To: Pierre Jolivet
> Cc: Lucas Banting ; petsc-users@mcs.anl.gov
>
> Subject: Re: [petsc-users] Preconditioner for LSQR
>
> Caution: This message was sent from outside the University of Manitoba.
>
>
> To use SLEPc's TSQR one would do something like this:
>
> i
the
resulting solution to produce the next right hand side vector.
Thanks again,
Lucas
From: Jose E. Roman
Sent: Tuesday, March 1, 2022 4:49 AM
To: Pierre Jolivet
Cc: Lucas Banting ; petsc-users@mcs.anl.gov
Subject: Re: [petsc-users] Preconditioner for LSQR
To use SLEPc's TSQR one would do something like this:
ierr = BVCreateFromMat(A,);CHKERRQ(ierr);
ierr = BVSetFromOptions(X);CHKERRQ(ierr);
ierr =
BVSetOrthogonalization(X,BV_ORTHOG_CGS,BV_ORTHOG_REFINE_IFNEEDED,PETSC_DEFAULT,BV_ORTHOG_BLOCK_TSQR);CHKERRQ(ierr);
ierr =
Hello Lucas,
In your sequence of systems, is A changing?
Are all right-hand sides available from the get-go?
In that case, you can solve everything in a block fashion and that’s how you
could get real improvements.
Also, instead of PCCHOLESKY on A^T * A + KSPCG, you could use PCQR on A +
This is a small problem for which direct Householder QR may be fast enough
(depending on the rest of your application). For multi-node, you can use TSQR
(backward stable like Householder) or Cholesky (unstable).
julia> A = rand(20, 200);
julia> @time Q, R = qr(A);
0.866989 seconds (14
On Mon, Feb 28, 2022 at 6:54 PM Lucas Banting
wrote:
> Hello,
>
> I have an MPIDENSE matrix of size about 200,000 x 200, using KSPLSQR on my
> machine a solution takes about 15 s. I typically run with six to eight
> processors.
> I have to solve the system several times, typically 4-30, and was
Hello,
I have an MPIDENSE matrix of size about 200,000 x 200, using KSPLSQR on my
machine a solution takes about 15 s. I typically run with six to eight
processors.
I have to solve the system several times, typically 4-30, and was looking for
recommendations on reusable preconditioners to use
