Random matrix package for Julia.

This extends the Distributions package to provide methods for working with matrix-valued random variables, a.k.a. random matrices. State of the art methods for computing random matrix samples and their associated distributions are provided.

The names of the various ensembles can vary widely across disciplines. Where possible, synonyms are listed.

Additional functionality is provided when these optional packages are installed:

• Symbolic manipulation of Haar matrices with GSL.jl
• Invariant ensembles with ApproxFun.jl

Much of classical random matrix theory has focused on matrices with matrix elements comprised of independently and identically distributed (iid) real, complex or quaternionic Gaussians. (Traditionally, these are associated with a parameter beta tracking the number of independent real random variables per matrix element, i.e. beta=1,2,4 respectively. This is also referred to as the Dyson 3-fold way.) Methods are provided for calculating random variates (samples) and various properties of these random matrices.

The hierarchy of dense matrices provided are

• Ginibre ensemble - all matrix elements are iid with no global symmetry
• Hermite ensemble - one global symmetry
  • Gaussian orthogonal ensemble (GOE, beta=1) - real and symmetric
  • Gaussian unitary ensemble (GUE, beta=2) - complex and Hermitian
  • Gaussian symplectic ensemble (GSE, beta=4) - quaternionic and self-dual
• Circular ensemble - uniformly distributed with |det|=1
  • Circular orthogonal ensemble (GOE, beta=1)
  • Circular unitary ensemble (GUE, beta=2)
  • Circular symplectic ensemble (GSE, beta=4)
• Laguerre matrices = white Wishart matrices
• Jacobi matrices = MANOVA matrices
• Unitary invariant ensembles

Unless otherwise specified, beta=1,2,4 are supported. For the symplectic matrices beta=4, the 2x2 outer block-diagonal complex representation USp(2N) is used.

Given eigenvalues lambda and the beta parameter of the random matrix distribution:

• VandermondeDeterminant(lambda, beta) computes the Vandermonde determinant
• HermiteJPDF(lambda, beta) computes the jpdf for the Hermite ensemble
• LaguerreJPDF(lambda, n, beta) computes the jpdf for the Laguerre(n) ensemble
• JacobiJPDF(lambda, n1, n2, beta) computes the jpdf for the Jacobi(n1, n2) ensemble

Constructs samples of random matrices corresponding to the classical Gaussian Hermite, Laguerre(m) and Jacobi(m1, m2) ensembles.

• GaussianHermiteMatrix(n, beta), GaussianLaguerreMatrix(n, m, beta), GaussianJacobiMatrix(n, m1, m2, beta) each construct a sample dense nxn matrix for the corresponding matrix ensemble with beta=1,2,4

• GaussianHermiteTridiagonalMatrix(n, beta), GaussianLaguerreTridiagonalMatrix(n, m, beta), GaussianJacobiSparseMatrix(n, m1, m2, beta) each construct a sparse nxn matrix for the corresponding matrix ensemble for arbitrary positive finite beta. GaussianHermiteTridiagonalMatrix(n, Inf) is also allowed. These sampled matrices have the same eigenvalues as above but are much faster to diagonalize oweing to their sparsity. They also extend Dyson's threefold way to arbitrary beta.

• GaussianHermiteSamples(n, beta), GaussianLaguerreSamples(n, m, beta), GaussianJacobiSamples(n, m1, m2, beta) return a set of n eigenvalues from the sparse random matrix samples

• HaarMatrix(n, beta) Generates a random matrix from the beta-circular ensemble.

• HaarMatrix(n, beta, correction) provides fine-grained control of what kind of correction is applied to the raw QR decomposition. By default, correction=1 (Edelman's correction) is used. Other valid values are 0 (no correction) and 2 (Mezzadri's correction).

• NeedsPiecewiseCorrection() implements a simple test to see if a correction is necessary.

• InvariantEnsemble(str,n) Generates a unitary invariant ensemble, where str determines the potential of the ensemble, see below. Only available if ApproxFun package is installed.

