1 files changed, 0 insertions, 154 deletions
diff --git a/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h b/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h
deleted file mode 100644
index 4e06097849..0000000000
--- a/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h
+++ /dev/null
@@ -1,154 +0,0 @@
- 
-// 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>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_ORDERING_H
-#define EIGEN_ORDERING_H
-
-namespace Eigen {
-  
-#include "Eigen_Colamd.h"
-
-namespace internal {
-    
-/** \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)
-{
-  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; 
-}
-    
-}
-
-#ifndef EIGEN_MPL2_ONLY
-
-/** \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
-{
-  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; C = mat;
-      
-      // Call the AMD routine 
-      // m_mat.prune(keep_diag()); //Remove the diagonal elements 
-      internal::minimum_degree_ordering(C, perm);
-    }
-};
-
-#endif // EIGEN_MPL2_ONLY
-
-/** \ingroup OrderingMethods_Module
-  * \class NaturalOrdering
-  *
-  * Functor computing the natural ordering (identity)
-  * 
-  * \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); 
-    }
-    
-};
-
-/** \ingroup OrderingMethods_Module
-  * \class COLAMDOrdering
-  *
-  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
-  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
-  */
-template<typename Index>
-class COLAMDOrdering
-{
-  public:
-    typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; 
-    typedef Matrix<Index, Dynamic, 1> IndexVector;
-    
-    /** Compute the permutation vector \a perm form the sparse matrix \a mat
-      * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
-      */
-    template <typename MatrixType>
-    void operator() (const MatrixType& mat, PermutationType& perm)
-    {
-      eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
-      
-      Index m = mat.rows();
-      Index n = mat.cols();
-      Index nnz = mat.nonZeros();
-      // Get the recommended value of Alen to be used by colamd
-      Index Alen = internal::colamd_recommended(nnz, m, n); 
-      // Set the default parameters
-      double knobs [COLAMD_KNOBS]; 
-      Index stats [COLAMD_STATS];
-      internal::colamd_set_defaults(knobs);
-      
-      Index info;
-      IndexVector p(n+1), A(Alen); 
-      for(Index i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
-      for(Index i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
-      // Call Colamd routine to compute the ordering 
-      info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats); 
-      eigen_assert( info && "COLAMD failed " );
-      
-      perm.resize(n);
-      for (Index i = 0; i < n; i++) perm.indices()(p(i)) = i;
-    }
-};
-
-} // end namespace Eigen
-
-#endif