Ultimately, the classical canonical form/isomorphism implementations run on
(di)graphs represented by 0-1 matrices, often
with bit entries. So that's how bliss_digraph is represented too.
Constructing it directly might be beneficial, especially if you have a
problem of doing this on many small ma
On Tuesday, March 6, 2018 at 11:56:59 PM UTC, Matthew Lancellotti wrote:
>
> sage: latest version (8.1) (from https://github.com/sagemath/sage, last
> updated Dec 17, 2017). tried both master branch and develop branch.
> OS: Mac OSX 10.13.3
> Xcode version 9.0.1
>
>
> I cloned the latest versio
Here is the log file:
https://drive.google.com/file/d/1zHxRRCueBxlHHI1qp4wfkHAT9yjwaxUG/view?usp=sharing
On Tuesday, March 6, 2018 at 6:56:59 PM UTC-5, Matthew Lancellotti wrote:
>
> sage: latest version (8.1) (from https://github.com/sagemath/sage, last
> updated Dec 17, 2017). tried both mast
sage: latest version (8.1) (from https://github.com/sagemath/sage, last
updated Dec 17, 2017). tried both master branch and develop branch.
OS: Mac OSX 10.13.3
Xcode version 9.0.1
I cloned the latest version of sage from the git repository. When
attempting to `make` sage, I run into the error
Hi Simon,
Thanks for trying! I was actually hoping for a way to completely avoid
creating this sage DiGraph. But either to get the matrix directly into the
algorithm (which currently seems impossible), or at least to directly
construct some internal graph data structure.
Looking at the code of
PS:
On 2018-03-06, Simon King wrote:
> On 2018-03-06, Christian Stump wrote:
> I haven't really been able to work around the bottle neck, but got a
> minor improvement (4%) as follows:
> ...
> And of course using cython helps as well. With the following Cython code, I am
> down to 272 µs per loo
Hi Christian,
On 2018-03-06, Christian Stump wrote:
> Let me add that the situations I care about are n,m <= 20, the entries are
><=5 and the matrices are sparsely filled. An random and typical example is
That matrix seems far too small to say anything substantial about
timings. However, profil
Dear Johan,
> The most inefficient part of _matrix_to_digraph seems to be the
> following line:
>
> > x = edge_labels.index((a,b))
>
you are totally right, thanks for this suggestion! (Unfortunately, this
will not change anything in practice, because the list edge_labels
Hi Christian,
The most inefficient part of _matrix_to_digraph seems to be the
following line:
> x = edge_labels.index((a,b))
which runs in linear time on the length of edge_labels. If all the pairs
(a,b) are different on your input matrix, that gives an n^3 running time
for t
Let me add that the situations I care about are n,m <= 20, the entries are
<=5 and the matrices are sparsely filled. An random and typical example is
sage: M = matrix([(0, -1, 0, 0, 0, 0, 0, 1),
: (1, 0, 1, 0, 0, 0, 0, 0),
: (0, -1, 0, 0, 1, 0, 0, 0),
: (0, 0, 0, 0, 0, 1, 0, 0),
..
(This question is about speed improvements of an existing approach, not
about finding a first toy solution.)
Let A, B be two ( (n+m) x n ) integer matrices. Say that these are
isomorphic if they coincide up to *simultaneous* row and column
permutations of the indices 0,...,n-1.
Example: [[1,0]
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