Further question about PC with Jaocbi Row Sum

[Barry Smith]

    the row sum option assumes that all the entries of the matrix are  
positive; this is true to linear elements
and mass matrices. If you have negative entries in your mass matrix  
then I would not trust any kind of
mass lumping as a preconditioner.

    Barry

On Apr 10, 2008, at 12:04 AM, Shi Jin wrote:
>
> Thank you. I have used the -ksp_converged_reason option.
> The result says:
> Linear solve did not converge due to DIVERGED_INDEFINITE_PC  
> iterations 2
> I then further checked the row sum matrix, it has negative  
> eigenvalues.
> So I guess it does not work at all.
> Thank you all for your help.
>
> --
> Shi Jin, PhD
>
> ----- Original Message ----
>> From: Matthew Knepley <knepley at gmail.com>
>> To: petsc-users at mcs.anl.gov
>> Sent: Wednesday, April 9, 2008 2:50:29 PM
>> Subject: Re: Further question about PC with Jaocbi Row Sum
>>
>> On Wed, Apr 9, 2008 at 3:25 PM, Shi Jin  wrote:
>>> Thank you very much.
>>>
>>>
>>>
>>>>> Is there something particular about this rowsum method?
>>>>
>>>>    No. If you use a -ksp_rtol of 1.e-12 and still get different
>>>> answers, this needs to be investigated.
>>>>
>>>>
>>>
>>> I have tried even with -ksp_rtol   1.e-20 but still got different  
>>> results.
>>>
>>> Here is what I got when solving the mass matrix with
>>>
>>> -pc_type  jacobi
>>> -pc_jacobi_rowsum 1
>>> -ksp_type cg
>>> -sub_pc_type icc
>>> -ksp_rtol 1.e-20
>>> -ksp_monitor
>>> -ksp_view
>>>
>>>  0 KSP Residual norm 2.975203858623e+00
>>>  1 KSP Residual norm 2.674371671721e-01
>>>  2 KSP Residual norm 1.841074927355e-01
>>> KSP Object:
>>>  type: cg
>>>  maximum iterations=10000, initial guess is zero
>>>  tolerances:  relative=1e-20, absolute=1e-50, divergence=10000
>>>  left preconditioning
>>> PC Object:
>>>  type: jacobi
>>>  linear system matrix = precond matrix:
>>>  Matrix Object:
>>>    type=seqaij, rows=8775, cols=8775
>>>    total: nonzeros=214591, allocated nonzeros=214591
>>>      not using I-node routines
>>>
>>> I realize that the iteration ended when the residual norm is quite  
>>> large.
>>> Do you think this indicates something wrong here?
>>
>> Can you run with
>>
>>  -ksp_converged_reason
>>
>> It appears that the solve fails rather than terminates with an  
>> answer. Is it
>> possible that your matrix is not SPD?
>>
>>  Matt
>>
>>> Thank you again.
>>>
>>> Shi
>>>
>>>
>>>
>>> __________________________________________________
>>> Do You Yahoo!?
>>> Tired of spam?  Yahoo! Mail has the best spam protection around
>>> http://mail.yahoo.com
>>>
>>>
>>
>>
>>
>> -- 
>> 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
>>
>>
>
>
>
> __________________________________________________
> Do You Yahoo!?
> Tired of spam?  Yahoo! Mail has the best spam protection around
> http://mail.yahoo.com
>