Scipy / Numpy：Cholesky 在检查是否为正定时

Question

I am translating a script from Matlab to Python where I have a matrix VarCov and I get the Cholesky decomposition of it. Sometimes, due to float approximation, a matrix that should be positive devinite (PD) is not, and i have to add a small number to the diagonal. Here is the Matlab code:

[CholeskyUpper,pd] = chol(VarCov);
while pd
    VarCov = VarCov + 0.0001 * eye(size(VarCov,1));
    [CholeskyUpper,pd] = chol(VarCov);
end

Matlab is convenient because it can return if a matrix is PD while doing the Cholesky decomposition. It doesn't raise an error while doing so. It seems that in Python (scipy), it will just return an error. Is there a way to do something similar to Matlab without having to compute the eigenvalues first ?

Edit: Following sascha's tip I tried this:

MyMatrix = np.array([[1,2],[1,2]])
PD = False
while PD == False:
    PD = True
    try:
        MyCholDec = sp.linalg.cholesky(MyMatrix)
    except np.linalg.LinAlgError:
        PD = False
        MyMatrix = MyMatrix + np.eye(2) * 0.001
print("done")  

Is this the best way ?

Answer 1

The fastest and easiest method I finally found is to directly use the scipy's lapack function:

MyMatrix = np.array([[1,2],[1,2]])        
(MyCholDec ,pd) = sp.linalg.lapack.dpotrf(MyMatrix )
while pd > 0:
     MyMatrix= MyMatrix  + np.eye(2) * 0.01
     (MyCholDec ,pd) = sp.linalg.lapack.dpotrf( MyMatrix )

This function works like the Matlab version retunring both the decomposition and an int > 0 if the matrix is not positive definite.