The eigenvalue spectrum of a large symmetric random matrix

TLDR: In this article, a Gaussian probability density function with the same mean and variance is used to calculate the eigenvalue spectrum of a large symmetric square matrix, each of whose upper triangular elements is described by the Gaussian distribution.

Abstract: A new and straightforward method is presented for calculating the eigenvalue spectrum of a large symmetric square matrix each of whose upper triangular elements is described by a Gaussian probability density function with the same mean and variance. Using the n to 0 method, the authors derive the semicircular eigenvalue spectrum when the mean of each element is zero and show that there is a critical finite mean value above which a single eigenvalue splits off from the semicircular continuum of eigenvalues.