Hypergeometric Function of a Matrix Argument
Current version: 1.5, posted
February 12, 2018.
What's new in 1.5:
- Introducing new functions mhgA1A and mhgA1B for computing the hypergeometric function faster when alpha=1. See below.
What was new in 1.4:
- The only changes pertain to mhg.c.
- Introduced a way to restrict the summation over partitions kappa \subset \lambda.
- Automatically restricting the summation over partitions in <=min(rank X, rank Y) parts.
- Automatically restricting the summation to partitions of only one part if one of the p_i's equals 1/alpha = beta/2.
What was new in 1.3: Improved algorithms for mhg and mhgi which should
be faster than the previous versions. logmhg remains unchanged.
Download and unzip the linked file.
Recompile the C files (by typing, e.g., "mex mhg.c" at the MATLAB prompt).
How to cite:
Plamen Koev and Alan Edelman,
The efficient evaluation of the
hypergeometric function of a matrix
argument, Math. Comp. 75 (2006), 833-846.
- How do these algorithms work? See our
- Only REAL arguments can be passed. If you need to use COMPLEX or
SYMBOLIC arguments, please contact me.
||[s,c]=mhg([M K lambda],alpha,a,b,x,y)
||Computes the truncated Hypergeometric function of one or
two matrix arguments |
b1,..., bq; X, Y)
as a series of Jack functions, truncated for
partitions of size not exceeding M.
- alpha is positive real
(typically alpha=2 in settings
involving REAL random matrices, and alpha=1 in settings involving
COMPLEX random matrices);
- a and b are real arrays;
- x and y are real arrays containing
the eigenvalues of the matrix arguments X and Y, respectively;
- The argument y may be omitted.
- The argument K may be omitted but if specified, the summation is over partitions kappa such that kappa<=K.
- The argument lambda may be omitted, but if specified, the summation is over partitions kappa such that kappa[i]<=lambda[i] for i=1,2,...
- s is the value of the truncated hypergeometric function
- c is a
vector of size M+1 with the
marginal sums for partitions of size 0,1,...,M
Larger values of M will yield more accurate results. There are no rules
on selecting the optimal M. Start with (say) M=10 and experiment until
you obtain the best value of M for your application. The values of the
output vector c may give you some idea about the convergence.
||mhg(20,2,[0.1,0.2],[0.3,0.4],[0.5 0.6 0.7]) returns
3.1349, which is a good approximation to
||mhg(20,1,[ ],[0.1],[0.2 0.3 0.4],[0.5 0.6 0.7]) returns
6.6265, which is a good approximation to
||mhg([20 4],1,[0.8],[0.1],[0.2 0.3 0.4]) returns
13.4183, which is an approximation to
diag(0.2,0.3,0.4)) summed over partitions whose size does not exceed 20 and all of whose parts do not exceed 4.
||mhg([10 4 4 3 1],1,[0.8],[0.1],[0.2 0.3 0.4]) returns
13.4182, which is an approximation to
diag(0.2,0.3,0.4)) summed over partitions kappa whose size does not exceed 20 AND all of whose parts do not exceed 4 AND kappa<=4, kappa<=3, kappa<=1.
||Same as mhg when X=xIn is a multiple of the identity
(but using a much more efficient algorithm available for this special
- alpha, a, and b are as in mhg
- M can now be a vector with 1 or 2 elements. If M is a vector with one
element, then it serves the same purpose as in mhg above. The second,
optional element in M, call it M(2), is such that the summation is only
over partitions whose parts do not exceed M(2)
- n is the size of the matrix argument
- x is a real number or a vector of real numbers
- s is the value of the truncated hypergeometric function evaluated at
each element of the vector x
- Since s is a polynomial in x of degree M, the output vector c contains
coefficients of that polynomial. This way
s=c(1)+x*c(2)+x^2*c(3)+...+x^M*c(M+1), which is meant componentwise when
x is a
vector. Equivalently, in MATLAB notation, s=polyval(c(end:-1:1),x).
||The output vector c is slightly different in mhg and mhgi.
||mhgi(20,1,[ ],[0.1],3,[0.5 0.6]) returns
the vector [11.9545, 9.3686], which is a good approximation to
x I3) for x=0.5 and 0.6, respectively.
||Same as mhg, but computes its natural logarithm. Useful in cases
when mhg overflows and has only one output argument.
||Same as mhg when alpha=1, but computed much more efficiently using the FIRST algorithm described in
"On Computing Schur functions and Series Thereof" by Chan, Drensky, Edelman, Kan, and Koev. Download Cy Chan's
package and compile using compileScript.
||Same as mhg when alpha=1, but computed much more efficiently using the SECOND algorithm described in
"On Computing Schur functions and Series Thereof" by Chan, Drensky, Edelman, Kan, and Koev.
Department of Mathematics, Massachusetts Institute
"firstname" dot "lastname" AT sjsu.edu