Jack function

In mathematics, the Jack function, introduced by Henry Jack, is a homogeneous, symmetric polynomial which generalizes the Schur and zonal polynomials, and is in turn generalized by the Heckman–Opdam polynomials and Macdonald polynomials.

Definition

The Jack function $J_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{m})$ of integer partition $\kappa$, parameter $\alpha$ and arguments $x_{1},x_{2},\ldots ,$ can be recursively defined as follows:

For m=1

$J_{k}^{(\alpha )}(x_{1})=x_{1}^{k}(1+\alpha )\cdots (1+(k-1)\alpha )$

For m>1

$J_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{m})=\sum _{\mu }J_{\mu }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{m-1})x_{m}^{|\kappa /\mu |}\beta _{\kappa \mu },$

where the summation is over all partitions $\mu$ such that the skew partition $\kappa /\mu$ is a horizontal strip, namely

$\kappa _{1}\geq \mu _{1}\geq \kappa _{2}\geq \mu _{2}\geq \cdots \geq \kappa _{n-1}\geq \mu _{n-1}\geq \kappa _{n}$ ($\mu _{n}$ must be zero or otherwise $J_{\mu }(x_{1},\ldots ,x_{n-1})=0$) and

$\beta _{\kappa \mu }={\frac {\prod _{(i,j)\in \kappa }B_{\kappa \mu }^{\kappa }(i,j)}{\prod _{(i,j)\in \mu }B_{\kappa \mu }^{\mu }(i,j)}},$

where $B_{\kappa \mu }^{\nu }(i,j)$ equals $\kappa _{j}'-i+\alpha (\kappa _{i}-j+1)$ if $\kappa _{j}'=\mu _{j}'$ and $\kappa _{j}'-i+1+\alpha (\kappa _{i}-j)$ otherwise. The expressions $\kappa '$ and $\mu '$ refer to the conjugate partitions of $\kappa$ and $\mu$, respectively. The notation $(i,j)\in \kappa$ means that the product is taken over all coordinates $(i,j)$ of boxes in the Young diagram of the partition $\kappa$.

C normalization

The Jack functions form an orthogonal basis in a space of symmetric polynomials, with inner product: $\langle f,g\rangle =\int _{[0,2\pi ]^{n}}f(e^{i\theta _{1}},\cdots ,e^{i\theta _{n}}){\overline {g(e^{i\theta _{1}},\cdots ,e^{i\theta _{n}})}}\prod _{1\leq j<k\leq n}|e^{i\theta _{j}}-e^{i\theta _{k}}|^{2/\alpha }d\theta _{1}\cdots d\theta _{n}$

This orthogonality property is unaffected by normalization. The normalization defined above is typically referred to as the J normalization. The C normalization is defined as

$C_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{n})={\frac {\alpha ^{|\kappa |}(|\kappa |)!}{j_{\kappa }}}J_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{n}),$

where

$j_{\kappa }=\prod _{(i,j)\in \kappa }(\kappa _{j}'-i+\alpha (\kappa _{i}-j+1))(\kappa _{j}'-i+1+\alpha (\kappa _{i}-j)).$

For $\alpha =2,\;C_{\kappa }^{(2)}(x_{1},x_{2},\ldots ,x_{n})$ denoted often as just $C_{\kappa }(x_{1},x_{2},\ldots ,x_{n})$ is known as the Zonal polynomial.

P normalization

The P normalization is given by the identity $J_{\lambda }=H'_{\lambda }P_{\lambda }$, where $H'_{\lambda }=\prod _{s\in \lambda }(\alpha a_{\lambda }(s)+l_{\lambda }(s)+1)$ and $a_{\lambda }$ and $l_{\lambda }$ denotes the arm and leg length respectively. Therefore, for $\alpha =1$, $P_{\lambda }$ is the usual Schur function.

Similar to Schur polynomials, $P_{\lambda }$ can be expressed as a sum over Young tableaux. However, one need to add an extra weight to each tableau that depends on the parameter $\alpha$.

Thus, a formula for the Jack function $P_{\lambda }$ is given by

$P_{\lambda }=\sum _{T}\psi _{T}(\alpha )\prod _{s\in \lambda }x_{T(s)}$

where the sum is taken over all tableaux of shape $\lambda$, and $T(s)$ denotes the entry in box s of T.

The weight $\psi _{T}(\alpha )$ can be defined in the following fashion: Each tableau T of shape $\lambda$ can be interpreted as a sequence of partitions $\emptyset =\nu _{1}\to \nu _{2}\to \dots \to \nu _{n}=\lambda$ where $\nu _{i+1}/\nu _{i}$ defines the skew shape with content i in T. Then $\psi _{T}(\alpha )=\prod _{i}\psi _{\nu _{i+1}/\nu _{i}}(\alpha )$ where

$\psi _{\lambda /\mu }(\alpha )=\prod _{s\in R_{\lambda /\mu }-C_{\lambda /\mu }}{\frac {(\alpha a_{\mu }(s)+l_{\mu }(s)+1)}{(\alpha a_{\mu }(s)+l_{\mu }(s)+\alpha )}}{\frac {(\alpha a_{\lambda }(s)+l_{\lambda }(s)+\alpha )}{(\alpha a_{\lambda }(s)+l_{\lambda }(s)+1)}}$

and the product is taken only over all boxes s in $\lambda$ such that s has a box from $\lambda /\mu$ in the same row, but not in the same column.

Connection with the Schur polynomial

When $\alpha =1$ the Jack function is a scalar multiple of the Schur polynomial

$J_{\kappa }^{(1)}(x_{1},x_{2},\ldots ,x_{n})=H_{\kappa }s_{\kappa }(x_{1},x_{2},\ldots ,x_{n}),$

where

$H_{\kappa }=\prod _{(i,j)\in \kappa }h_{\kappa }(i,j)=\prod _{(i,j)\in \kappa }(\kappa _{i}+\kappa _{j}'-i-j+1)$

is the product of all hook lengths of $\kappa$.

Properties

If the partition has more parts than the number of variables, then the Jack function is 0:

$J_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{m})=0,{\mbox{ if }}\kappa _{m+1}>0.$

Matrix argument

In some texts, especially in random matrix theory, authors have found it more convenient to use a matrix argument in the Jack function. The connection is simple. If $X$ is a matrix with eigenvalues $x_{1},x_{2},\ldots ,x_{m}$, then

$J_{\kappa }^{(\alpha )}(X)=J_{\kappa }^{(\alpha )}(x_{1},x_{2},\ldots ,x_{m}).$

References

• Demmel, James; Koev, Plamen (2006), "Accurate and efficient evaluation of Schur and Jack functions", Mathematics of Computation, 75 (253): 223–239, doi:10.1090/S0025-5718-05-01780-1, MR 2176397.
• Jack, Henry (1970–1971), "A class of symmetric polynomials with a parameter", Proceedings of the Royal Society of Edinburgh, Section A. Mathematics, 69: 1–18, MR 0289462.
• Macdonald, I. G. (1995), Symmetric functions and Hall polynomials, Oxford Mathematical Monographs (2nd ed.), New York: Oxford University Press, ISBN 0-19-853489-2, MR 1354144
• Stanley, Richard P. (1989), "Some combinatorial properties of Jack symmetric functions", Advances in Mathematics, 77 (1): 76–115, doi:10.1016/0001-8708(89)90015-7, MR 1014073.

External links

• Software for computing the Jack function by Plamen Koev.
• MOPS: Multivariate Orthogonal Polynomials (symbolically) (Maple Package)
• SAGE documentation for Jack Symmetric Functions