The asymptotic number of (0,1)-matrices with zero permanent

It is shown that Z_n/n·2^{n2 - n + 1}→1, where Z_n is the number of n × n (0,1)-matrices with zero permanent.     

Universal tilings and universal (0,1)-matrices

A periodic regular tiling of the plane by black and white squares is k-universal if it contains all possible k × k blocks of black and white tiles. There is a 4 × 4 periodic tiling that is 2-universal; this paper looks for the smallest 3-universal tiling and obtains a 64 × 32 periodic tiling that is 3-universal.Related to this is the following: a (0,1)-matrix is k-universal if every possible k × k (0,1)-matrix occurs as a submatrix. It is proved that, for k even, there is a k2^k/2 matrix that is k-universal and, for k odd, there is a (3k + 1)2^(k−3)/2 by (3k − 1)2^k−3/2 matrix that is k-universal.     

The 1-width of (0,1)-matrices having constant row sum 3

In this note we establish upper bounds for the 1-width of an m × n matrix of 0's and 1's having three 1's in every row and having a constant number, c, of 1's in every column. When c = 3, this upper bound is $\text{n}\text{2}$ and when c = 4 this estimate is $\text{5n}\text{9}$. In these cases the upper bound is best possible, in the sense that for every possible size there exist matrices with this maximal 1-width. The technique of proof is also used to improve the known bound for the 1-width of (0, 1)-matrices with constant line sum 4.     

Reconstructing (0,1)-matrices from projections using integer programming

We study the problem of reconstructing (0,1)-matrices based on projections along a small number of directions. This discrete inverse problem is generally hard to solve for more than 3 projection directions. Building on previous work by the authors, we give a problem formulation with the objective of finding matrices with the maximal number of neighboring ones. A solution approach based on variable splitting and the use of subgradient optimization is given. Further, computational results are given for some structured instances. Optimal solutions are found for instances with up to 10,000 binary variables.     

行和列和为常量的(0,1)-矩阵的计数

Let fs,t(m,n) be the number of (0,1) - matrices of size m x n such that each row has exactly s ones and each column has exactly t ones (sm = nt). How to determine fs,t(m,n)? As R. P. Stanley has observed (Enumerative CombinatoricsⅠ(1997), Example 1.1.3), the determination of fs,t(m, n) is an unsolved problem, except for very small s, t. In this paper the closed formulas for f2,2(n,n), f3,2(m,n), f4,2(m,n) are given. And recursion formulas and generating functions are discussed.     

(0,1)-矩阵的积和式的图表示及其相关性质

将(0，1)．矩阵的积和式的记数问题转化为它的伴随图或伴随有向图上相关元素的记数问题，能使复杂的计数问题变得相对直观化和简单化．本文给出了(0，1)-矩阵的积和式的图论表达式，并以该表达式为基础，主要解决了2．正则图类的邻接矩阵的最大积和式的记数问题以及它的反问题，即确定了零积和式临界图的极大边数及其图类．     

On the inverse M-matrix problem for (0,1)-matrices

A graph-theoretic approach is used to characterize (0,1)-matrices which are inverses of M-matrices. Our main results show that a (0,1)-matrix is an inverse of an M-matrix if and only if its graph induces a partial order on its set of vertices and does not contain a certain specific subgraph.     

The numerical range of elementary operators

For n-tuplesA=(A ₁,...,A _n ) andB=(B ₁,...,B _n ) of operators on a Hilbert spaceH, letR _A,B denote the operator onL(H) defined by . In this paper we prove that

Properties of (0, 1)-matrices without certain configurations

We generalize results of Ryser on (0, 1)-matrices without triangles, 3 × 3 submatrices with row and column sums 2. The extremal case of matrices without triangles was previously studied by the author. Let the row intersection of row i and row j (i ≠ j) of some matrix, when regarded as a vector, have a 1 in a given column if both row i and row j do not 0 otherwise. For matrices satisfying some conditions on forbidden configurations and column sums ? 2, we find that the number of linearly independent row intersections is equal to the number of distinct columns. The extremal matrices with m rows and $\text{(}\text{m}\text{2}\text{)}$ distinct columns have a unique SDR of pairs of rows with 1's. A triangle bordered with a column of 0's and its (0, 1)-complement are also considered as forbidden configurations. Similar results are obtained and the extremal matrices are closely related to the extremal matrices without triangles.     

The numerical range of a nonnegative matrix

We offer an almost self-contained development of Perron–Frobenius type results for the numerical range of an (irreducible) nonnegative matrix, rederiving and completing the previous work of Issos, Nylen and Tam, and Tam and Yang on this topic. We solve the open problem of characterizing nonnegative matrices whose numerical ranges are regular convex polygons with center at the origin. Some related results are obtained and some open problems are also posed.     

The block numerical range of matrix polynomials

Block numerical ranges of matrix polynomials, especially the quadratic numerical range, are considered. The main results concern spectral inclusion, boundedness of the block numerical range, an estimate of the resolvent in terms of the quadratic numerical range, geometrical properties of the quadratic numerical range, and inclusion between block numerical ranges of the matrix polynomials for refined block decompositions. As an application, we connect the quadratic numerical range with the localization of the spectrum of matrix polynomials.     

The numerical range of a tridiagonal operator

Let r be a real number and A a tridiagonal operator defined by Aej=ej−1+rjej+1, j=1,2,…, where {e₁,e₂,…} is the standard orthonormal basis for ?²(N). Such tridiagonal operators arise in Rogers-Ramanujan identities. In this paper, we study the numerical ranges of these tridiagonal operators and finite-dimensional tridiagonal matrices. In particular, when r=−1, the numerical range of the finite-dimensional tridiagonal matrix is the convex hull of two explicit ellipses. Applying the result, we obtain that the numerical range of the tridiagonal operator is the square

     

The numerical evaluation of certain multivariate normal integrals

We show that a multivariate normal integral with tridiagonal covariance matrix can be computed efficiently using iterated integration.     

The higher rank numerical range is convex

In this short note we provide the final step in showing that the higher rank numerical range is convex. The previous steps appear in the paper “Geometry of Higher-Rank Numerical Ranges” by Choi, M.-D., Giesinger, M., Holbrook, J.A. and Kribs, D.W.     

The higher rank numerical range of nonnegative matrices

In this article the rank-k numerical range ∧ _k (A) of an entrywise nonnegative matrix A is investigated. Extending the notion of elements of maximum modulus in ∧ _k (A), we examine their location on the complex plane. Further, an application of this theory to ∧ _k (L(λ)) of a Perron polynomial L(λ) is elaborated via its companion matrix C _L .