scholarly journals Rank relations between a {0, 1}-matrix and its complement

2018 ◽  
Vol 16 (1) ◽  
pp. 190-195
Author(s):  
Chao Ma ◽  
Jin Zhong
Keyword(s):  
Diagonal Entry ◽  
Symmetric Case ◽  
Identity Matrix ◽  
The Matrix ◽  
Rank Relations

AbstractLet A be a {0, 1}-matrix and r(A) denotes its rank. The complement matrix of A is defined and denoted by Ac = J − A, where J is the matrix with each entry being 1. In particular, when A is a square {0, 1}-matrix with each diagonal entry being 0, another kind of complement matrix of A is defined and denoted by A = J − I − A, where I is the identity matrix. We determine the possible values of r(A) ± r(Ac) and r(A) ± r(A) in the general case and in the symmetric case. Our proof is constructive.

scholarly journals Toeplitz Operators with Lagrangian Invariant Symbols Acting on the Poly-Fock Space of ℂ n

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jorge Luis Arroyo Neri ◽  
Armando Sánchez-Nungaray ◽  
Mauricio Hernández Marroquin ◽  
Raquiel R. López-Martínez
Keyword(s):  
Toeplitz Operators ◽  
Complex Space ◽  
Fock Space ◽  
Identity Matrix ◽  
The Matrix

We introduce the so-called extended Lagrangian symbols, and we prove that the C ∗ -algebra generated by Toeplitz operators with these kind of symbols acting on the homogeneously poly-Fock space of the complex space ℂ n is isomorphic and isometric to the C ∗ -algebra of matrix-valued functions on a certain compactification of ℝ n obtained by adding a sphere at the infinity; moreover, the matrix values at the infinity points are equal to some scalar multiples of the identity matrix.

Energy Spectrum of a Particle Within a Confined Double Well Potential

2006 ◽  
Vol 3 (2) ◽  
pp. 257-262
Author(s):  
J. L. Marin ◽  
G. Campoy ◽  
R. Riera
Keyword(s):  
Energy Levels ◽  
Numerical Approach ◽  
Symmetric Case ◽  
Basis Functions ◽  
Hidden Symmetry ◽  
The Matrix ◽  
Jahn Teller ◽  
Free Case ◽  
Jahn Teller Effect ◽  
Double Well Potential

The energy levels of a particle within a confined double well potential are studied in this work. The spectrum of the particle can be obtained by solving the corresponding Schrödinger equation but, for practical purposes, we have used a numerical approach based in the diagonalization of the matrix related to the Hamiltonian when the wavefunction is represented as an expansion in terms of "a particle-in-a-box" basis functions. The results show that, in the symmetric confining case, the energy levels are degenerate and a regular pairwise association between them is observed, similarly as it occurs in the free case. Moreover, when the confining is asymmetric, the degeneration is partially lifted but the pairwise association of the energy levels becomes irregular. The lifting of the degeneration in the latter case is addressed to the lack of symmetry or distortion of the system, namely, to a sort of Jahn-Teller effect which is common in the energy levels of diatomic molecules, to which a double well potential can be crudely associated. In the symmetric case, the states with nodes at the origin are recognized to be the same as those of the harmonic oscillator confined by two impenetrable walls, in such a way that the system presented in this work would be interpreted as half the solution of the problem of a particle within a confined four well potential. The latter suggests the existence of a sort of hidden symmetry which remains to be studied in a more detailed way.

scholarly journals On the Main Eigenvalues of Universal Adjacency Matrices and U-Controllable Graphs

2015 ◽  
Vol 30 ◽  
pp. 812-826
Author(s):  
Alexander Farrugia ◽  
Irene Sciriha
Keyword(s):  
Adjacency Matrix ◽  
Sufficient Conditions ◽  
Laplacian Matrix ◽  
Simple Graph ◽  
Identity Matrix ◽  
The Matrix ◽  
Vertex Degrees ◽  
Definition Of ◽  
Main Eigenvalues ◽  
Necessary And Sufficient

A universal adjacency matrix U of a graph G is a linear combination of the 0–1 adjacency matrix A, the diagonal matrix of vertex degrees D, the identity matrix I and the matrix J each of whose entries is 1. A main eigenvalue of U is an eigenvalue having an eigenvector that is not orthogonal to the all–ones vector. It is shown that the number of distinct main eigenvalues of U associated with a simple graph G is at most the number of orbits of any automorphism of G. The definition of a U–controllable graph is given using control–theoretic techniques and several necessary and sufficient conditions for a graph to be U–controllable are determined. It is then demonstrated that U–controllable graphs are asymmetric and that the converse is false, showing that there exist both regular and non–regular asymmetric graphs that are not U–controllable for any universal adjacency matrix U. To aid in the discovery of these counterexamples, a gamma–Laplacian matrix L(gamma) is used, which is a simplified form of U. It is proved that any U-controllable graph is a L(gamma)–controllable graph for some parameter gamma.

