Gaussian ensemble

In random matrix theory, the Gaussian ensembles are specific probability distributions over self-adjoint matrices whose entries are independently sampled from the gaussian distribution. They are among the most-commonly studied matrix ensembles, fundamental to both mathematics and physics. The three main examples are the Gaussian orthogonal, unitary, and symplectic ensembles. These are classified by the Dyson index β, which takes values 1, 2, and 4 respectively, counting the number of real components per matrix element. The index can be extended to take any real positive value.
The gaussian ensembles are also called the Wigner ensembles, or the Hermite ensembles.

Definitions

Conventions

There are many conventions for defining the Gaussian ensembles. In this article, we specify exactly one of them.
In all definitions, the Gaussian ensemble have zero expectation.

: a positive real number. Called the Dyson index. The cases of are special.
: the side-length of a matrix. Always a positive integer.
: a matrix sampled from a Gaussian ensemble with size. The letter stands for "Wigner".
: the adjoint of a matrix. We assume when is sampled from a gaussian ensemble.
* If is real, then is its transpose.
* If is complex or quaternionic, then is its conjugate transpose.
: the eigenvalues of the matrix, which are all real, since the matrices are always assumed to be self-adjoint.
: the variance of on-diagonal matrix entries. We assume that for each, all on-diagonal matrix entries have the same variance. It is always defined as.
: the variance of off-diagonal matrix entries. We assume that for each, all off-diagonal matrix entries have the same variance. It is always defined as where.
* For a complex number,.
* For a quaternion,.
: the partition function.

Name	GOE	GUE	GSE	GβE
Full name	Gaussian orthogonal ensemble	Gaussian unitary ensemble	Gaussian symplectic ensemble	Gaussian beta ensemble
	1	2	4	β
	2	1	1/2	2/β
	1	1	1	1
matrix density

When referring to the main reference works, it is necessary to translate the formulas from them, since each convention leads to different constant scaling factors for the formulas.

Name
Wikipedia	2/β	1
	1/2	1/2
	1/β	1/2
	2/β	1
for β = 1, 2, 4	1/β	1/2
for β ≠ 1, 2, 4	1	β/2
	1	1
	1/N	1/N
	1	β/2

There are equivalent definitions for the GβE ensembles, given below.

By sampling

For all cases, the GβE ensemble is defined by how it is sampled:

Sample a gaussian matrix, such that all its entries are IID sampled from the corresponding standard normal distribution.
* If, then.
* If, then.
* If, then.
Let.
By density

For all cases, the GβE ensemble is defined with density function where the partition function is.
The Gaussian orthogonal ensemble GOE is defined as the probability distribution over symmetric matrices with density functionwhere the partition function is.
Explicitly, since there are only degrees of freedom, the parameterization is as follows:where we pick the upper diagonal entries as the degrees of freedom.
The Gaussian unitary ensemble GUE is defined as the probability distribution over Hermitian matrices with density functionwhere the partition function is.
Explicitly, since there are only degrees of freedom, the parameterization is as follows:
where we pick the upper diagonal entries as the degrees of freedom.
The Gaussian symplectic ensemble GSE is defined as the probability distribution over self‑adjoint quaternionic matrices with density functionwhere the partition function is.
Explicitly, since there are only degrees of freedom, the parameterization is as follows:where we write and pick the upper diagonal entries as the degrees of freedom.

By invariance

For all cases, the GβE ensemble is uniquely characterized by its symmetries, or invariance under appropriate transformations.
For GOE, consider a probability distribution over symmetric matrices satisfying the following properties:

Invariance under orthogonal transformation: For any fixed orthogonal matrix, let be a random sample from the distribution. Then has the same distribution as.
Independence: The entries are independently distributed.

For GUE, consider a probability distribution over Hermitian matrices satisfying the following properties:

Invariance under unitary transformation: For any fixed unitary matrix, let be a random sample from the distribution. Then has the same distribution as.
Independence: The entries are independently distributed.

For GSE, consider a probability distribution over self-adjoint quaternionic matrices satisfying the following properties:

Invariance under symplectic transformation: For any fixed symplectic matrix, let be a random sample from the distribution. Then has the same distribution as.
Independence: The entries are independently distributed.

