Unitary matrix digraphs and minimum semidefinite rank

Bounds for minimum semidefinite rank from superpositions and cutsets

Matrices attaining the minimum semidefinite rank of a chordal graph

Decompositions of minimum rank matrices

The (0, 1)-matrix A of order n is a tournament matrix provided A + A + I = J, where I is the identity matrix, and J = Jn is the all 1’s matrix of order n. It was shown by de Caen and Michael that the rank of a tournament matrix A of order n over a field of characteristic p satisfies rankp(A) ≥ (n − 1)/2 with equality if and only if n is odd and AA = O. This article shows that the rank of a tournament matrix A of even order n over a field of characteristic p satisfies rankp(A) ≥ n/2 with equality if and only if after simultaneous row and column permutations AA = [ ±Jm O O O ] , for a suitable integer m. The results and constructions for even order tournament matrices are related to and shed light on tournament matrices of odd order with minimum rank.

Minimum rank and maximum eigenvalue multiplicity of symmetric tree sign patterns

We prove that the ordered subgraph number of a connected graph that has no duplicate vertices is at most three if and only if the complement does not contain a cycle on four vertices. The duality between zero forcing and ordered subgraphs then provides a complementary characterization for positive semidefinite zero forcing. We also provide some necessary conditions for when the minimum semidefinite rank can be computed using tree size.

We construct a bipartite generalization of Alon and Szegedy's nearly orthogonal vectors, thereby obtaining strong bounds for several extremal problems involving the Lovász theta function, vector chromatic number, minimum semidefinite rank, nonnegative rank, and extension complexity of polytopes. In particular, we answer a question from our previous work together with Letzter and Sudakov, while also addressing a question of Hrubeš and of Kwan, Sauermann, and Zhao. Along the way, we derive a couple of general lower bounds for the vector chromatic number which may be of independent interest.

Matrix completion problems studies the partial matrices, that is, a rectangular matrix some of whose entries are specified, and the remainder entries are free variables of some indicated set. By a completion of a partial matrix we consider a specification of the free variables obtaining a conventional matrix. The basic type of these problems try to obtain conditions for the existence of a completion for a given partial matrix in a class of interest.In this work, we study the minimal rank completion problem when the partial matrix P has the specified entries equal to zero, and the remaining entries are positive real numbers. By a graph theoretic approach we introduce some approximations to the question. Furthermore, we obtain completions for some classes of positive pattern matrices with minimal rank.

The two papers in this issue have to do with matrices and sparsity—but from different points of view. Sparsity, in the first paper, means many zero elements in the matrix, while in the second paper it refers to many zero singular values, i.e., low rank. The context of the first paper, “On the Block Triangular Form of Symmetric Matrices” by Iain Duff and Bora Uçar, is the solution of linear systems of equations $Ax = b$ whose coefficient matrix A is sparse, i.e., has many zero elements. A direct method, such as Gaussian elimination, becomes more efficient if one can first permute the rows and columns of A into block triangular form. The classical method for permuting a matrix to block triangular form is due to Dulmage and Mendelsohn and dates back to 1963. The idea is to represent the matrix A as a bipartite graph whose nodes are columns and rows of A, and whose edges correspond to nonzero elements of A, and then to determine a matching of maximum cardinality in this graph. That means determining a maximal number of nonzero elements no two of which belong to the same row or column. Duff and Uçar analyze the block triangular form for a particular class of square matrices A: These matrices are structurally symmetric, i.e., their zero/nonzero structure is symmetric; and they can be structurally rank deficient, i.e., any rank deficiency is evident from the zero/nonzero structure of A. This paper illustrates nicely how graph theory can contribute to improving the efficiency of sparse matrix methods. The paper should appeal to those who need to solve large sparse linear systems, as well as those interested in graph theory. The second paper, “Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization” by Benjamin Recht, Maryam Fazel, and Pablo Parrilo, has applications to model reduction and system identification, machine learning, and image compression, just to name a few. Given a set of affine constraints for a matrix, the problem is to find a matrix of minimum rank that satisfies these constraints. The authors attack this hard nonconvex optimization problem by replacing it with a convex approximation: Instead of minimizing the rank, they minimize the sum of the singular values, the so-called nuclear norm. Nuclear norm minimization thus extends the compressed sensing framework from finding sparse vectors via $\ell_1$ minimization to finding low-rank matrices via $\ell_1$ minimization of the (vector of) singular values. The purpose of this paper is to justify mathematically why nuclear norm minimization does so well in practice. In addition to extending the restricted isometry property to matrices, the authors also discuss algorithms, computational performance, and numerical issues. This is a fascinating and well-written paper that combines results from compressed sensing, matrix theory, optimization, and probability.