scholarly journals Non-Unit Bidiagonal Matrices for Factorization of Vandermonde Matrices

2015 ◽  
Vol 12 ◽  
pp. 1-13
Author(s):  
R. Purushothaman Nair
Keyword(s):  
Vandermonde Matrix ◽  
Inverse Matrix ◽  
Identity Matrix ◽  
Vandermonde Matrices ◽  
The Matrix ◽  
Matrix Transforms ◽  
The Given ◽  
Zero Vector

A non-unit bidiagonal matrix and its inverse with simple structures are introduced. These matrices can be constructed easily using the entries of a given non-zero vector without any computations among the entries. The matrix transforms the given vector to a column of the identity matrix. The given vector can be computed back without any round off error using the inverse matrix. Since a Vandermonde matrix can also be constructed using given n quantities, it is established in this paper that Vandermonde matrices can be factorized in a simple way by applying these bidiagonal matrices. Also it is demonstrated that factors of Vandermonde and inverse Vandermonde matrices can be conveniently presented using the matrices introduced here.

scholarly journals The Fundamental Matrix of the Simple Random Walk with Mixed Barriers

2016 ◽  
Vol 8 (6) ◽  
pp. 128
Author(s):  
Yao Elikem Ayekple ◽  
Derrick Asamoah Owusu ◽  
Nana Kena Frempong ◽  
Prince Kwaku Fefemwole
Keyword(s):  
Random Walk ◽  
Fundamental Matrix ◽  
Transition Matrix ◽  
Transition Probabilities ◽  
Simple Random Walk ◽  
Identity Matrix ◽  
The Matrix

The simple random walk with mixed barriers at state $ 0 $ and state $ n $ defined on non-negative integers has transition matrix $ P $ with transition probabilities $ p_{ij} $. Matrix $ Q $ is obtained from matrix $ P $ when rows and columns at state $ 0 $ and state $ n $ are deleted . The fundamental matrix $ B $ is the inverse of the matrix $ A = I -Q $, where $ I $ is an identity matrix. The expected reflecting and absorbing time and reflecting and absorbing probabilities can be easily deduced once $ B $ is known. The fundamental matrix can thus be used to calculate the expected times and probabilities of NCD's.

scholarly journals Necessary and Sufficient Conditions for the Existence of a Positive Definite Solution for the Matrix Equation

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Naglaa M. El-Shazly
Keyword(s):  
Matrix Equation ◽  
Sufficient Conditions ◽  
Positive Definite ◽  
Necessary And Sufficient Conditions ◽  
Identity Matrix ◽  
Positive Definite Solution ◽  
The Matrix ◽  
Necessary And Sufficient ◽  
Real Matrices ◽  
Definite Solution

In this paper necessary and sufficient conditions for the matrix equation to have a positive definite solution are derived, where , is an identity matrix, are nonsingular real matrices, and is an odd positive integer. These conditions are used to propose some properties on the matrices , . Moreover, relations between the solution and the matrices are derived.

scholarly journals Karakteristik Matriks sebagai Daerah Asal Suatu Logaritma

2018 ◽  
Vol 6 (1) ◽  
pp. 60
Author(s):  
Era Dewi Kartika
Keyword(s):  
Positive Characteristic ◽  
Identity Matrix ◽  
Singular Matrix ◽  
Natural Logarithm ◽  
The Matrix ◽  
Characteristic Values ◽  
Commutative Matrix ◽  
Jordan Matrix ◽  
Logarithm Function ◽  
Square Matrix

