Ackermann set theory

In mathematics, the hypergeometric function of a matrix argument is a generalization of the classical hypergeometric series. It is a function defined by an infinite summation which can be used to evaluate certain multivariate integrals.

Hypergeometric functions of a matrix argument have applications in random matrix theory. For example, the distributions of the extreme eigenvalues of random matrices are often expressed in terms of the hypergeometric function of a matrix argument.

Definition

Let $p\ge 0$ and $q\ge 0$ be integers, and let $X$ be an $m\times m$ complex symmetric matrix. Then the hypergeometric function of a matrix argument $X$ and parameter $\alpha >0$ is defined as

$p_{}{F}_q^{(\alpha )}(a_1,\dots ,a_p;b_1,\dots ,b_q;X)=\sum _{k=0}^{\mathrm{\infty }}{\sum }_{\kappa ⊢k}\frac{1}{k!}\cdot \frac{(a_1{)}_{\kappa }^{(\alpha )}\cdots (a_p{)}_{\kappa }^{(\alpha )}}{(b_1{)}_{\kappa }^{(\alpha )}\cdots (b_q{)}_{\kappa }^{(\alpha )}}\cdot C_{\kappa }^{(\alpha )}(X),$

where $\kappa ⊢k$ means $\kappa$ is a partition of $k$, $(a_i{)}_{\kappa }^{(\alpha )}$ is the Generalized Pochhammer symbol, and $C_{\kappa }^{(\alpha )}(X)$ is the "C" normalization of the Jack function.

Two matrix arguments

If $X$ and $Y$ are two $m\times m$ complex symmetric matrices, then the hypergeometric function of two matrix arguments is defined as:

$p_{}{F}_q^{(\alpha )}(a_1,\dots ,a_p;b_1,\dots ,b_q;X,Y)=\sum _{k=0}^{\mathrm{\infty }}{\sum }_{\kappa ⊢k}\frac{1}{k!}\cdot \frac{(a_1{)}_{\kappa }^{(\alpha )}\cdots (a_p{)}_{\kappa }^{(\alpha )}}{(b_1{)}_{\kappa }^{(\alpha )}\cdots (b_q{)}_{\kappa }^{(\alpha )}}\cdot \frac{C_{\kappa }^{(\alpha )}(X)C_{\kappa }^{(\alpha )}(Y)}{C_{\kappa }^{(\alpha )}(I)},$

where $I$ is the identity matrix of size $m$.

Not a typical function of a matrix argument

Unlike other functions of matrix argument, such as the matrix exponential, which are matrix-valued, the hypergeometric function of (one or two) matrix arguments is scalar-valued.

The parameter $\alpha$

In many publications the parameter $\alpha$ is omitted. Also, in different publications different values of $\alpha$ are being implicitly assumed. For example, in the theory of real random matrices (see, e.g., Muirhead, 1984), $\alpha =2$ whereas in other settings (e.g., in the complex case--see Gross and Richards, 1989), $\alpha =1$. To make matters worse, in random matrix theory researchers tend to prefer a parameter called $\beta$ instead of $\alpha$ which is used in combinatorics.

The thing to remember is that

$\alpha =\frac{2}{\beta }.$

Care should be exercised as to whether a particular text is using a parameter $\alpha$ or $\beta$ and which the particular value of that parameter is.

Typically, in settings involving real random matrices, $\alpha =2$ and thus $\beta =1$. In settings involving complex random matrices, one has $\alpha =1$ and $\beta =2$.

References

• K. I. Gross and D. St. P. Richards, "Total positivity, spherical series, and hypergeometric functions of matrix argument", J. Approx. Theory, 59, no. 2, 224–246, 1989.

• J. Kaneko, "Selberg Integrals and hypergeometric functions associated with Jack polynomials", SIAM Journal on Mathematical Analysis, 24, no. 4, 1086-1110, 1993.

• Plamen Koev and Alan Edelman, "The efficient evaluation of the hypergeometric function of a matrix argument", Mathematics of Computation, 75, no. 254, 833-846, 2006.

• Robb Muirhead, Aspects of Multivariate Statistical Theory, John Wiley & Sons, Inc., New York, 1984.

External links

• Software for computing the hypergeometric function of a matrix argument by Plamen Koev.