On Fri, Dec 4, 2009 at 2:09 AM, David Goldsmith <[email protected]>wrote:
> On Thu, Dec 3, 2009 at 9:17 AM, Charles R Harris < > [email protected]> wrote: > >> >> >> On Thu, Dec 3, 2009 at 7:59 AM, Peter Cai <[email protected]> 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? > > Leave off the Hermitean bit since it is irrelevant to our decomposition, show a = u*diag(s)*v, and make a note explaining the usual convention. Chuck
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