Hi, I'm interested in this as well. Could you post or link to your 
solution? Thanks.


On Friday, 19 February 2016 17:21:22 UTC+1, Michael Harmon wrote:
>
> If you want to use Trilinos, I refactored their direct solver so that it 
> can be used the way you want, I will submit the patch, but if you want can 
> send you the changes as well.
>
>
> On Tuesday, December 1, 2015 at 4:05:29 AM UTC-6, Pascal Kraft wrote:
>>
>> Hello Wolfgang,
>>
>> does the same hold true for other direct solvers? I would like to use 
>> Mumps (PETSc) to get a LU-factorization and reuse that a couple of times. 
>> The PETSc wrappers only provide a constructor and solve. If I reuse solve 
>> withe the same matrix argument, will the factorization be done again or 
>> does it detect that the inverse is already present? The code for both the 
>> PETSc wrappers as well as the Trilinos Wrappers does not look as if that 
>> was supported.
>>
>> Greetings,
>> Pascal Kraft
>>
>> Am Sonntag, 4. Oktober 2015 20:14:30 UTC+2 schrieb bangerth:
>>>
>>>
>>> Zhen, 
>>>
>>> Jean-Paul already gave the correct answer, namely use a sparse direct 
>>> solver 
>>> and factorize only once. 
>>>
>>> > Therefore my following questions are: 
>>> > 
>>> > What is the limit of the size of matrix system can I do a lapack LU 
>>> > factorization on a single machine? 
>>>
>>> For a sparse direct solver, I think you can probably go to around 1M 
>>> unknowns. 
>>> If you use a LAPACK matrix, you will quickly be limited by memory. For 
>>> example, a 10,000 x 10,000 matrix already requires almost a GB of 
>>> memory. 
>>>
>>>
>>> > Are there any better way to get LU factorization in deal.ii? 
>>> > Can we incorporate "history experience" to achieve more efficient 
>>> solvers? 
>>>
>>> People have tried that and there is a substantial amount of literature 
>>> on the 
>>> topic of Krylov subspace recycling. I always think that it is a great 
>>> idea and 
>>> want to investigate using it, but it has not found its way into 
>>> mainstream 
>>> applications to the best of my knowledge. 
>>>
>>> Best 
>>>   W. 
>>>
>>> -- 
>>> ------------------------------------------------------------------------ 
>>> Wolfgang Bangerth               email:            bang...@math.tamu.edu 
>>>                                  www: 
>>> http://www.math.tamu.edu/~bangerth/ 
>>>
>>>

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