rkjt50r983 - 2 months ago 7
Python Question

# Generate non-singular sparse matrix in Python

When a sparse matrix is generated by

`scipy.sparse.rand`
, it can be singular. In fact,the below code raises an error
`"RuntimeError: superlu failure (singular matrix?) at line 100 in file scipy/sparse/linalg/dsolve/SuperLU/SRC/dsnode_bmod.c"`
.

``````dim = 20000
ratio = 0.000133

A = scipy.sparse.rand(dim,dim,ratio)
inv_sparse = scipy.sparse.linalg.inv(A)
``````

Is there a way to generate non-singular sparse matrix?

What I really want to do is comparing performance (process time) of
`scipy.sparse.linalg.inv`
with
`np.linalg.inv`
. That's why I need generate random sparse matrix which is not singular.

Answer

The density `ratio = 0.000133` of your matrix is very low. It means that about one item out of 7518 is non-null. Hence, the probability of each term to be null is about 7517/7518.

Each row is made of 20000 independent terms. So the probability for a row to be null is (7517/7518)^20000=6.99%. Hence, the probability for a row to be non-null is 1-(7517/7518)^20000=93.0%.

Then, the matrix is made of 20000 rows. The rows can be considered as being independent. Hence, the probability that the matrix does not contain null rows is (1-(7517/7518)^20000)^20000=(93.0%)^20000. This probability is very low.

As the matrix is likely to contain a null row, it is often singular.

Moreover, due to the the limited precision of floating-point numbers, programs often consider ill-conditionned matrices as singular. Indeed, in such cases, the computed inverse would be highly unprecise and meaningless.

Finally, to compare the inverse function, it may be better to use matrices known to be invertible... At least, you could try to increase the density so that the probability of a null row becomes very low.

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