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| author |  Desire NUENTSA <desire.nuentsa_wakam@inria.fr> | 2012-09-25 11:55:33 +0200 |
|---|---|---|
| committer | Desire NUENTSA <desire.nuentsa_wakam@inria.fr> | 2012-09-25 11:55:33 +0200 |
| commit | 357fe3641d696b0d7b878ca2d4e1b45639dff5f8 (patch) | |
| tree | 9b1795271c425e8fa3d6c74090e7167f966753fc /Eigen | |
| parent | 15a9f6b9c1d2673f5c319c826794c4bce0282696 (diff) | |
Correct reference to iterative scaling method
Diffstat (limited to 'Eigen')
| -rw-r--r-- | Eigen/src/SparseLU/SparseLU.h | 3 |
1 files changed, 2 insertions, 1 deletions
diff --git a/Eigen/src/SparseLU/SparseLU.h b/Eigen/src/SparseLU/SparseLU.h index 6f4458a26..9ea121ce5 100644 --- a/Eigen/src/SparseLU/SparseLU.h +++ b/Eigen/src/SparseLU/SparseLU.h @@ -60,7 +60,8 @@ namespace Eigen { * * \NOTE Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. - * If this is the case for your matrices, you can try the basic scaling method in \ref Scaling. + * If this is the case for your matrices, you can try the basic scaling method at + * "unsupported/Eigen/src/IterativeSolvers/Scaling.h" * * \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<> * \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS |