Spectral arbitrariness for trees fails spectacularly

If $G$ is a graph and $\mathbf{m}$ is an ordered multiplicity list which is realizable by at least one symmetric matrix with graph $G$, what can we say about the eigenvalues of all such realizing matrices for $\mathbf{m}$? It has sometimes been tempting to expect, especially in the case that $G$ is a tree, that any spacing of the multiple eigenvalues should be realizable. In 2004, however, F. Barioli and S. Fallat produced the first counterexample: a tree on 16 vertices and an ordered multiplicity list for which every realizing set of eigenvalues obeys a nontrivial linear constraint. We extend this by giving an infinite family of trees and ordered multiplicity lists whose sets of realizing eigenvalues are very highly constrained, with at most 5 degrees of freedom, regardless of the size of the tree in this family. In particular, we give the first examples of multiplicity lists for a tree which impose nontrivial nonlinear eigenvalue constraints and produce an ordered multiplicity list which is achieved by a unique set of eigenvalues, up to shifting and scaling.Comment: 45 page

Ordered multiplicity lists for eigenvalues of symmetric matrices whose graph is a linear tree

a b s t r a c t We consider the class of trees for which all vertices of degree at least 3 lie on a single induced path of the tree. For such trees, a new superposition principle is proposed to generate all possible ordered multiplicity lists for the eigenvalues of symmetric (Hermitian) matrices whose graph is such a tree. It is shown that no multiplicity lists other than these can occur and that for two subclasses all such lists do occur. Important contrasts with trees outside the class are given, and it is shown that several prior conjectures about multiplicity lists, including the Degree Conjecture, follow from our superposition principle

Spectral graph theory and the inverse eigenvalue problem of a graph

Spectral Graph Theory is the study of the spectra of certain matrices defined from a given graph, including the adjacency matrix, the Laplacian matrix and other related matrices. Graph spectra have been studied extensively for more than fifty years. In the last fifteen years, interest has developed in the study of generalized Laplacian matrices of a graph, that is, real symmetric matrices with negative off-diagonal entries in the positions described by the edges of the graph ( and zero in every other off-diagonal position).The set of all real symmetric matrices having nonzero off-diagonal entries exactly where the graph G has edges is denoted by S( G). Given a graph G, the problem of characterizing the possible spectra of B, such that B. S( G), has been referred to as the Inverse Eigenvalue Problem of a Graph. In the last fifteen years a number of papers on this problem have appeared, primarily concerning trees.The adjacency matrix and Laplacian matrix of G and their normalized forms are all in S( G). Recent work on generalized Laplacians and Colin de Verdiere matrices is bringing the two areas closer together. This paper surveys results in Spectral Graph Theory and the Inverse Eigenvalue Problem of a Graph, examines the connections between these problems, and presents some new results on construction of a matrix of minimum rank for a given graph having a special form such as a 0,1-matrix or a generalized Laplacian

Linear and Nonlinear Trees: Multiplicity Lists of Symmetric Matrices

Let A be a real symmetric matrix whose graph is a tree, T. If T is a linear tree (meaning all vertices with degree 3 or larger lie on the same induced path), then we can use a ”Linear Superposition Principle” to determine all possible multiplicities of eigenvalues of A. If T is a nonlinear tree, we must use other ad hoc methods. I utilize these methods to compute all possible multiplicity lists of trees on 12 vertices, and augment an existing multiplicities database. This database allows us to examine of the effects that the structure of tree can have on a multiplicity list. Then, I investigate the enumeration of linear and nonlinear trees, and examine the ratio of nonlinear trees to total trees

Diagonalizable matrices whose graph is a tree: The minimum number of distinct eigenvalues and the feasibility of eigenvalue assignments

UID/MAT/00297/2019Considered are combinatorially symmetric matrices, whose graph is a given tree, in view of the fact recent analysis shows that the geometric multiplicity theory for the eigenvalues of such matrices closely parallels that for real symmetric (and complex Hermitian) matrices. In contrast to the real symmetric case, it is shown that (a) the smallest example (13 vertices) of a tree and multiplicity list (3, 3, 3, 1, 1, 1, 1) meeting standard necessary conditions that has no real symmetric realizations does have a diagonalizable realization and for arbitrary prescribed (real and multiple) eigenvalues, and (b) that all trees with diameter < 8 are geometrically di-minimal (i.e., have diagonalizable realizations with as few of distinct eigenvalues as the diameter). This re-raises natural questions about multiplicity lists that proved subtly false in the real symmetric case. What is their status in the geometric multiplicity list case?publishersversionpublishe