numpy.linalg.cond

numpy.linalg.cond(x, p=None)

Compute the condition number of a matrix.

The condition number of x is the norm of x times the norm of the inverse of x. The norm can be the usual L2 (root-of-sum-of-squares) norm or a number of other matrix norms.

Parameters:

x : array_like, shape (M, N)

The matrix whose condition number is sought.

p : {None, 1, -1, 2, -2, inf, -inf, ‘fro’}

Order of the norm:

p	norm for matrices
None	2-norm, computed directly using the SVD
‘fro’	Frobenius norm
inf	max(sum(abs(x), axis=1))
-inf	min(sum(abs(x), axis=1))
1	max(sum(abs(x), axis=0))
-1	min(sum(abs(x), axis=0))
2	2-norm (largest sing. value)
-2	smallest singular value

Returns:

c : float

The condition number of the matrix. May be infinite.