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# RandomMatrices.jl

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

# Gaussian matrix ensembles

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 (COE, `beta=1`)
• Circular unitary ensemble (CUE, `beta=2`)
• Circular symplectic ensemble (CSE, `beta=4`)
• Laguerre matrices = white Wishart matrices
• Jacobi matrices = MANOVA matrices

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.

## Joint probability density functions (jpdfs)

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

## Matrix samples

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 `n`x`n` 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 `n`x`n` 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.

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

# Formal power series

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

## Types

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

## FormalPowerSeries methods

### Elementary operations

• 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

### Utility methods

• `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

# Densities

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

# Utility functions

• `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.

# Stochastic processes

Julia iterators for stochastic operators.

All subtypes of `StochasticProcess` contain at least one field, `dt`, representing the time interval being discretized over.

The available `StochasticProcess`es are

• `BrownianProcess(dt)`: Brownian random walk. The state of the iterator is the cumulative displacement of the random walk.
• `WhiteNoiseProcess(dt)` : White noise. The value of this iterator is `randn()*dt`. The state associated with this iterator is `nothing`.
• `StochasticAiryProcess(dt, beta)`: stochastic Airy process with real positive `beta`. The value of this iterator in the limit of an infinite number of iterations is known to follow the `beta`-Tracy-Widom law. The state associated with this iteratior is a `SymTridiagonal` matrix whose largest eigenvalue is the value of this process.

# Invariant ensembles

`InvariantEnsemble(str,n)` supports n x n unitary invariant ensemble with distribution. This has been moved to separate package InvariantEnsembles.jl

# References

• 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] 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]

03/18/2013

29 days ago

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