacian operators for instance）. Even though the algorithm is formally similar to the Rayleigh quotient iteration which is well-known in the linear situation, but they are essentially different. The point is that the standard Rayleigh quotient cannot be used as a shift in the non-linear setup. We have to employ a different quantity which has been obtained only recently. As a surprised gift, the explicit formulas for the algorithm restricted to the linear case （p = 2） is obtained, which improves the author＇s approximating procedure for the leading eigenvalues in different context, appeared in a group of publications. The paper begins with p-Laplacian, and is closed by the non-linear operators corresponding to the well-known Hardy-type inequalities.

Linear homotopy method for computing generalized tensor eigenpairs

Let m, m＇, n be positive integers such that m ≠ m＇. Let A be an ruth order n-dimensional tensor, and let B be an m＇th order n-dimensional tensor. ）, ∈ C is called a B-eigenvalue of A if Ax^m-1 = λBx^m＇-1 and Bx^m＇ = 1 for some x ∈ Cn／{0}. In this paper, we propose a linear homotopy method for solving this eigenproblem. We prove that the method finds all isolated B- eigenpairs. Moreover, it is easy to implement. Numerical results are provided to show the efficiency of the proposed method.

BLOCK ALGORITHMS WITH AUGMENTED RAYLEIGH-RITZ PROJECTIONS FOR LARGE-SCALE EIGENPAIR COMPUTATION

Most iterative algorithms for eigenpair computation consist of two main steps:a subspace update(SU)step that generates bases for approximate eigenspaces,followed by a Rayleigh-Ritz(RR)projection step that extracts approximate eigenpairs.So far the predominant methodology for the SU step is based on Krylov subspaces that builds orthonormal bases piece by piece in a sequential manner.In this work,we investigate block methods in the SU step that allow a higher level of concurrency than what is reachable by Krylov subspace methods.To achieve a competitive speed,we propose an augmented Rayleigh-Ritz(ARR)procedure.Combining this ARR procedure with a set of polynomial accelerators,as well as utilizing a few other techniques such as continuation and deflation,we construet a block algorithm designed to reduce the number of RR steps and elevate concurrency in the SU steps.Extensive computational experiments are conducted in C on a representative set of test problems to evaluate the performance of two variants of our algorithm.Numerical results,obtained on a many-core computer without explicit code parallelization,show that when computing a relatively large number of eigenpairs,the performance of our algorithms is competitive with that of several state-of-the-art eigensolvers.

Maximal number of distinct H-eigenpairs for a two-dimensional real tensor

Based on the generalized characteristic polynomial introduced by J. Canny in Generalized characteristic polynomials [J. Symbolic Comput., 1990, 9（3）： 241-250], it is immediate that for any m-order n-dimensional real tensor, the number of distinct H-eigenvalues is less than or equal to n（m-1）n-1. However, there is no known bounds on the maximal number of distinct H- eigenvectors in general. We prove that for any m ~〉 2, an m-order 2-dimensional tensor sd exists such that d has 2（m - 1） distinct H-eigenpairs. We give examples of 4-order 2-dimensional tensors with six distinct H-eigenvalues as well as six distinct H-eigenvectors. We demonstrate the structure of eigenpairs for a higher order tensor is far more complicated than that of a matrix. Further- more, we introduce a new class of weakly symmetric tensors, called p-symmetric tensors, and show under certain conditions, p-symmetry will effectively reduce the maximal number of distinct H-eigenveetors for a given two-dimensional tensor. Lastly, we provide a complete classification of the H-eigenvectors of a given 4-order 2-dimensional nonnegative p-symmetric tensor. Additionally, we give sufficient conditions which prevent a given 4-order 2-dimensional nonnegative irreducible weakly symmetric tensor from possessing six pairwise distinct H-eigenveetors.

