Cleaning documentation pass in ordering and ILUT

Eigen/src/IterativeLinearSolvers/IncompleteLUT.h

@@ -15,7 +15,7 @@ namespace Eigen {
 
 namespace internal {
     
-/**
+/** \internal
   * Compute a quick-sort split of a vector 
   * On output, the vector row is permuted such that its elements satisfy
   * abs(row(i)) >= abs(row(ncut)) if i<ncut
@@ -60,8 +60,11 @@ int QuickSplit(VectorV &row, VectorI &ind, int ncut)
 }
 
 }// end namespace internal
-/**
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \class IncompleteLUT
   * \brief Incomplete LU factorization with dual-threshold strategy
+  *
   * During the numerical factorization, two dropping rules are used :
   *  1) any element whose magnitude is less than some tolerance is dropped.
   *    This tolerance is obtained by multiplying the input tolerance @p droptol 
@@ -462,4 +465,3 @@ struct solve_retval<IncompleteLUT<_MatrixType>, Rhs>
 } // end namespace Eigen
 
 #endif // EIGEN_INCOMPLETE_LUT_H
-

Eigen/src/OrderingMethods/Amd.h

@@ -86,6 +86,7 @@ Index cs_tdfs(Index j, Index k, Index *head, const Index *next, Index *post, Ind
 
 
 /** \internal
+  * \ingroup OrderingMethods_Module 
   * Approximate minimum degree ordering algorithm.
   * \returns the permutation P reducing the fill-in of the input matrix \a C
   * The input matrix \a C must be a selfadjoint compressed column major SparseMatrix object. Both the upper and lower parts have to be stored, but the diagonal entries are optional.

Eigen/src/OrderingMethods/Ordering.h

@@ -33,13 +33,14 @@ namespace Eigen {
 
 namespace internal {
     
-    /**
-    * Get the symmetric pattern A^T+A from the input matrix A. 
+/** \internal
+  * \ingroup OrderingMethods_Module
+  * \returns the symmetric pattern A^T+A from the input matrix A. 
   * FIXME: The values should not be considered here
   */
-    template<typename MatrixType> 
-    void ordering_helper_at_plus_a(const MatrixType& mat, MatrixType& symmat)
-    {
+template<typename MatrixType> 
+void ordering_helper_at_plus_a(const MatrixType& mat, MatrixType& symmat)
+{
   MatrixType C;
   C = mat.transpose(); // NOTE: Could be  costly
   for (int i = 0; i < C.rows(); i++) 
@@ -48,14 +49,17 @@ namespace internal {
         it.valueRef() = 0.0;
   }
   symmat = C + mat; 
-    }
+}
     
 }
 
-/** 
- * Get the approximate minimum degree ordering
+/** \ingroup OrderingMethods_Module
+  * \class AMDOrdering
+  *
+  * Functor computing the \em approximate \em minimum \em degree ordering
   * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
   * \tparam  Index The type of indices of the matrix 
+  * \sa COLAMDOrdering
   */
 template <typename Index>
 class AMDOrdering
@@ -90,10 +94,12 @@ class AMDOrdering
     }
 };
 
-/** 
- * Get the natural ordering
+/** \ingroup OrderingMethods_Module
+  * \class NaturalOrdering
   *
- *NOTE Returns an empty permutation matrix
+  * Functor computing the natural ordering (identity)
+  * 
+  * \note Returns an empty permutation matrix
   * \tparam  Index The type of indices of the matrix 
   */
 template <typename Index>
@@ -111,20 +117,19 @@ class NaturalOrdering
     
 };
 
-/** 
- * Get the column approximate minimum degree ordering 
+/** \ingroup OrderingMethods_Module
+  * \class COLAMDOrdering
+  *
+  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
   * The matrix should be in column-major format
   */
-template<typename Index>
-class COLAMDOrdering; 
-#include "Eigen_Colamd.h"
-
 template<typename Index>
 class COLAMDOrdering
 {
   public:
     typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; 
     typedef Matrix<Index, Dynamic, 1> IndexVector;
+    
     /** Compute the permutation vector form a sparse matrix */
     template <typename MatrixType>
     void operator() (const MatrixType& mat, PermutationType& perm)
@@ -149,10 +154,9 @@ class COLAMDOrdering
       
       perm.resize(n);
       for (int i = 0; i < n; i++) perm.indices()(p(i)) = i;
-        
     }
- 
 };
 
 } // end namespace Eigen
+
 #endif