Singular value decompositions of complex symmetric matrices

If the complex square matrix A is symmetric, i.e. A=A^T, then it has a symmetric singular value decomposition A=Q∑Q^T. An algorithm is presented for the computation of this decomposition.     

On completions of symmetric and antisymmetric block diagonal partial matrices

A partial matrix is a matrix where only some of the entries are given. We determine the maximum rank of the symmetric completions of a symmetric partial matrix where only the diagonal blocks are given and the minimum rank and the maximum rank of the antisymmetric completions of an antisymmetric partial matrix where only the diagonal blocks are given.     

On matrices stochastically similar to matrices with equal diagonal elements

A necessary and sufficient condition for a matrix to be stochastically similar to a matrix with equal diagonal elements is obtained Aand B are called Stochastically similar if B=SAS^{- 1} where S is quasi-stochastic i.e., all row sums of .S are I. An inverse elementary divisor problem for quasi-stochastic matrices is also considered.     

On matrices stochastically similar to matrices with equal diagonal elements

A necessary and sufficient condition for a matrix to be stochastically similar to a matrix with equal diagonal elements is obtained Aand B are called Stochastically similar if B=SAS ^{? 1} where S is quasi-stochastic i.e., all row sums of .S are I. An inverse elementary divisor problem for quasi-stochastic matrices is also considered.     

Counterexamples concerning the diagonal elements of normal matrices

An infinite class of conditions known to be satisfied by the diagonal elements of a normal matrix with prescribed spectrum is shown to be independent of other known conditions satisfied by the diagonal elements but nevertheless insufficient to characterize the diagonal.     

Singular values of Gaussian matrices and permanent estimators

We present estimates on the small singular values of a class of matrices with independent Gaussian entries and inhomogeneous variance profile, satisfying a broad‐connectedness condition. Using these estimates and concentration of measure for the spectrum of Gaussian matrices with independent entries, we prove that for a large class of graphs satisfying an appropriate expansion property, the Barvinok–Godsil‐Gutman estimator for the permanent achieves sub‐exponential errors with high probability. © 2014 Wiley Periodicals, Inc. Random Struct. Alg., 48, 183–212, 2016     

Factoring symmetric totally nonpositive matrices and inverses with a diagonal pivoting method

In this paper, we consider how to factor symmetric totally nonpositive matrices and their inverses by taking advantage of the symmetric property. It is well-known that the Bunch-Kaufman algorithm is the most commonly used pivoting strategy which can, however, produce arbitrarily large entries in the lower triangular factor for such matrices as illustrated by our example. Therefore, it is interesting to show that when the Bunch-Parlett algorithm is simplified for these matrices, it only requires O(n ²) comparisons with the growth factor being nicely bounded by 4. These facts, together with a nicely bounded lower triangular factor and a pleasantly small relative backward error, show that the Bunch-Parlett algorithm is more preferable than the Bunch-Kaufman algorithm when dealing with these matrices.     

On the product of the diagonal elements of hermitian matrices

In [5] M. Marcus stated a conjecture concerning the product of the diagonal elements of normal matrices which is false [6]. In this paper we prove that such a conjecture is true in the Hermitian case.     

Principal minors of complex symmetric and skew matrices

The singular values of principal submatrices of complex symmetric and skew matrices are examined.     

On rank-one corrections of complex symmetric matrices

Let a matrix A ∈ M_n(C) be a rank-one perturbation of a complex symmetric matrix, i.e., A = X + Y for some unknown matrices X and Y such that X = X^T and rank Y = 1. The problem of determining the matrices X and Y is solved. Bibliography: 4 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 334, 2006, pp. 78–83.     

Algebraic multigrid for complex symmetric matrices and applications

This paper is concerned with the numerical study of an algebraic multigrid preconditioner for complex symmetric system matrices. We use several different Krylov subspace methods as an outer iteration, namely the QMR method proposed by Freund and Nachtigal, the BiCGCR method of Clemens, and the CSYM method of Bunse-Gerstner and Stoever. In addition, we compare the results with the standard Jacobi preconditioner for complex symmetric problems. We test our approach on the numerical simulation of high-voltage insulators.     

A min-max theorem for complex symmetric matrices

We optimize the form Re x^tTx to obtain the singular values of a complex symmetric matrix T. We prove that for ,

     

Products of exponentials of hermitian and complex symmetric matrices

A conjecture involving exponentials of Hermitian matrices is stated, and proved when one of the matrices has rank at most one. As a consequence, the complete conjecture is proved when the matrices are 2×2. A second conjecture involving exponentials of complex symmetric matrices is also stated, and completely proved when the matrices are 2×2. The two conjectures have similar structures but require quite different techniques to analyze.     

Stabilizing block diagonal preconditioners for complex dense matrices in electromagnetics

Preconditioning techniques are widely used to speed up the convergence of iterative methods for solving large linear systems with sparse or dense coefficient matrices. For certain application problems, however, the standard block diagonal preconditioner makes the Krylov iterative methods converge more slowly or even diverge. To handle this problem, we apply diagonal shifting and stabilized singular value decomposition (SVD) to each diagonal block, which is generated from the multilevel fast multiple algorithm (MLFMA), to improve the stability and efficiency of the block diagonal preconditioner. Our experimental results show that the improved block diagonal preconditioner maintains the computational complexity of MLFMA, converges faster and also reduces the CPU cost.     

具不变主对角线元矩阵新的特征值包含集

利用S-SDD矩阵的非奇异性给出具不变主对角线元矩阵非奇异的一个充分条件,并由此得到了具不变主对角线元矩阵的一个新的特征值包含集,改进了相关文献的结果.最后把该结果应用到Toeplitz矩阵,得到Toeplitz矩阵的一个新的特征值包含集.文中数值例子表明在某些情况下该结果也改进了几个已有结果.