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    AN EIGENVALUE APPROACH FOR ESTIMATING THE GENERALIZED CROSS VALIDATION FUNCTION FOR CORRELATED MATRICES

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    This work proposes a fast estimate for the generalized cross-validation function when the design matrix of an experiment has correlated columns. The eigenvalue structure of this matrix is used to derive probability bounds satisfied by an appropriate index of proximity, which provides a simple and accurate estimate for the numerator of the generalized cross-validation function. The denominator of the function is evaluated by an analytical formula. Several simulation tests performed in statistical models having correlated design matrix with intercept confirm the reliability of the proposed probabilistic bounds and indicate the applicability of the proposed estimate for these models

    Pairwise Completely Positive Matrices and Conjugate Local Diagonal Unitary Invariant Quantum States

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    A generalization of the set of completely positive matrices called pairwise completely positive (PCP) matrices is introduced. These are pairs of matrices that share a joint decomposition so that one of them is necessarily positive semidefinite while the other one is necessarily entrywise non-negative. Basic properties of these matrix pairs are explored and several testable necessary and sufficient conditions are developed to help determine whether or not a pair is PCP. A connection with quantum entanglement is established by showing that determining whether or not a pair of matrices is pairwise completely positive is equivalent to determining whether or not a certain type of quantum state, called a conjugate local diagonal unitary invariant state, is separable. Many of the most important quantum states in entanglement theory are of this type, including isotropic states, mixed Dicke states (up to partial transposition), and maximally correlated states. As a specific application of these results, a wide family of states that have absolutely positive partial transpose are shown to in fact be separable

    Tridiagonal pairs of type III with height one

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    Let K\mathbb{K} denote an algebraically closed field with characteristic 00. Let VV denote a vector space over K\mathbb{K} with finite positive dimension, and let A,AA,A^{*} denote a tridiagonal pair on VV of diameter dd. Let V0,,VdV_{0},\ldots,V_{d} denote a standard ordering of the eigenspaces of AA on VV, and let θ0,,θd\theta_{0},\ldots,\theta_{d} denote the corresponding eigenvalues of AA. It is assumed that d3d\geq3. Let ρi\rho_{i} denote the dimension of ViV_{i}. The sequence ρ0,ρ1,,ρd\rho_{0},\rho_{1},\ldots,\rho_{d} is called the {\it{shape}} of the tridiagonal pair. It is known that ρ0=1\rho_{0}=1 and there exists a unique integer h (0hd/2)h~(0\leq h\leq d/2) such that ρi13˘cρi\rho_{i-1}\u3c\rho_{i} for 1ih1\leq i\leq h, ρi1=ρi\rho_{i-1}=\rho_{i} for h3˘cidhh\u3c i\leq d-h, and ρi13˘eρi\rho_{i-1}\u3e\rho_{i} for dh3˘cidd-h\u3c i\leq d. The integer hh is known as the {\it{height}} of the tridiagonal pair. In this paper, it is showed that the shape of a tridiagonal pair of type III with height one is either 11, 22, 22, \ldots, 22, 11 or 11, 33, 33, 11. In each case, an interesting basis is found for VV and the actions of A,AA,A^{*} on this basis are described

    Diagonal Sums of Doubly Substochastic Matrices

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    Let Ωn\Omega_n denote the convex polytope of all n×nn\times n doubly stochastic matrices, and ωn\omega_{n} denote the convex polytope of all n×nn\times n doubly substochastic matrices. For a matrix AωnA\in\omega_n, define the sub-defect of AA to be the smallest integer kk such that there exists an (n+k)×(n+k)(n+k)\times(n+k) doubly stochastic matrix containing AA as a submatrix. Let ωn,k\omega_{n,k} denote the subset of ωn\omega_n which contains all doubly substochastic matrices with sub-defect kk. For π\pi a permutation of symmetric group of degree nn, the sequence of elements a1π(1),a2π(2),,anπ(n)a_{1\pi(1)},a_{2\pi(2)}, \ldots, a_{n\pi(n)} is called the diagonal of AA corresponding to π\pi. Let h(A)h(A) and l(A)l(A) denote the maximum and minimum diagonal sums of Aωn,kA\in \omega_{n,k}, respectively. In this paper, existing results of hh and ll functions are extended from Ωn\Omega_n to ωn,k.\omega_{n,k}. In addition, an analogue of Sylvesters law of the hh function on ωn,k\omega_{n,k} is proved

    Illegitimate Succession: Vestigial Discrimination in Wyoming’s Rules of Intestate Descent

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    Resolution Of Conjectures Related To Lights Out! And Cartesian Products

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    Lights Out!\ is a game played on a 5×55 \times 5 grid of lights, or more generally on a graph. Pressing lights on the grid allows the player to turn off neighboring lights. The goal of the game is to start with a given initial configuration of lit lights and reach a state where all lights are out. Two conjectures posed in a recently published paper about Lights Out!\ on Cartesian products of graphs are resolved

    A note on linear preservers of semipositive and minimally semipositive matrices

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    Semipositive matrices (matrices that map at least one nonnegative vector to a positive vector) and minimally semipositive matrices (semipositive matrices whose no column-deleted submatrix is semipositive) are well studied in matrix theory. In this short note, the structure of linear maps which preserve the set of all semipositive/minimally semipositive matrices is studied. An open problem is solved, and some ambiguities in the article [J. Dorsey, T. Gannon, N. Jacobson, C.R. Johnson and M. Turnansky. Linear preservers of semi-positive matrices. {\em Linear and Multilinear Algebra}, 64:1853--1862, 2016.] are clarified

    On Sign Pattern Matrices that Allow or Require Algebraic Positivity

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    A square matrix M with real entries is algebraically positive (AP) if there exists a real polynomial p such that all entries of the matrix p(M) are positive. A square sign pattern matrix S allows algebraic positivity if there is an algebraically positive matrix M whose sign pattern is S. On the other hand, S requires algebraic positivity if matrix M, having sign pattern S, is algebraically positive. Motivated by open problems raised in a work of Kirkland, Qiao, and Zhan (2016) on AP matrices, all nonequivalent irreducible 3 by 3 sign pattern matrices are listed and classify into three groups (i) those that require AP, (ii) those that allow but not require AP, or (iii) those that do not allow AP. A necessary condition for an irreducible n by n sign pattern to allow algebraic positivity is also provided

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