矩阵的几个不等式

利用谱分解定理和几个标量不等式,得到了矩阵加权几何均值和酉不变范数的几个不等式,它们是Kittaneh和Manasrah所得相关结果的改进.     

广义半正定矩阵的一个不等式

在广义半正定矩阵上推广、改进了Ｏｐｐｅｎｈｅｉｍ不等式。     

复矩阵的亚半正定性

复亚半正定矩阵是Hermite正定阵的推广,研究了它的Kronecker积,Hadamard积和行列式理论,将实对称阵的Schur定理,华罗庚定理,Minkowski不等式,Ky-Fan不等式,Ostrowski-Taussky不等式推广到一类非Hermite复矩阵上,扩大了Minkowski不等式的指数范围,削弱了华罗庚不等式的条件。     

复正定矩阵的Minkowski不等式

建立了复正定矩阵的几个行列式不等式,将正定Ｈｅｒｍｉｔｅ阵的Ｍｉｎｋｏｗｓｋｉ不等式、 Ｏｓｔｒｏｗｓｋｉ－Ｔａｕｓｓｋｙ不等式推广到了复正定矩阵上,推广改进了一些文献的结果．     

矩阵的酉不变范数不等式

利用凹函数和半正定矩阵的性质,讨论并且得到了一些矩阵Rotfel型范数不等式.另外,通过研究Hermitian矩阵和斜Hermitian矩阵和的特征值的模行列式的不等式,得到一些关于Hermitian矩阵和斜Hermitian矩阵和的范数不等式.推广了文献中的相关结果.     

关于酉不变范数不等式的一个注记

本文研究酉不变范数不等式的问题.利用函数的凸性,得到关于矩阵酉不变范数的几个不等式,理论验证,证明了新不等式优于相关文献中的结果.     

两个酉不变范数不等式的推广

本文研究了矩阵酉不变范数不等式的问题.在v∈[0,1]时,利用函数?(v)=‖A^vXB^1-v+A^1-vXB^v‖的凸性,推广了两个酉不变范数不等式.     

与复半正定方阵相关的矩阵分解及其应用

与复数域相对应,从Hermite方阵的对角标准型出发,对复方阵的分解,半正定方阵的复线性组合,极分解进行较为详细的探讨.基此可证明任何两个相似的同阶Hermite方阵一定是酉相似的.     

关于矩阵奇异值p-范数的讨论的注记

指出近期矩阵奇异值p-范数的讨论中一些值得商榷的问题．应用已有的半正定Hermitian矩阵特征值和迹的性质，我们研究了相关问题．     

正定复矩阵的若干性质

本文讨论一类正定复矩阵的某些性质，特别给出了两个正定复矩阵的积仍为正定的条件，以及正定复矩阵的一种分解．     

Commutator inequalities associated with the polar decomposition

Let be a polar decomposition of an complex matrix . Then for every unitarily invariant norm , it is shown that

where denotes the operator norm. This is a quantitative version of the well-known result that is normal if and only if . Related inequalities involving self-commutators are also obtained.

     

Convergence properties of preconditioned Hermitian and skew-Hermitian splitting methods for non-Hermitian positive semidefinite matrices

For the non-Hermitian and positive semidefinite systems of linear equations, we derive necessary and sufficient conditions for guaranteeing the unconditional convergence of the preconditioned Hermitian and skew-Hermitian splitting iteration methods. We then apply these results to block tridiagonal linear systems in order to obtain convergence conditions for the corresponding block variants of the preconditioned Hermitian and skew-Hermitian splitting iteration methods.

     

Norm inequalities for matrix geometric means of positive definite matrices

We obtain eigenvalue inequalities for matrix geometric means of positive definite matrices. This implies matrix norm inequalities for unitarily invariant norms, which are considered as complementary to a series of norm inequalities among geometric means. We give complements of the Ando–Hiai type inequality for the Karcher mean by means of the generalized Kantorovich constant. Finally, we consider the monotonicity of the eigenvalue function for the Karcher mean.     

On parallel multisplitting methods for symmetric positive semidefinite linear systems

In this paper we propose some parallel multisplitting methods for solving consistent symmetric positive semidefinite linear systems, based on modified diagonally compensated reduction. The semiconvergence of the parallel multisplitting method is discussed. The results here generalize some known results for the nonsingular linear systems to the singular systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Improved Young and Heinz inequalities for matrices

We give refinements of the classical Young inequality for positive real numbers and we use these refinements to establish improved Young and Heinz inequalities for matrices.     

子空间上的对称正定及对称半正定阵的左右特征值反问题

文[1][2][3]中讨论AX＝B的对称阵逆特征值问题，文[4][5][6]中讨论了半正定阵的逆特征值问题。本文讨论了空间了子空间上的对称正定及对称半正定阵的左右特征值反问题，给出了解存在的充分条件及解的表达式。     

Equality conditions for the singular values of 3 × 3 matrices with one-point spectrum

Let Γ_a be an upper triangular 3 × 3 matrix with diagonal entries equal to a complex scalar a. Necessary and su.cient conditions are found for two of the singular values of Γ_a to be equal. These conditions are much simpler than the equality discr ? = 0, where the expression in the left-hand side is the discriminant of the characteristic polynomial ? of the matrix _Ga = Γ_aΓ_a. Understanding the behavior of singular values of Γ_a is important in the problem of finding a matrix with a triple zero eigenvalue that is closest to a given normal matrix A.