[Numpy-discussion] question about the documentation of linalg.solve
Charles R Harris
charlesr.harris@gmail....
Thu Nov 20 00:20:25 CST 2008
On Wed, Nov 19, 2008 at 10:58 PM, Scott Sinclair <scott.sinclair.za@
gmail.com> wrote:
> 2008/11/20 Charles R Harris <charlesr.harris@gmail.com>:
> >
> > On Wed, Nov 19, 2008 at 3:20 PM, Fabrice Silva <silva@lma.cnrs-mrs.fr>
> > wrote:
> >>
> >> Le mercredi 19 novembre 2008 à 14:27 -0500, Alan G Isaac a écrit :
> >> > So my question is not just what is the algorithm
> >> > but also, what is the documentation goal?
> >>
> >> Concerning the algorithm (only):
> >> in Joshua answer, you have might have seen that solve is a wrapper to
> >> lapack routines *gesv (z* or d* depending on the input type).
> >
> > Which, IIRC, calls *getrf to get the LU factorization of the lhs matrix
> A.
> > Here:
> >
> > * DGESV computes the solution to a real system of linear equations
> > * A * X = B,
> > * where A is an N-by-N matrix and X and B are N-by-NRHS matrices.
> >
> > *
> > * The LU decomposition with partial pivoting and row interchanges is
> > * used to factor A as
> > * A = P * L * U,
> > * where P is a permutation matrix, L is unit lower triangular, and U is
> > * upper triangular. The factored form of A is then used to solve the
> >
> > * system of equations A * X = B.
> > *
>
> It's not always fun to read the code in order to find out what a
> function does. So I guess the documentation goal is to eventually add
> sufficient detail, for those who want to know what's happening without
> diving into the source code.
>
> A Notes section giving an overview of the algorithm has been added to
> the docstring http://docs.scipy.org/numpy/docs/numpy.linalg.linalg.solve/.
>
> I didn't feel comfortable quoting directly from the LAPACK comments,
> so maybe someone else can look into adding more detail.
>
Looks fine to me.
Chuck
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://projects.scipy.org/pipermail/numpy-discussion/attachments/20081119/0c4fa3c0/attachment.html
More information about the Numpy-discussion
mailing list