[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.
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