关于κ-广义酉矩阵

本文研究了κ-广义酉矩阵的性质及其与酉矩阵、辛矩阵、Householder矩阵之间的联系，取得了许多新的结果，推广了酉矩阵及Householder矩阵的相应结果，特别将正交矩阵的广义Cayley分解推广到了广义酉矩阵上；并将各类酉矩阵及辛矩阵统一了起来．     

广义酉矩阵与广义Hermite矩阵

给出了广义酉矩阵与广义(斜)Hermite矩阵的概念，研究了它们的性质及其与酉阵、共轭辛阵、Hermite阵、Hamilton及广义逆矩阵之间的联系；取得了许多新的结果；推广了酉矩阵、Hermite阵与斜Hermite阵间的相应结果，特别将正交阵的广义Cayley分解推广到了广义酉矩阵上；将各类酉矩阵、Hermite矩阵及广义逆矩阵统一了起来．     

k-广义Hermite矩阵

本文给出了k-广义(反)Hermite矩阵的概念,研究了它的性质及其与k-广义酉矩阵之间的联系,推广了酉矩阵和(反)Hermite矩阵的相应结果.     

广义酉矩阵与广义Hermite矩阵的张量积与诱导矩阵

本文研究了有限个广义酉矩阵与广义(反)Hermite矩阵的张量积和诱导矩阵.利用矩阵的张量积和诱导矩阵的性质,得到了它的张量积和诱导矩阵仍为广义酉矩阵与广义(反)Hermite矩阵.     

一四元数矩阵方程组的广义酉矩阵解

给出了四元数矩阵方程组[XmnAns=Bns,XnnCnt=Dnt]有广义酉矩阵解的充要条件及其解集结构。     

关于次酉矩阵与次镜象矩阵

提出了共轭次转置矩阵、次酉矩阵与次镜象矩阵的概念，对它们的基本性质及其与（反）次Kermite阵的关系进行了深入的研究，获得了一些新的结果，将正交阵的广义Gayley分解推广到了次酉阵上。     

拟酉矩阵与拟Hermite矩阵

利用次Hermite矩阵给出了拟酉矩阵与（反）拟Hermite矩阵的概念,研究了它们的基础本性质及其之间的关系,将各类酉矩阵与Hermite矩阵一了起来。     

关于矩阵方程XB=C的结构解

很多应用中导出矩阵方程XB=G,本文考虑此方程的结构解.首先考虑自伴矩阵解及反自伴矩阵解,接下来考虑广义对称解及广义反对称解,最后讨论更广泛的矩阵方程AXB=C的酉矩阵解.所得结果推广了Sun,Tisseur,Trench等人的-些结果.     

次正规矩阵、次酉矩阵、次厄米特矩阵及反次厄米特矩阵

主要研究了下列几方面问题:(i)次酉矩阵、次厄米特矩阵及反次厄米特矩阵的特征值与次特征值;(ii)次正规矩阵、次酉矩阵、次厄米特矩阵及反次厄米特矩阵分别与正规矩阵、酉矩阵、厄米特矩阵及反厄米特矩阵之间的关系;(iii)次正规矩阵、次酉矩阵、次厄米特矩阵及反次厄米特矩阵之间的联系.     

四元数矩阵的广义Schmidt分解与广酉空间中向量组的广义标准正交化

推广了四元数矩阵的Schmidt分解及广酉空间中向量组的标准正交化问题，给出了实四元数矩阵分解为广酉矩阵与生对角元全正的上三角阵乘积的实用方法．     

Computing a logarithm of a unitary matrix with general spectrum

We analyze an algorithm for computing a skew‐Hermitian logarithm of a unitary matrix and also skew‐Hermitian approximate logarithms for nearly unitary matrices. This algorithm is very easy to implement using standard software, and it works well even for unitary matrices with no spectral conditions assumed. Certain examples, with many eigenvalues near ? 1, lead to very non‐Hermitian output for other basic methods of calculating matrix logarithms. Altering the output of these algorithms to force skew‐Hermitian output creates accuracy issues, which are avoided by the considered algorithm. A modification is introduced to deal properly with the J‐skew‐symmetric unitary matrices. Applications to numerical studies of topological insulators in two symmetry classes are discussed. Copyright © 2014 John Wiley & Sons, Ltd.     

