On Sun, Mar 11, 2018 at 1:14 AM, Smith, Barry F. <bsm...@mcs.anl.gov> wrote:

>
>   1) Run the problem with -ksp_view_mat and -ksp_view_rhs and mail
> petsc-ma...@mcs.anl.gov  the resulting file produced called binaryoutput
>
>    2) By default PCLU does a reordering to reduce fill that could
> introduce a zero pivoit, PCILU does not do a reordering by default. You can
> use -pc_factor_mat_ordering_type none to force no reordering (PCLU does not
> do numerical pivoting for stability so can fail with zero pivots).
>
>    3) If you need to solve these tiny 7 by 7 systems many times
> (presumably you are solving these to set up a large algebraic system solved
> afterwards) then you probably don't want to use KSP to solve them. You can
> use the low level kernel PetscKernel_A_gets_inverse_A_7() that does do
> pivoting followed by a multiply like
>
> s1 = v[0]*x1 + v[6]*x2  + v[12]*x3 + v[18]*x4 + v[24]*x5 + v[30]*x6;
>       s2 = v[1]*x1 + v[7]*x2  + v[13]*x3 + v[19]*x4 + v[25]*x5 + v[31]*x6;
>       s3 = v[2]*x1 + v[8]*x2  + v[14]*x3 + v[20]*x4 + v[26]*x5 + v[32]*x6;
>       s4 = v[3]*x1 + v[9]*x2  + v[15]*x3 + v[21]*x4 + v[27]*x5 + v[33]*x6;
>       s5 = v[4]*x1 + v[10]*x2 + v[16]*x3 + v[22]*x4 + v[28]*x5 + v[34]*x6;
>       s6 = v[5]*x1 + v[11]*x2 + v[17]*x3 + v[23]*x4 + v[29]*x5 + v[35]*x6;
>
> where v is the dense 7 by 7 matrix (stored column oriented like Fortran)
> an the x are the seven values of the right hand side.
>

Note that PCPBJACOBI will do this automatically.

   Matt


>    Barry
>
>
>
>
>
> > On Mar 10, 2018, at 5:22 AM, Ali Berk Kahraman <
> aliberkkahra...@yahoo.com> wrote:
> >
> > Hello All,
> >
> > I am trying to get the finite difference coefficients for a given
> irregular grid. For this, I follow the following webpage, which tells me to
> solve a linear system.
> >
> > http://web.media.mit.edu/~crtaylor/calculator.html
> >
> > I solve a 7 unknown linear system with a 7x7 dense matrix to get the
> finite difference coefficients. Since I will call this code many many many
> times in my overall project, I need it to be as fast, yet as exact as
> possible. So I use PCLU. I make sure that there are no zero diagonals on
> the matrix, I swap required rows for it. However, PCLU still diverges with
> the output at the end of this e-mail. It indicates
> "FACTOR_NUMERIC_ZEROPIVOT" , but as I have written above I make sure there
> are no zero main diagonal entries on the matrix. When I use PCILU instead,
> it converges pretty well.
> >
> > So my question is, is PCILU the same thing mathematically as PCLU when
> applied on a small dense matrix? I need to know if I get the exact solution
> with PCILU, because my whole project will depend on the accuracy of the
> finite differences.
> >
> > Best Regards,
> >
> > Ali Berk Kahraman
> > M.Sc. Student, Mechanical Engineering Dept.
> > Boğaziçi Uni., Istanbul, Turkey
> >
> > Linear solve did not converge due to DIVERGED_PCSETUP_FAILED iterations 0
> >                PCSETUP_FAILED due to FACTOR_NUMERIC_ZEROPIVOT
> > KSP Object: 1 MPI processes
> >   type: gmres
> >     restart=30, using Classical (unmodified) Gram-Schmidt
> Orthogonalization with no iterative refinement
> >     happy breakdown tolerance 1e-30
> >   maximum iterations=10000, initial guess is zero
> >   tolerances:  relative=1e-05, absolute=1e-50, divergence=10000.
> >   left preconditioning
> >   using PRECONDITIONED norm type for convergence test
> > PC Object: 1 MPI processes
> >   type: lu
> >     out-of-place factorization
> >     tolerance for zero pivot 2.22045e-14
> >     matrix ordering: nd
> >     factor fill ratio given 5., needed 1.
> >       Factored matrix follows:
> >         Mat Object: 1 MPI processes
> >           type: seqaij
> >           rows=7, cols=7
> >           package used to perform factorization: petsc
> >           total: nonzeros=49, allocated nonzeros=49
> >           total number of mallocs used during MatSetValues calls =0
> >             using I-node routines: found 2 nodes, limit used is 5
> >   linear system matrix = precond matrix:
> >   Mat Object: 1 MPI processes
> >     type: seqaij
> >     rows=7, cols=7
> >     total: nonzeros=49, allocated nonzeros=49
> >     total number of mallocs used during MatSetValues calls =0
> >       using I-node routines: found 2 nodes, limit used is 5
> >
> >
>
>


-- 
What most experimenters take for granted before they begin their
experiments is infinitely more interesting than any results to which their
experiments lead.
-- Norbert Wiener

https://www.cse.buffalo.edu/~knepley/ <http://www.caam.rice.edu/~mk51/>

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