Eigenvalue statistics for beta-ensembles

Abstract

Random matrix theory is a maturing discipline with decades of research in multiple fields now beginning to converge. Experience has shown that many exact formulas are available for certain matrices with real, complex, or quaternion entries. In random matrix jargon, these are the cases β = 1, 2 and 4 respectively. This thesis explores the general P > 0 case mathematically and with symbolic software. We focus on generalizations of the Hermite distributions originating in physics (the "Gaussian" ensembles) and the Laguerre distributions of statistics (the "Wishart" matrices). One of our main contributions is the construction of tridiagonal matrix models for the general (β > 0) 0 β-Hermite and (β > 0, a > β(m - 1)/2) β-Laguerre ensembles of parameter a and size m, and investigate applications of these new ensembles, particularly in the areas of eigenvalue statistics. The new models are symmetric tridiagonal, and with entries from real distributions, regardless of the value of β. The entry distributions are either normal or X, so "classical", and the independence pattern is maximal, in the sense that the only constraints arise from the symmetric/semi-definite condition. The β-ensemble distributions have been studied for the particular 1, 2, 4 values of p as joint eigenvalue densities for full random matrix ensembles (Gaussian, or Hermite, and Wishart, or Laguerre) with real, complex, and quaternion entries (for references, see [66] and [70]). In addition, general -ensembles were considered and studied as theoretical distributions ([8, 51, 50, 55, 56]), with applications in lattice gas theory and statistical mechanics (the parameter being interpreted as an arbitrary inverse temperature of a Coulomb gas with logarithmic potential).

(cont.) Certain eigenvalue statistics over these general β-ensembles, namely those expressible in terms of integrals of symmetric polynomials with corresponding Hermite or Laguerre weights, can be computed in terms of multivariate orthogonal polynomials (Hermite or Laguerre). We have written a Maple Library (MOPs: Multivariate Orthogonal Polynomials symbolically) which implements some new and some known algorithms for computing the Jack, Hermite, Laguerre, and Jacobi multivariate polynomials for arbitrary. This library can be used as a tool for conjecture-formulation and testing, for statistical computations, or simply for getting acquainted with the mathematical concepts. Some of the figures in this thesis have been obtained using MOPs. Using the new β-ensemble models, we have been able to provide a unified perspective of the previously isolated 1, 2, and 4 cases, and prove generalizations for some of the known eigenvalue statistics to arbitrary β. We have rediscovered (in the Hermite case) a strong version of the Wigner Law (semi-circle), and proved (in the Laguerre case) a strong version of the similar law (generalized quarter-circle). We have obtained first-order perturbation theory for the P large case, and we have reason to believe that the tridiagonal models in the large n (ensemble size) limit will also provide a link between the largest eigenvalue distributions for both Hermite and Laguerre for arbitrary P (for β = 1, 2, this link was proved to exist by Johannson [52] and Johnstone [53]) ...