Thanasis:
I have finished the change that I mentioned in my previous email, the details
are here https://gitlab.com/slepc/slepc/-/merge_requests/586
The change has been already merged into the main branch, and thus will be
included in version 3.20, to be released on Friday this week.
You
The conversion from MATAIJ to MATDENSE should be very cheap, see
https://gitlab.com/petsc/petsc/-/blob/main/src/mat/impls/dense/seq/dense.c?ref_type=heads#L172
The matrix copy hidden inside createFromMat() is likely more expensive. I am
currently working on a modification of BV that will be
Thanks Jose,
This works indeed. However, I was under the impression that this conversion
might be very costly for big matrices with low sparsity and it would scale with
the number of non-zero values.
Do you have any idea of the efficiency of this operation?
Thanks
> On 29 Aug 2023, at
Ah, there is
https://petsc.org/release/manualpages/Mat/MATSOLVERSPQR/#matsolverspqr See
also https://petsc.org/release/manualpages/Mat/MatGetFactor/#matgetfactor and
https://petsc.org/release/manualpages/Mat/MatQRFactorSymbolic/
> On Aug 29, 2023, at 1:17 PM, Jed Brown wrote:
>
>
Suitesparse includes a sparse QR algorithm. The main issue is that (even with
pivoting) the R factor has the same nonzero structure as a Cholesky factor of
A^T A, which is generally much denser than a factor of A, and this degraded
sparsity impacts Q as well.
I wonder if someone would like to
The result of bv.orthogonalize() is most probably a dense matrix, and the
result replaces the input matrix, that's why the input matrix is required to be
dense.
You can simply do this:
bv = SLEPc.BV().createFromMat(A.convert('dense'))
Jose
> El 29 ago 2023, a las 18:50, Thanasis
Are the nonzero structures of all the rows related? If they are, one could
devise a routine to take advantage of this relationship, but if the nonzero
structures of each row are "randomly" different from all the other rows, then
it is difficult to see how one can take advantage of the
Hi all, I have the following code that orthogonalizes a PETSc matrix. The
problem is that this implementation requires that the PETSc matrix is dense,
otherwise, it fails at bv.SetFromOptions(). Hence the assert in orthogonality().
What could I do in order to be able to orthogonalize sparse
