<mailto:
guillaume.saupin@xxxxxx
<mailto:
guillaume.saupin@xxxxxx>>> wrote:
Gael Guennebaud a écrit :
this is not an easy question because we are
still unsure how
to manage efficiently and with minimal code all
these kind of
"special" matrices.
Of course an easy solution would be to make it
inherit
SparseMatrixBase and let the generic algorithms
based on
sparse iterators do the job. However, this
approach won't be
optimal because the iterators won't take
advantage of the
specificity of a SkylineMatrix which, e.g.,
should allow
vectorization.
Actually skyline matrices are very similar to
banded matrices,
and in particular it seems to me that they are
similar enough
to share the same algorithms. So for instance
these two kind
of matrices could inherit the same matrix base
and the
algorithms could be implemented in a generic
way via the
concept of "range inner vector" which is a
column (or a row)
vector with an associated start index... We
also need
efficient basic operators dealing with such
vector. E.g. the
addition of two such vector can be efficiently
done by first
copying the non overlapping part (start and
end), and then sum
the overlapping part using Eigen's core module
feature.
The way we implemented skyline matrix is not really
similar to
what is usually done for banded matrix, as we store
the diagonal,
the upper, and the lower elements in separate
arrays. This way, we
can achieve quite efficient LU decomposition (and
solving) due to
coherent memory accesses.
ok I see, then it is not really what people call
skyline storage
(
http://www.netlib.org/linalg/html_templates/node96.html),
but it is more like an extension of the tridiagonal
storage.
I think that our storage is actually quite similar to the
one proposed there for non symetric skyline (referred as
NSK format by Saad) matrix. As in these format, we store
the upper element in a column-oriented way, and the lower
element in a row-oriented way.
The only difference is that we store the diagonal elements
in a separate array instead of keeping an index array to
these elements. This allows contiguous access to diagonal
elements.
However, I don't see how your storage can yield to a
more efficient implementation of the LU decomposition
than the standard skyline storage. For instance here
is a basic algorithm without pivoting for the standard
skyline storage:
for(int k = 0; k+1 < rows; ++k)
{
int rrows = rows-k-1;
int rsize = size-k-1;
// line 1
lu.col(k).end(rrows) /= lu.coeff(k,k);
// line 2
lu.corner(BottomRight,rrows,rsize).noalias() -=
lu.col(k).end(rrows) * lu.row(k).end(rsize);
}
where lu is the working matrix, lu.col(k) is assumed
to return a "range vector" as I described in my
previous email.
Here line 1 would be trivially optimized (i.e.,
vectorized) since lu.col(k).end(rrows) is just a small
dense vector.
Line 2 is an outer product which again is
trivially/automatically vectorized sicne it is
impelmented as a sequence of: "col_range_vector_i -=
scalar * col_range_vector".
Here the locality is pretty good because the vector
"lu.col(k).end(rrows)" which is reused multiple times
is sequentially stored in memory.
But perhaps there exists a special algorithm which
perfectly fit your storage ? Is it possible to see
your code somewhere ? Finally, if it is really more
efficient then that would make sense to have it in Eigen.
The benefit of using the NSK, i.e.. of storing the lower
matrix in a column-oriented way, and the upper matrix in a
row-oriented way is that in line 1 we access contigous
elements (the column k of the lower matrix), and in line 2
we also access contiguous element (the row elements of the
upper matrix). I don't know if it's efficient for
vectorization, but without, it is quite efficient, as the
locality is very good.
gael.
So I don't think that our algorithms share common
features with
band matrix algorithms. They seem closer to what
can be done with
diagonal matrices.
Anyway, I've used the SparseMatrix code as a base
for my
SkylineMatrix code. Currently it only supports very
basic
operations. I'll implement the LU decomposition
soon. I will also
try to use vectorization.
guillaume
These are just some initial thoughts and the
discussion is
very open!
gael
On Mon, Oct 19, 2009 at 12:55 PM, guillaume saupin
<
guillaume.saupin@xxxxxx
<mailto:
guillaume.saupin@xxxxxx>
<mailto:
guillaume.saupin@xxxxxx
<mailto:
guillaume.saupin@xxxxxx>>
<mailto:
guillaume.saupin@xxxxxx
<mailto:
guillaume.saupin@xxxxxx>
<mailto:
guillaume.saupin@xxxxxx
<mailto:
guillaume.saupin@xxxxxx>>>> wrote:
Hello,
We are planning to use your library in our
projects, but we
need a
skyline matrix. Therefore I'd like to
implement one for
eigen, but
I don't now where to start.
Is there a specific class that can be a good
starting point /
skeleton to start with ? The
SparseMatrixBase might be a
good choice.
Should this SkylineMatrix inherit from
SparseMatrixBase, or
be a
separate class ?
Thanks,
guillaume