jax.numpy.linalg.cond#
- jax.numpy.linalg.cond(x, p=None)[source]#
Compute the condition number of a matrix.
LAX-backend implementation of
numpy.linalg.cond()
.Original docstring below.
This function is capable of returning the condition number using one of seven different norms, depending on the value of p (see Parameters below).
- Parameters:
x ((..., M, N) array_like) – The matrix whose condition number is sought.
p ({None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional) –
Order of the norm used in the condition number computation:
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
inf means the numpy.inf object, and the Frobenius norm is the root-of-sum-of-squares norm.
- Returns:
c – The condition number of the matrix. May be infinite.
- Return type:
{float, inf}
References