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 19:13,
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:
>
> Suite
Well - you sent in libmesh log not petsc's configure.log/make.log for petsc-3.17
Anyway - with petsc-3.13 - you have:
Matlab:
Includes: -I/usr/local/MATLAB/R2020b/extern/include
/usr/local/MATLAB/R2020b
MatlabEngine:
Library:
-Wl,-rpath,/usr/local/MATLAB/R2020b/sys/os/glnxa64:/usr/l
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 c
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 Boutsikakis
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 sparsit
On Tue, Aug 29, 2023 at 9:08 AM Satish Balay via petsc-users <
petsc-users@mcs.anl.gov> wrote:
> Send configure.log, make.log from both petsc-3.13 and 3.17 [or 3.19].
>
> [you can gzip them to make the logs friendly to mailing list - or send
> them to petsc-maint]
>
> And does test suite work with
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 matr
Send configure.log, make.log from both petsc-3.13 and 3.17 [or 3.19].
[you can gzip them to make the logs friendly to mailing list - or send them to
petsc-maint]
And does test suite work with 3.17? [or 3.19?]
Satish
On Tue, 29 Aug 2023, INTURU SRINIVAS 20PHD0548 via petsc-users wrote:
> I am