矩阵在量子计算与量子信息中的应用

介绍了量子计算与量子信息中的一些重要的矩阵及其应用,包括密度矩阵、酉矩阵等.     

On tridiagonal matrices unitarily equivalent to normal matrices

In this article the unitary equivalence transformation of normal matrices to tridiagonal form is studied.It is well-known that any matrix is unitarily equivalent to a tridiagonal matrix. In case of a normal matrix the resulting tridiagonal inherits a strong relation between its super- and subdiagonal elements. The corresponding elements of the super- and subdiagonal will have the same absolute value.In this article some basic facts about a unitary equivalence transformation of an arbitrary matrix to tridiagonal form are firstly studied. Both an iterative reduction based on Krylov sequences as a direct tridiagonalization procedure via Householder transformations are reconsidered. This equivalence transformation is then applied to the normal case and equality of the absolute value between the super- and subdiagonals is proved. Self-adjointness of the resulting tridiagonal matrix with regard to a specific scalar product is proved. Properties when applying the reduction on symmetric, skew-symmetric, Hermitian, skew-Hermitian and unitary matrices and their relations with, e.g., complex symmetric and pseudo-symmetric matrices are presented.It is shown that the reduction can then be used to compute the singular value decomposition of normal matrices making use of the Takagi factorization. Finally some extra properties of the reduction as well as an efficient method for computing a unitary complex symmetric decomposition of a normal matrix are given.     

算子值酉随机矩阵及其渐近自由性

该文在算子值非交换概率空间上引入半标准酉随机矩阵的概念, 证明了它是算子值Haar酉元的矩阵模型,并给出了半标准酉随机矩阵的渐近自由判定定理.     

When is a matrix unitary or Hermitian plus low rank?

Hermitian and unitary matrices are two representatives of the class of normal matrices whose full eigenvalue decomposition can be stably computed in quadratic computing complexity once the matrix has been reduced, for instance, to tridiagonal or Hessenberg form. Recently, fast and reliable eigensolvers dealing with low‐rank perturbations of unitary and Hermitian matrices have been proposed. These structured eigenvalue problems appear naturally when computing roots, via confederate linearizations, of polynomials expressed in, for example, the monomial or Chebyshev basis. Often, however, it is not known beforehand whether or not a matrix can be written as the sum of a Hermitian or unitary matrix plus a low‐rank perturbation. In this paper, we give necessary and sufficient conditions characterizing the class of Hermitian or unitary plus low‐rank matrices. The number of singular values deviating from 1 determines the rank of a perturbation to bring a matrix to unitary form. A similar condition holds for Hermitian matrices; the eigenvalues of the skew‐Hermitian part differing from 0 dictate the rank of the perturbation. We prove that these relations are linked via the Cayley transform. Then, based on these conditions, we identify the closest Hermitian or unitary plus rank k matrix to a given matrix A, in Frobenius and spectral norm, and give a formula for their distance from A. Finally, we present a practical iteration to detect the low‐rank perturbation. Numerical tests prove that this straightforward algorithm is effective.     

Reachability of condensed forms via unitary congruence transformations

There are several well-known facts about unitary similarity transformations of complex n-by-n matrices: every matrix of order n = 3 can be brought to tridiagonal form by a unitary similarity transformation; if n ≥ 5, then there exist matrices that cannot be brought to tridiagonal form by a unitary similarity transformation; for any fixed set of positions (pattern) S whose cardinality exceeds n(n ? 1)/2, there exists an n-by-n matrix A such that none of the matrices that are unitarily similar to A can have zeros in all of the positions in S. It is shown that analogous facts are valid if unitary similarity transformations are replaced by unitary congruence ones.     

酉空间中一类子空间的排列问题与具有纠错能力的d~z-析取矩阵

Pooling设计在实践中有着广泛的应用,它的数学模型是d~z-析取矩阵.本文利用酉空间的一类子空间构做了一类新的d~z-析取矩阵.为了讨论此设计的纠错能力,重点研究了酉空间中的一类子空间的排列问题,即对于酉空间F_q~2~((n))上的一个给定的(m,s)型子空间C和一个整数d,找到C的d个(m-1,s-1)型子空间H_1,H_2,…,H_d,使得包含在H_1∪H_2∪…∪H_d中的(r,s-4)型子空间的个数最多,并确定这个数的上界.然后应用此结果,给出了d~z-析取矩阵中反映纠错能力的z值的紧界.