The parameters m, m1, m2 refer to the number to independent "data" degrees of freedom. For the dense samples these must be Integers but can be Reals for the rest.

Allows for manipulations of formal power series (fps) and formal Laurent series (fLs), which come in handy for the computation of free cumulants.

• FormalPowerSeries: power series with coefficients allowed only for non-negative integer powers
• FormalLaurentSeries: power series with coefficients allowed for all integer powers

• basic arithmetic operations ==, +, -, ^
• * computes the Cauchy product (discrete convolution)
• .* computes the Hadamard product (elementwise multiplication)
• compose(P,Q) computes the series composition P.Q
• derivative computes the series derivative
• reciprocal computes the series reciprocal

• trim(P) removes extraneous zeroes in the internal representation of P
• isalmostunit(P) determines if P is an almost unit series
• isconstant(P) determines if P is a constant series
• isnonunit(P) determines if P is a non-unit series
• isunit(P) determines if P is a unit series
• MatrixForm(P) returns a matrix representation of P as an upper triangular Toeplitz matrix
• tovector returns the series coefficients

Famous distributions in random matrix theory

• Semicircle provides the semicircle distribution
• TracyWidom computes the Tracy-Widom density distribution by brute-force integration of the Painlevé II equation

• hist_eig computes the histogram of eigenvalues of a matrix using the method of Sturm sequences. This is recommended for SymTridiagonal matrices as it is significantly faster than hist(eigvals()) This is also implemented for dense matrices, but it is pretty slow and not really practical.

Provides finite-dimensional matrix representations of stochastic operators.

In the following, dt is the time interval being discretized over and t_end is the final time.

• BrownianProcess(dt, t_end) generates a vector corresponding to a Brownian random walk starting from time t=0 and position x=0
• WhiteNoiceProcess(dt, t_end) generates a vector corresponding to white noise.
• StochasticAiryProcess(dt, t_end, beta) generates the largest eigenvalue corresponding to the stochastic Airy process with real positive beta. This is known to be distributed in the t_end -> Inf limit to the beta-Tracy-Widom law.

InvariantEnsemble(str,n) supports n x n unitary invariant ensemble with distribution

exp(- Tr Q(M)) dM

str specifies an ensemble with precomputed recurrence coefficients. The currently include ensembles are

• Quartic: Q(M) = n M^4
• Eight: Q(M) = n M^8
• HODecay: Q(M) = n (M^4/20 - 4/15M^3 +M^2/5 + 8/5M)
• CoshUnscaled: Q(M) = cosh(M)
• QuarticUnscaled: Q(M) = M^4
• EightUnscaled: Q(M) = M^8

• James Albrecht, Cy Chan, and Alan Edelman, "Sturm Sequences and Random Eigenvalue Distributions", Foundations of Computational Mathematics, vol. 9 iss. 4 (2009), pp 461-483. [pdf] [doi]

• Ioana Dumitriu and Alan Edelman, "Matrix Models for Beta Ensembles", Journal of Mathematical Physics, vol. 43 no. 11 (2002), pp. 5830-5547 [[doi]](http://dx.doi.org/doi: 10.1063/1.1507823) arXiv:math-ph/0206043

• Alan Edelman, Per-Olof Persson and Brian D Sutton, "The fourfold way", Journal of Mathematical Physics, submitted (2013). [pdf]

• Alan Edelman and Brian D. Sutton, "The beta-Jacobi matrix model, the CS decomposition, and generalized singular value problems", Foundations of Computational Mathematics, vol. 8 iss. 2 (2008), pp 259-285. [pdf] [doi]

• Peter Henrici, Applied and Computational Complex Analysis, Volume I: Power Series---Integration---Conformal Mapping---Location of Zeros, Wiley-Interscience: New York, 1974 [worldcat]

• Frank Mezzadri, "How to generate random matrices from the classical compact groups", Notices of the AMS, vol. 54 (2007), pp592-604 [arXiv]