A scalarization technique for computing the power and exponential moments of Gaussian random matrices.
A semigroup treatment of some theorems on non-negative matrices
A sharp upper bound for the spectral radius of a nonnegative matrix and applications
We obtain a sharp upper bound for the spectral radius of a nonnegative matrix. This result is used to present upper bounds for the adjacency spectral radius, the Laplacian spectral radius, the signless Laplacian spectral radius, the distance spectral radius, the distance Laplacian spectral radius, the distance signless Laplacian spectral radius of an undirected graph or a digraph. These results are new or generalize some known results.
A shorter proof of the distance energy of complete multipartite graphs
Caporossi, Chasser and Furtula in [Les Cahiers du GERAD (2009) G-2009-64] conjectured that the distance energy of a complete multipartite graph of order n with r ≥ 2 parts, each of size at least 2, is equal to 4(n − r). Stevanovic, Milosevic, Hic and Pokorny in [MATCH Commun. Math. Comput. Chem. 70 (2013), no. 1, 157-162.] proved the conjecture, and then Zhang in [Linear Algebra Appl. 450 (2014), 108-120.] gave another proof. We give a shorter proof of this conjecture using the interlacing inequalities...
A simple cocyclic Jacket matrices.
A simple derivation of the eigenvalues of a tridiagonal matrix arising in biogeography
In investigating a certain optimization problem in biogeography, Simon [IEEE Trans. Evolutionary Comput. 12 (2008), 702-713] encountered a certain specially structured tridiagonal matrix and made a conjecture regarding its eigenvalues. A few years later, the validity of the conjecture was established by Igelnik and Simon [Appl. Math. Comput. 218 (2011), 195-201]. In this paper, we give another proof of this conjecture that is much shorter, almost computation-free, and does not resort to the eigenvectors...
A simple method for estimating the bounds of spectral radius of nonnegative irreducible matrices.
A simple proof of polar decomposition in pseudo-Euclidean geometry
We give a simple direct proof of the polar decomposition for separated linear maps in pseudo-Euclidean geometry.
A simple proof of the classification of normal Toeplitz matrices.
A Simple Proof of the Convexity of the Field of Values Defined by Two Hermitian Forms.
A simple proof of Valiant's lemma
A solution of the continuous Lyapunov equation by means of power series
A sparsity result on nonnegative real matrices with given spectrum
Let σ=(λ1,...,λn) be the spectrum of a nonnegative real n × n matrix. It is shown that σ is the spectrum of a nonnegative real n × n matrix having at most nonzero entries.
A spectral theorem for matrices over fields of power series
A Spectral Theory for Tensors
In this paper we propose a general spectral theory for tensors. Our proposed factorization decomposes a tensor into a product of orthogonal and scaling tensors. At the same time, our factorization yields an expansion of a tensor as a summation of outer products of lower order tensors. Our proposed factorization shows the relationship between the eigen-objects and the generalised characteristic polynomials. Our framework is based on a consistent multilinear algebra which explains how to generalise...
A strategy for proving Riemann hypothesis.
A strong complement property of Dedekind domains
A stronger version of matrix convexity as applied to functions of Hermitian matrices.
A structured staircase algorithm for skew-symmetric/symmetric pencils.
A study on new right/left inverses of nonsquare polynomial matrices
This paper presents several new results on the inversion of full normal rank nonsquare polynomial matrices. New analytical right/left inverses of polynomial matrices are introduced, including the so-called τ-inverses, σ-inverses and, in particular, S-inverses, the latter providing the most general tool for the design of various polynomial matrix inverses. The applicationoriented problem of selecting stable inverses is also solved. Applications in inverse-model control, in particular robust minimum...