In all 3 cases, these conditions force the distribution to have the form, where and. Thus, with the further specification of, we recover the GOE, GUE, GSE. Notably, if mere invariance is demanded, then any spectral distribution can be produced by multiplying with a function of form.
More succinctly stated, each of GOE, GUE, GSE is uniquely specified by invariance, independence, the mean, and the variance.

By spectral distribution

For all cases, the GβE ensemble is defined as the ensemble obtained by, where

is a diagonal real matrix with its entries sampled according to the spectral density, defined below;
is an orthogonal/unitary/symplectic matrix sampled uniformly, that is, from the normalized Haar measure of the orthogonal/unitary/symplectic group.

In this way, the GβE ensemble may be defined after the spectral density is defined first, so that any method to motivate the spectral density then motivates the GβE ensemble, and vice versa.

By maximal entropy

For all cases, the GβE ensemble is uniquely characterized as the absolutely continuous probability distribution over real/complex/quaternionic symmetric/orthogonal/symplectic matrices that maximizes entropy, under the constraint of.

Spectral density

For eigenvalues the joint density of GβE iswhere is the Vandermonde determinant, and the partition function is explicitly evaluated as a Selberg integral:where is the Euler Gamma function. The expression is particularly simple when, where we have a superfactorial:

Determinantal point process

Define functions, where is the probabilist's Hermite polynomial. These are the wavefunction states of the quantum harmonic oscillator.
The spectrum of GUE is a determinantal point process with kernel, and by the Christoffel–Darboux formula,Using the confluent form of Christoffel–Darboux and the three-term recurrence of Hermite polynomials, the spectral density of GUE for finite values of :The spectral distribution of can also be written as a quaternionic determinantal point process involving skew-orthogonal polynomials.

Tridiagonalization

For all cases, given a sampled matrix from the GβE ensemble, we can perform a Householder transformation tridiagonalization on it to obtain a tridiagonal matrix, which has the same distribution aswhere each is gaussian-distributed, and each is chi-distributed, and all are independent. The case was first noted in 1984, and the general case was noted in 2002. Like how the Laplac differential operator can be discretized to the Laplacian matrix, this tridiagonal form of the gaussian ensemble allows a reinterpretation of the gaussian ensembles as an ensemble over not matrices, but over differential operators, specifically, a "stochastic Airy operator". This leads more generally to the study of random matrices as stochastic operators.
Computationally, this allows efficient sampling of eigenvalues, from on the full matrix, to just on the tridiagonal matrix. If one only requires a histogram of the eigenvalues with bins, the time can be further decreased to, by using the Sturm sequences. Theoretically, this definition allows extension to all cases, leading to the gaussian beta ensembles, and "anti-symmetric" gaussian beta ensembles.
Relatedly, let be a matrix, with all entries IID sampled from the corresponding standard normal distribution – for example, if, then. Then applying repeated Housholder transform on only the left side of a results in, where each is a Householder matrix, and is an upper triangular matrix with independent entries, such that each for, and each for.

Global law

The Wigner semicircle law states that the empirical eigenvalue distribution of converges in distribution to the Wigner semicircle distribution with radius 2. That is, the distribution on with probability density function
The requirement that the matrix ensemble to be a gaussian ensemble is too strong for the Wigner semicircle law. Indeed, the theorem applies generally for much more generic matrix ensembles.

As Coulomb gas

The joint density can be written as a Gibbs measure:with the energy function . This can be interpreted physically as a Boltzmann distribution of a physical system consisting of identical unit electric charges constrained to move on the real line, repelling each other via the two-dimensional Coulomb potential, while being attracted to the origin via a quadratic potential. This is the Coulomb gas model for the eigenvalues.
In the macroscopic limit, one rescales and defines the empirical measure, obtaining, where the mean-field functional yields the leading order term in, termed the Coulomb gas free energy. The Coulomb gas free energy is minimized by the Wigner semicircle law, which gives the limiting eigenvalue density.
Alternatively, suppose that there exists a, such that the quadratic electric potential can be recreated viaThen, imposing a fixed background negative electric charge of density exactly cancels out the electric repulsion between the freely moving positive charges. Such a function does exist:, which can be found by solving an integral equation. This indicates that the Wigner semicircle distribution is the equilibrium distribution.
Gaussian fluctuations about obtained by expanding to second order produce the sine kernel in the bulk and the Airy kernel at the soft edge after proper rescaling.