Sparse matrix decompositions and graph characterizations

Given an undirected graph G=(V,E) with node set V=[1,n], a subset $S\subseteq V$, and a rational vector $a\in {\rm\bf Q}^{S\cup E}$, the positive semidefinite matrix completion problem consists of determining whether there exists a real symmetric n × n positive semidefinite matrix X=(xij) satisfying xii=ai ($i\in S$) and xij=aij ($ij\in E$). Similarly, the Euclidean distance matrix completion problem asks for the existence of a Euclidean distance matrix completing a partially defined given matrix. It is not known whether these problems belong to NP. We show here that they can be solved in polynomial time when restricted to the graphs having a fixed minimum fill-in, the minimum fill-in of graph G being the minimum number of edges needed to be added to G in order to obtain a chordal graph. A simple combinatorial algorithm permits us to construct a completion in polynomial time in the chordal case. We also show that the completion problem is polynomially solvable for a class of graphs including wheels of fixed length (assuming all diagonal entries are specified). The running time of our algorithms is polynomially bounded in terms of n and the bitlength of the input a. We also observe that the matrix completion problem can be solved in polynomial time in the real number model for the class of graphs containing no homeomorph K4.

This paper deals with embedded index coding problem (EICP), introduced by A. Porter and M. Wootters, which is a decentralized communication problem among users with side information. An alternate definition of the parameter minrank of an EICP, which has reduced computational complexity compared to the existing definition, is presented. A graphical representation for an EICP is given using directed bipartite graphs, called bipartite problem graph, and the side information alone is represented using an undirected bipartite graph called the side information bipartite graph. Inspired by the well-studied single unicast index coding problem, graphical structures, similar to cycles and cliques, are identified in the side information bipartite graph of a single unicast embedded index coding problem (SUEICP). Transmission schemes based on these graphical structures, called tree cover scheme and bi-clique cover scheme are also presented. For a class of SUEICPs, scalar linear optimal solution is given using bi-clique cover. A relation between connectedness of the side information bipartite graph and the number of transmissions required in a scalar linear solution of an EICP is established.

Let F be a field, let G be an undirected graph on n vertices, and let S(F, G)be the set of all F-valued symmetric n × n matrices whose nonzero off-diagonal entries occur in exactly the positions corresponding to the edges of G. The minimum rank of G over F is defined to be mr(F, G) = min{rank A | A ∈ S(F, G)}. The problem of finding the minimum rank (maximum nullity) of edge subdivisions of a given graph G is investigated. Is is shown that if an edge is adjacent to a vertex of degree 1 or 2, its maximum nullity is unchanged upon subdividing the edge. This enables us to reduce the problem of finding the minimum rank of any graph obtained from G by subdividing edges to finding the minimum rank of those graphs obtained from G by subdividing each edge at most once. The graph obtained by subdividing each edge of G once is called its subdivision graph and is denoted by ❛G. It is shown that its maximum nullity is an upper bound for the maximum nullity of any graph obtained from G by subdividing edges. It is also shown that the minimum rank of ❛G often depends only upon the number of vertices of G. In conclusion, some illustrative examples and open questions are presented.

On the difference between the maximum multiplicity and path cover number for tree-like graphs

Extreme rays for cones of Hermitian matrix functions that are non-negative on the positive semi-definite matrices