Abstrak Rumus umum fungsi logaritma asli dengan daerah asal suatu matriks adalah ln⁡A=T S_((J_A ) ) {ln⁡〖(λ_1 I^((p_1 ) )+H^((p_1 ) ) ),ln⁡(λ_2 I^((p_2 ) )+H^((p_2 ) ) ),…,ln⁡(λ_u I^((p_u ) )+H^((p_u ) ) ) 〗 } 〖S_((J_A ) )〗^(-1) T^(-1) dengan T adalah matriks non-singular dimana A=TJ_A T^(-1), S_((J_A ) )adalah sebarang matriks yang komutatif dengan J_A, J_A adalah matriks Jordan dari matriks A, λ_i adalah nilai karakteristik dari pembagi elementer A, I adalah matriks identitas dan H^((p)) adalah matriks berukuran p×p yang mempunyai 1 sebagai anggota pada superdiagonal pertama dan 0 untuk lainnya. Karakteristik matriks A sebagai daerah asal suatu fungsi logaritma adalah matriks persegi yang non-singular dengan nilai-nilai karakteristik real positif Kata Kunci: matriks, daerah asal, logaritma asli Abstract The general formula of the natural logarithm function with domain of a matrix is ln⁡A=T S_((J_A ) ) {ln⁡〖(λ_1 I^((p_1 ) )+H^((p_1 ) ) ),ln⁡(λ_2 I^((p_2 ) )+H^((p_2 ) ) ),…,ln⁡(λ_u I^((p_u ) )+H^((p_u ) ) ) 〗 } 〖S_((J_A ) )〗^(-1) T^(-1) with T is the non-singular matrix which A=TJ_A T^(-1), S_((J_A ) ) is any commutative matrix with J_A, J_Ais the Jordan matrix of the matrix A, λ_i is the characteristic value of the elementary divider A, I is the identity matrix and H^((p)) is a square matrix which has 1 as a member of the first superdiagonal and 0 for other. The characteristic of matrix A as domain of a natural logarithm function is a non-singular square matrix with real positive characteristic values Keywords: matrix, domain, natural logarithm

scholarly journals Matrix Representation of Bi-Periodic Jacobsthal Sequence

2020 ◽  
pp. 1-12
Author(s):  
Sukran Uygun ◽  
Evans Owusu
Keyword(s):  
Generating Function ◽  
Initial Conditions ◽  
Matrix Representation ◽  
General Term ◽  
Identity Matrix ◽  
Summation Formulas ◽  
Matrix Sequence ◽  
Binet Formula ◽  
The Matrix ◽  
Generalized Matrix

In this paper, we bring into light the matrix representation of bi-periodic Jacobsthal sequence, which we shall call the bi-periodic Jacobsthal matrix sequence. We dene it as with initial conditions J0 = I identity matrix, . We obtained the nth general term of this new matrix sequence. By studying the properties of this new matrix sequence, the well-known Cassini or Simpson's formula was obtained. We then proceed to find its generating function as well as the Binet formula. Some new properties and two summation formulas for this new generalized matrix sequence were also given.

On the Solutions of the Matrix Equation f(X,X*)=g(X,X*)

1972 ◽  
Vol 15 (1) ◽  
pp. 45-49
Author(s):  
P. Basavappa
Keyword(s):  
Matrix Equation ◽  
Identity Matrix ◽  
Normal Matrices ◽  
Special Cases ◽  
The Matrix ◽  
Conjugate Transpose ◽  
Matrix Identities ◽  
Square Matrix

It is well known that the matrix identities XX*=I, X=X* and XX* = X*X, where X is a square matrix with complex elements, X* is the conjugate transpose of X and I is the identity matrix, characterize unitary, hermitian and normal matrices respectively. These identities are special cases of more general equations of the form (a)f(X, X*)=A and (b)f(Z, X*)=g(X, X*) where f(x, y) and g(x, y) are monomials of one of the following four forms: xyxy…xyxy, xyxy…xyx, yxyx…yxyx, and yxyx…yxy. In this paper all equations of the form (a) and (b) are solved completely. It may be noted a particular case of f(X, X*)=A, viz. XX'=A, where X is a real square matrix and X' is the transpose of X was solved by WeitzenbÖck [3]. The distinct equations given by (a) and (b) are enumerated and solved.

Log-Concavity and the Spectral Gap of Stochastic Matrices

1997 ◽  
Vol 6 (3) ◽  
pp. 371-379
Author(s):  
EKKEHARD WEINECK
Keyword(s):  
Characteristic Polynomial ◽  
Spectral Gap ◽  
Stochastic Matrix ◽  
Identity Matrix ◽  
Combinatorial Approach ◽  
Stochastic Matrices ◽  
Largest Eigenvalue ◽  
The Matrix ◽  
Second Largest Eigenvalue

Let Q be a stochastic matrix and I be the identity matrix. We show by a direct combinatorial approach that the coefficients of the characteristic polynomial of the matrix I−Q are log-concave. We use this fact to prove a new bound for the second-largest eigenvalue of Q.

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