1))
0      abs(x).sum(axis=axis)
1      max(sum(abs(x), axis=0))
-1     min(sum(abs(x), axis=0))
2      Spectral norm (the largest singular value)
-2     Not implemented
other  Not implemented
=====  ============================

The Frobenius norm is given by [1]_:

    :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`

References
----------
.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
    Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15

Examples
--------
>>> from scipy.sparse import csr_array, diags_array
>>> import numpy as np
>>> from scipy.sparse.linalg import norm
>>> a = np.arange(9) - 4
>>> a
array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
>>> b = a.reshape((3, 3))
>>> b
array([[-4, -3, -2],
       [-1, 0, 1],
       [ 2, 3, 4]])

>>> b = csr_array(b)
>>> norm(b)
7.745966692414834
>>> norm(b, 'fro')
7.745966692414834
>>> norm(b, np.inf)
9
>>> norm(b, -np.inf)
2
>>> norm(b, 1)
7
>>> norm(b, -1)
6

The matrix 2-norm or the spectral norm is the largest singular
value, computed approximately and with limitations.

>>> b = diags_array([-1, 1], [0, 1], shape=(9, 10))
>>> norm(b, 2)
1.9753...
