Hi Soufiane,
> My mistake is from a misunderstanding of the documentation. I read that:
>
> Conjugate Gradient (CG) symmetric positive definite
> Stabilized Bi-CG (BiCGStab) non-symmetric
> Generalized Minimum Residual (GMRES) general
>
> Like "General A" NxM, my is for any kind of square-matrice
Hello Karl,
My mistake is from a misunderstanding of the documentation. I read that:
Conjugate Gradient (CG) symmetric positive definite
Stabilized Bi-CG (BiCGStab) non-symmetric
Generalized Minimum Residual (GMRES) general
Like "General A" NxM, my is for any kind of square-matrices.
I have alr
Hi Soufiane,
from your description it seems to me that you want to solve a
least-squares problem, while the GMRES implementation in ViennaCL is for
square systems. I suggest you use a QR-factorization as outlined in
examples/tutorial/least-squares.cpp.
If you have any good pointers for GMRES a
Hello,
I am trying to solve Ax=b
With a Dense and Rectangular (14000 Rows and 140 Cols).
I use as presented on the sample/documentation:
viennacl::vector< Scalar > _b( m_RowsCount );
viennacl::vector< Scalar > _x( m_ColumnsCount );
viennacl::fast_copy( b.Datas, b.Datas + m_RowsCount, _b.begin(
