# [SciPy-dev] Summer Marathon "Category of the Week"

Stéfan van der Walt stefan@sun.ac...
Thu Jul 2 05:20:12 CDT 2009

```Hi marathon runners,

Well done on the progress made last week!

2009/7/1 David Goldsmith <d_l_goldsmith@yahoo.com>:
> Linear Algebra! (My favorite! :-))  Let's get it to pink or better by next Wed!

Excellent!  The linalg module is shown here:

http://docs.scipy.org/numpy/docs/numpy.linalg/

You can choose from:

cholesky cond det eig eigh eigvals eigvalsh
inv lstsq matrix_power norm inv qr solve svd tensorinv tensorsolve

These mostly have decent docstrings already, so I'd suggest focusing
on making the following more accessible to your typical linear algebra
student:

http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.eig/ [1]
http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.qr/ [2]
http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.norm/ [3]
http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.svd/ [4]
http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.tensorinv/ [5]

Have fun!
Stéfan

[1] In the notes section, describe right vs left eigenvectors.
Personally, I think we can drop the reference to the characteristic
polynomial (we
don't solve it that way in any case).

[2] Needs formatting and a link to an explanation of QR.

[3] Describe what Frobenius means, or give a link to an appropriate reference.

[4] Note Pauli's comment: indicate clearly that the decomposition A =
U S V.T is returned (V.H for complex matrices).

[5] Badly in need of some examples.
```