The backend engine of triple integration is different in Python and Matlab.
In Python we use scipy.integration.tplquad to do triple integration; In Matlab the function is called integral3.
Also the level of parallelism is different. In Python, it is totally serialized, one evalulation at one integration point each time. However, in Matlab it is highly paralleled. I found a 14 times 14 matrix is passed to the evalulation function. As a result, the evalulation function should support matrix computation.
Because the difference in parallelism, integral3 is much faster than scipy.integration.tplquad.
Another big difference is the convergence result. I use the following example in matlab:
n = 8;
k = 4;
C0 = gamma(n/2) * gamma((n-1)/2) / (gamma(0.5) * gamma(k/2) * gamma((k-1)/2) * gamma((n-k)/2) * gamma((n-k-1)/2));
2 * C0 * integral3(@(x,y,z) (x.*y-z.^2).^((k-3)/2) .* (1-x-y+x.*y-z.^2).^((n-k-3)/2), 0,1,0,1,0,@(x,y) min(sqrt(x.*y), sqrt(1-x-y+x.*y)))
The final result is number 1.
However, when I compute the same integral in Python, I cannot get the right result:
import numpy as np
from scipy.special import gamma
from scipy.integrate import tplquad
n = 8
k = 4
C0 = 2 * gamma(n/2) * gamma((n-1)/2)
C0 /= (gamma(0.5) * gamma(k/2) * gamma((k-1)/2) * gamma((n-k)/2) * gamma((n-k-1)/2))
C0 *= tplquad(lambda x,y,z: abs(x*y-z**2)**((k-3)/2) * abs(1-x-y+x*y-z**2)**((n-k-3)/2),
0, 1, lambda x: 0, lambda x: 1, lambda x,y: 0,
lambda x,y: np.sqrt(np.min([x*y,1-x-y+x*y])))[0]
print(C0)