[Numpy-discussion] How to solve homogeneous linear equations with NumPy?

[David Goldsmith]

On Thu, Dec 3, 2009 at 9:17 AM, Charles R Harris
<charlesr.harris at gmail.com>wrote:

>
>
> On Thu, Dec 3, 2009 at 7:59 AM, Peter Cai <newptcai at gmail.com> wrote:
>
>> Thanks, I've read some explanations on wikipedia and finally found out
>> how to solve homogeneous equations by singular value decomposition.
>>
>>
> Note that the numpy svd doesn't quite conform to what you will see in those
> sources and the documentation is confusing. Numpy returns
> u,s,v and a = u*diag(s)*v, whereas the decomposition is normally written as
> u*diag(s)*v^T, i.e., the numpy v is the transpose (Hermitean conjugate) of
> the conventional v.
>
> Chuck
>

It's quite clear to me (at least in the version of the doc in the Wiki) that
what is returned in the third "slot" is the "Hermitean of v", i.e., the
third factor in the decomposition the way it is "normally written"; how
would you suggest it be made clearer?

DG
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