Global algorithms for maximal eigenpair

这份报纸是我们的以前的工作的继续[前面。数学。中国， 2016， 11 (6 ) ：1379-1418 ] 在为计算最大的 eigenpair 的一个有效算法为 tridiagonal 矩阵首先被介绍然后与 nonnegative 延长了到无法缩减的矩阵的地方离开斜的元素。这份报纸主要为在相当一般的安装计算最大的 eigenpair 介绍二个全球算法，包括甚至一个班真实(与一些否定离开斜的元素) 或复杂矩阵。

基于免逆牛顿法的对称张量Z-特征对可信验证

利用免逆牛顿法及区间算法理论,研究对称张量Z-特征对的可信验证问题,提出了一种计算Z-特征对的区间算法.该算法通过输出一个近似Z-特征对及其相应的误差界,使得在近似解的误差范围内必存在一个精确的Z-特征对.

工程结构特征值问题的加速解法

求解大型结构动态响应的特征模态时 ,用传统的特征值解法很难完成 ,因此有必要寻求一种加速解法。本文给出了求解大型结构特征值问题的加速子空间迭代法 ,它采用Gram Schmidt正交化过程消除收敛的特征向量 ,使得计算时间大大减少。文中通过实例对此法进行了验证。

非对称阻尼系统特征对一阶导数与二阶导数的计算

提出了一种计算非对称阻尼系统特征对一阶、二阶导数的方法.该方法利用阻尼系统的特征向量计算特征对的导数,避免了状态空间中特征向量的使用,节省了计算量,且不要求系统所有特征值的互异性.最后以两个非对称阻尼系统进行数值试验,数值结果表明提出的方法是有效的.

多参数结构特征二阶灵敏度

提出了一种有效计算多参数结构特征值与特征向量二阶灵敏度矩阵——Hessian矩阵的方法.将特征值和特征向量二阶摄动法转变为多参数形式,推导出二阶摄动灵敏度矩阵,由此得到特征值和特征向量的二阶估计式.该法解决了无法用直接求导法计算特征值和特征向量二阶灵敏度矩阵的问题.数值算例说明了该算法的应用和计算精度.

对称箭形矩阵最大最小特征对的逆特征值问题的一个有效算法

研究一个对称箭形矩阵的逆特征值问题:给定非零向量x∈Rn,y∈Rk,k≤n,以及两个实数λ>μ,求对称箭形矩阵A,使得(,λx)是对称箭形矩阵A的最大特征对,而(μ,y)是A的k阶顺序主子阵Ak的最小特征对。给出该问题有解的充分必要条件,并且给出一个算法计算该问题的一个解,数值实例说明是可行的。

R对称矩阵左右逆特征值问题的有解条件

研究了R对称矩阵的左右逆特征值问题,得到可解条件及一般解的表达式.本文的结论推广了李范良的文章:反中心对称矩阵的左右逆特征值问题.

交叉研究的感悟

本文是基于北京大学"许宝騄讲座"(2019/3/22)及随后在各地的报告扩充而成.开头是受惠于许宝騄先生的一些回忆;末尾是感谢北京大学一批老师几十年来的支持和帮助.中间的主题部分先给出个人交叉研究的概述.然后从来自计算的挑战,进入一年多来笔者关于具有实谱的复矩阵理论的研究.这涉及计算、概率、统计力学和量子力学等领域.随后介绍算法方面的最新进展,此乃概率论与计算交叉的又一案例.作为结束,也略述交叉研究的感悟.

一种快速的混合参数宏模型无源性补偿方法

针对高速数字系统的混合参数宏建模,采用模态矢量拟合算法具有模型拟合精度高,易于电路仿真集成的优点,但是初始生成的宏模型可能会在局部频带内不满足系统无源性。该文提出一种基于特征值模态的混合参数宏模型无源性补偿方法。首先构建了包含宏模型特征对的无源性目标函数,利用特征对加权的形式来控制参数扰动带来的模型精度变化,然后结合相应的无源性约束条件,将无源性补偿过程等效为可以解析求解的最小二乘优化问题。该方法在补偿混合参数宏模型无源性的同时,兼顾模型在任意端接激励源条件下的应用要求。结合实例并与留数扰动、模态扰动等无源性补偿方法进行比较,结果表明该方法在精度或效率方面均具有一定的优势。