Indeed, this is a terribly naive code to permute the entries... assuming
that the following is fast
U,V,D = m.__pari__().matsnf(1).sage()
permuting the entries should be done
- in place
- not in Python
I would also suggest to provide access to the alternative implementation
with flint
void fmpz_mat_snf(fmpz_mat_t S, const fmpz_mat_t A);
Best
Vincent
Le 11/10/2018 à 01:20, William Stein a écrit :
On Wed, Oct 10, 2018 at 3:29 PM Travis Scrimshaw <tsc...@ucdavis.edu> wrote:
So the bulk of the time is spent converting the Pari object back into a Sage
object; mainly, this line:
U = self.matrix_space(ncols = self._nrows)([v[0][i,j] for i in
xrange(self._nrows-1,-1,-1) for j in xrange(self._nrows)])
which has 1000x1000 calls to extra the data and then has to be reconstituted
into a matrix object. I am not sure if there is a way around this. It might
just be a by-product of outsourcing the computation.
Thanks for boiling this down to the bottleneck. It can probably be
made much faster. Instead of doing
v[0][i,j],
which probably involves at least two complicated pari calls, is there
a way to convert v[0] to a Python list in one call? If not, one could
add an optimized way to do so. Then convert that list to a matrix in
sage.
Best,
Travis
On Thursday, October 11, 2018 at 5:58:04 AM UTC+10, John Cremona wrote:
The method smith_form() for matrices over ZZ uses pari's matsnf() but is
vastly slower. Compare these:
sage: M=MatrixSpace(ZZ,1000,5).random_element()
sage: %timeit S=M.smith_form(transformation=True)
1 loop, best of 3: 2.57 s per loop
sage: pM=pari(M)
sage: %timeit S=pM.matsnf(1)
10 loops, best of 3: 49.5 ms per loop
That's a factor of 50. There's something to be done in the interface because
pari's convention is opposite to Sage's so one has to reverse the order of
rows/columns in the output from pari, but still...
John
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