Hello all,

thank you Benoit but I was hoping to get an answer specific to the eigen 
library. In my project a end-user friendly solution is necessary. I can not 
program anything on my own that is much too time consuming. If there is a 
solver that can be applied to many different matrices with the sparsity that I 
described above and can be either parallelized, multithreaded, or GPU 
"accelerated" I would prefer to apply this solver. Is there anything in like 
this in the eigen library? Can your version of LSQR be used for this purpose?

Best Joerg
________________________________
From: Benoit Bardet <[email protected]>
Sent: Tuesday, September 3, 2019 11:12 AM
To: [email protected] <[email protected]>
Subject: Re: [eigen] Best Solver for Large but Sparse Linear System


        External Email - Use Caution

Hi Joerg,

It depends on your matrix condition factor (not sure of the name in english).
A way I like for bad condition number is IncompleteLUT preconditionner with 
GMRES. Try different fill factor for ILUT.

For better matrix, UMFPACK is very efficient. I'm not sure it is in eigen. 
Maybe with Diagonal preconditionner.
You can use CG as well with Diag precond.

This page gives example to make a first benchmarking (without precond). 
https://eigen.tuxfamily.org/dox/group__MatrixfreeSolverExample.html
But do the benchmark on a real matrix, not a random one. And benchmark in time 
and not in iterations.


I used GMRES for 8e6 by 8e6 matrix with about 10 non Zero as well per line 
(Finite Element with bad meshes). Be careful with memory.


Regards.





Le mar. 3 sept. 2019 à 16:52, Pfannmoeller, Joerg Peter 
<[email protected]<mailto:[email protected]>> a écrit :
Hello Eigen-Experts,

I have a linear system with a matrix size of about 2E+06 x 2E+06. The maximum 
number of non-zero matrix elements in each row is 10. Which solver would you 
recommend for this system?

Best Joerg

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