[Numpy-discussion] svd error checking vs. speed

[alex]

Hello list,

Here's another idea resurrection from numpy github comments that I've
been advised could be posted here for re-discussion.

The proposal would be to make np.linalg.svd more like scipy.linalg.svd
with respect to input checking.  The argument against the change is
raw speed; if you know that you will never feed non-finite input to
svd, then np.linalg.svd is a bit faster than scipy.linalg.svd.  An
argument for the change could be to avoid issues reported on github
like crashes, hangs, spurious non-convergence exceptions, etc. from
the undefined behavior of svd of non-finite input.

"""
[...] the following numpy code hangs until I `kill -9` it.

```
$ python runtests.py --shell
$ python
Python 2.7.5+
[GCC 4.8.1] on linux2
>>> import numpy as np
>>> np.__version__
'1.9.0.dev-e3f0f53'
>>> A = np.array([[1e3, 0], [0, 1]])
>>> B = np.array([[1e300, 0], [0, 1]])
>>> C = np.array([[1e3000, 0], [0, 1]])
>>> np.linalg.svd(A)
(array([[ 1.,  0.],
       [ 0.,  1.]]), array([ 1000.,     1.]), array([[ 1.,  0.],
       [ 0.,  1.]]))
>>> np.linalg.svd(B)
(array([[ 1.,  0.],
       [ 0.,  1.]]), array([  1.00000000e+300,   1.00000000e+000]),
array([[ 1.,  0.],
       [ 0.,  1.]]))
>>> np.linalg.svd(C)
[hangs forever]
```
"""

Alex