aboutsummaryrefslogtreecommitdiffhomepage
path: root/Eigen/OrderingMethods
diff options
context:
space:
mode:
authorGravatar Desire NUENTSA <desire.nuentsa_wakam@inria.fr>2013-01-21 15:37:47 +0100
committerGravatar Desire NUENTSA <desire.nuentsa_wakam@inria.fr>2013-01-21 15:37:47 +0100
commitd2dd5063b6f52e16e15790eac7c9d1f57a60c96f (patch)
treea1cbf21f6acc902fd8cf1d2954b9fd48ed5be233 /Eigen/OrderingMethods
parent5b9bb0026512d078ca06ff8029abf499da2e66d9 (diff)
Documentation for the ordering methods
Diffstat (limited to 'Eigen/OrderingMethods')
-rw-r--r--Eigen/OrderingMethods46
1 files changed, 43 insertions, 3 deletions
diff --git a/Eigen/OrderingMethods b/Eigen/OrderingMethods
index 8abc128b9..423cfb0cd 100644
--- a/Eigen/OrderingMethods
+++ b/Eigen/OrderingMethods
@@ -8,12 +8,52 @@
/**
* \defgroup OrderingMethods_Module OrderingMethods module
*
- * This module is currently for internal use only.
- *
- *
+ * This module is currently for internal use only
+ *
+ * It defines various built-in and external ordering methods for sparse matrices.
+ * They are typically used to reduce the number of elements during
+ * the sparse matrix decomposition (LLT, LU, QR).
+ * Precisely, in a preprocessing step, a permutation matrix P is computed using
+ * those ordering methods and applied to the columns of the matrix.
+ * Using for instance the sparse Cholesky decomposition, it is expected that
+ * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
+ *
+ *
+ * Usage :
* \code
* #include <Eigen/OrderingMethods>
* \endcode
+ *
+ * A simple usage is as a template parameter in the sparse decomposition classes :
+ *
+ * \code
+ * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * \code
+ * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * It is possible as well to call directly a particular ordering method for your own purpose,
+ * \code
+ * AMDOrdering<int> ordering;
+ * PermutationMatrix<Dynamic, Dynamic, int> perm;
+ * SparseMatrix<double> A;
+ * //Fill the matrix ...
+ *
+ * ordering(A, perm); // Call AMD
+ * \endcode
+ *
+ * \note Some of these methods (like AMD or METIS), need the sparsity pattern
+ * of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
+ * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
+ * If your matrix is already symmetric (at leat in structure), you can avoid that
+ * by calling the method with a SelfAdjointView type.
+ *
+ * \code
+ * // Call the ordering on the pattern of the lower triangular matrix A
+ * ordering(A.selfadjointView<Lower>(), perm);
+ * \endcode
*/
#include "src/OrderingMethods/Amd.h"