merge Sparse LU branch

Eigen/src/OrderingMethods/Ordering.h

@@ -0,0 +1,156 @@
+ 
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_ORDERING_H
+#define EIGEN_ORDERING_H
+
+#include "Amd.h"
+#include "Eigen_Colamd.h"
+namespace Eigen {
+namespace internal {
+    
+    /**
+    * Get 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)
+    {
+      MatrixType C;
+      C = mat.transpose(); // NOTE: Could be  costly
+      for (int i = 0; i < C.rows(); i++) 
+      {
+          for (typename MatrixType::InnerIterator it(C, i); it; ++it)
+            it.valueRef() = 0.0;
+      }
+      symmat = C + mat; 
+    }
+    
+}
+
+/** 
+ * Get the approximate minimum 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 
+ */
+template <typename Index>
+class AMDOrdering
+{
+  public:
+    typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
+    
+    /** Compute the permutation vector from a sparse matrix
+     * This routine is much faster if the input matrix is column-major     
+     */
+    template <typename MatrixType>
+    void operator()(const MatrixType& mat, PermutationType& perm)
+    {
+      // Compute the symmetric pattern
+      SparseMatrix<typename MatrixType::Scalar, ColMajor, Index> symm;
+      internal::ordering_helper_at_plus_a(mat,symm); 
+    
+      // Call the AMD routine 
+      //m_mat.prune(keep_diag());
+      internal::minimum_degree_ordering(symm, perm);
+    }
+    
+    /** Compute the permutation with a selfadjoint matrix */
+    template <typename SrcType, unsigned int SrcUpLo> 
+    void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
+    { 
+      SparseMatrix<typename SrcType::Scalar, ColMajor, Index> C = mat;
+      
+      // Call the AMD routine 
+      // m_mat.prune(keep_diag()); //Remove the diagonal elements 
+      internal::minimum_degree_ordering(C, perm);
+    }
+};
+
+/** 
+ * Get the natural ordering
+ * 
+ *NOTE Returns an empty permutation matrix
+ * \tparam  Index The type of indices of the matrix 
+ */
+template <typename Index>
+class NaturalOrdering
+{
+  public:
+    typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
+    
+    /** Compute the permutation vector from a column-major sparse matrix */
+    template <typename MatrixType>
+    void operator()(const MatrixType& mat, PermutationType& perm)
+    {
+      perm.resize(0); 
+    }
+    
+};
+
+/** 
+ * Get the column approximate minimum 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)
+    {
+        int m = mat.rows();
+        int n = mat.cols();
+        int nnz = mat.nonZeros();
+        // Get the recommended value of Alen to be used by colamd
+        int Alen = eigen_colamd_recommended(nnz, m, n); 
+        // Set the default parameters
+        double knobs [EIGEN_COLAMD_KNOBS]; 
+        int stats [EIGEN_COLAMD_STATS];
+        eigen_colamd_set_defaults(knobs);
+        
+        int info;
+        IndexVector p(n+1), A(Alen); 
+        for(int i=0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
+        for(int i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
+        // Call Colamd routine to compute the ordering 
+        info = eigen_colamd(m, n, Alen, A.data(), p.data(), knobs, stats); 
+        eigen_assert( info && "COLAMD failed " );
+        
+        perm.resize(n);
+        for (int i = 0; i < n; i++) perm.indices()(p(i)) = i;
+        
+    }
+ 
+};
+
+} // end namespace Eigen
+#endif