# hypergeomatrix: Hypergeometric function of a matrix argument

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Evaluation of hypergeometric functions of a matrix argument, following Koev & Edelman's algorithm.    ## Modules

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# hypergeomatrix

## Evaluation of the hypergeometric function of a matrix argument (Koev & Edelman's algorithm)

Let $$(a_1, \ldots, a_p)$$ and $$(b_1, \ldots, b_q)$$ be two vectors of real or complex numbers, possibly empty, $$\alpha > 0$$ and $$X$$ a real symmetric or a complex Hermitian matrix. The corresponding hypergeometric function of a matrix argument is defined by

${}_pF_q^{(\alpha)} \left(\begin{matrix} a_1, \ldots, a_p \\\\ b_1, \ldots, b_q\end{matrix}; X\right) = \sum_{k=0}^{\infty}\sum_{\kappa \vdash k} \frac{{(a_1)}_{\kappa}^{(\alpha)} \cdots {(a_p)}_{\kappa}^{(\alpha)}} {{(b_1)}_{\kappa}^{(\alpha)} \cdots {(b_q)}_{\kappa}^{(\alpha)}} \frac{C_{\kappa}^{(\alpha)}(X)}{k!}.$

The inner sum is over the integer partitions $$\kappa$$ of $$k$$ (which we also denote by $$|\kappa| = k$$). The symbol $${(\cdot)}_{\kappa}^{(\alpha)}$$ is the generalized Pochhammer symbol, defined by

${(c)}^{(\alpha)}_{\kappa} = \prod_{i=1}^{\ell}\prod_{j=1}^{\kappa_i} \left(c - \frac{i-1}{\alpha} + j-1\right)$

when $$\kappa = (\kappa_1, \ldots, \kappa_\ell)$$. Finally, $$C_{\kappa}^{(\alpha)}$$ is a Jack function. Given an integer partition $$\kappa$$ and $$\alpha > 0$$, and a real symmetric or complex Hermitian matrix $$X$$ of order $$n$$, the Jack function

$C_{\kappa}^{(\alpha)}(X) = C_{\kappa}^{(\alpha)}(x_1, \ldots, x_n)$

is a symmetric homogeneous polynomial of degree $$|\kappa|$$ in the eigen values $$x_1$$, $$\ldots$$, $$x_n$$ of $$X$$.

The series defining the hypergeometric function does not always converge. See the references for a discussion about the convergence.

The inner sum in the definition of the hypergeometric function is over all partitions $$\kappa \vdash k$$ but actually $$C_{\kappa}^{(\alpha)}(X) = 0$$ when $$\ell(\kappa)$$, the number of non-zero entries of $$\kappa$$, is strictly greater than $$n$$.

For $$\alpha=1$$, $$C_{\kappa}^{(\alpha)}$$ is a Schur polynomial and it is a zonal polynomial for $$\alpha = 2$$. In random matrix theory, the hypergeometric function appears for $$\alpha=2$$ and $$\alpha$$ is omitted from the notation, implicitely assumed to be $$2$$.

Koev and Edelman (2006) provided an efficient algorithm for the evaluation of the truncated series

$\sideset{_p^m}{_q^{(\alpha)}}F \left(\begin{matrix} a_1, \ldots, a_p \\\\ b_1, \ldots, b_q\end{matrix}; X\right) = \sum_{k=0}^{m}\sum_{\kappa \vdash k} \frac{{(a_1)}_{\kappa}^{(\alpha)} \cdots {(a_p)}_{\kappa}^{(\alpha)}} {{(b_1)}_{\kappa}^{(\alpha)} \cdots {(b_q)}_{\kappa}^{(\alpha)}} \frac{C_{\kappa}^{(\alpha)}(X)}{k!}.$

Hereafter, $$m$$ is called the truncation weight of the summation (because $$|\kappa|$$ is called the weight of $$\kappa$$), the vector $$(a_1, \ldots, a_p)$$ is called the vector of upper parameters while the vector $$(b_1, \ldots, b_q)$$ is called the vector of lower parameters. The user has to supply the vector $$(x_1, \ldots, x_n)$$ of the eigenvalues of $$X$$.

For example, to compute

$\sideset{_2^{15}}{_3^{(2)}}F \left(\begin{matrix} 3, 4 \\\\ 5, 6, 7\end{matrix}; 0.1, 0.4\right)$

you have to enter

hypergeomat 15 2 [3.0, 4.0], [5.0, 6.0, 7.0] [0.1, 0.4]


We said that the hypergeometric function is defined for a real symmetric matrix or a complex Hermitian matrix $$X$$. Thus the eigenvalues of $$X$$ are real. However we do not impose this restriction in hypergeomatrix. The user can enter any list of real or complex numbers for the eigenvalues.

### Gaussian rational numbers

The library allows to use Gaussian rational numbers, i.e. complex numbers with a rational real part and a rational imaginary part. The Gaussian rational number $$a + ib$$ is obtained with a +: b, e.g. (2%3) +: (5%2). The imaginary unit usually denoted by $$i$$ is represented by e(4):

ghci> import Math.HypergeoMatrix
ghci> import Data.Ratio
ghci> alpha = 2%1
ghci> a = (2%7) +: (1%2)
ghci> b = (1%2) +: (0%1)
ghci> c = (2%1) +: (3%1)
ghci> x1 = (1%3) +: (1%4)
ghci> x2 = (1%5) +: (1%6)
ghci> hypergeomat 3 alpha [a, b] [c] [x1, x2]
26266543409/25159680000 + 155806638989/3698472960000*e(4)


### Univariate case

For $$n = 1$$, the hypergeometric function of a matrix argument is known as the generalized hypergeometric function. It does not depend on $$\alpha$$. The case of $$\sideset{_{2\thinspace}^{}}{_1^{}}F$$ is the most known, this is the Gauss hypergeometric function. Let's check a value. It is known that

$\sideset{_{2\thinspace}^{}}{_1^{}}F \left(\begin{matrix} 1/4, 1/2 \\\\ 3/4\end{matrix}; 80/81\right) = 1.8.$

Since $$80/81$$ is close to $$1$$, the convergence is slow. We compute the truncated series below for $$m = 300$$.

ghci> h <- hypergeomat 300 2 [1/4, 1/2] [3/4] [80/81]
ghci> h
1.7990026528192298

• Plamen Koev and Alan Edelman. The efficient evaluation of the hypergeometric function of a matrix argument. Mathematics of computation, vol. 75, n. 254, 833-846, 2006.

• Robb Muirhead. Aspects of multivariate statistical theory. Wiley series in probability and mathematical statistics. Probability and mathematical statistics. John Wiley & Sons, New York, 1982.

• A. K. Gupta and D. K. Nagar. Matrix variate distributions. Chapman and Hall, 1999.