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Matrix analysis
TLDR
This new edition of the acclaimed text presents results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrates their importance in a variety of applications. Expand
Topics in Matrix Analysis
1. The field of values 2. Stable matrices and inertia 3. Singular value inequalities 4. Matrix equations and Kronecker products 5. Hadamard products 6. Matrices and functions.
Positive definite completions of partial Hermitian matrices
Abstract The question of which partial Hermitian matrices (some entries specified, some free) may be completed to positive definite matrices is addressed. It is shown that if the diagonal entries areExpand
Totally Nonnegative Matrices
Totally nonnegative matrices arise in a remarkable variety of mathematical applications. This book is a comprehensive and self-contained study of the essential theory of totally nonnegative matrices,Expand
Inverse M-matrices☆
Abstract This is an attempt at a comprehensive expository study of those nonnegative matrices which happen to be inverses of M -matrices and is aimed at an audience conversant with basic ideas ofExpand
Row Stochastic Matrices Similar to Doubly Stochastic Matrices
The problem of determining which row stochastic n-by-n matrices are similar to doubly stochastic matrices is considered. That not all are is indicated by example, and an abstract characterization asExpand
Sufficient conditions for D-stability
Abstract Sufficient conditions for an n by n matrix to be D-stable are surveyed. Use is made of some transformations under which the D-stables are invariant and relations among the conditions areExpand
NUMERICAL DETERMINATION OF THE FIELD OF VALUES OF A GENERAL COMPLEX MATRIX
For an $n \times n$ complex matrix A, the convexity of $F(A) \equiv \{ x^ * Ax:x^ * x = 1,x \in C^n \} $ and some simple observations are exploited to determine certain boundary points and tangentsExpand
The maximum multiplicity of an eigenvalue in a matrix whose graph is a tree
We study the maximum possible multiplicity of an eigenvalue of a matrix whose graph is a tree, expressing that maximum multiplicity in terms of certain parameters associated with the tree.
A note on cospectral graphs
TLDR
It is shown that in many instances this adjacency matrix is superior to the usual (0, 1) adjacencies, and will distinguish cospectral pairs, when the latter will not. Expand
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