Remove old stuff with Eigen

1 files changed, 0 insertions, 678 deletions

diff --git a/src/Eigen/src/PaStiXSupport/PaStiXSupport.h b/src/Eigen/src/PaStiXSupport/PaStiXSupport.h
deleted file mode 100644
index 3742687..0000000
--- a/src/Eigen/src/PaStiXSupport/PaStiXSupport.h
+++ /dev/null
@@ -1,678 +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_PASTIXSUPPORT_H
-#define EIGEN_PASTIXSUPPORT_H
-
-namespace Eigen { 
-
-#if defined(DCOMPLEX)
-  #define PASTIX_COMPLEX  COMPLEX
-  #define PASTIX_DCOMPLEX DCOMPLEX
-#else
-  #define PASTIX_COMPLEX  std::complex<float>
-  #define PASTIX_DCOMPLEX std::complex<double>
-#endif
-
-/** \ingroup PaStiXSupport_Module
-  * \brief Interface to the PaStix solver
-  * 
-  * This class is used to solve the linear systems A.X = B via the PaStix library. 
-  * The matrix can be either real or complex, symmetric or not.
-  *
-  * \sa TutorialSparseDirectSolvers
-  */
-template<typename _MatrixType, bool IsStrSym = false> class PastixLU;
-template<typename _MatrixType, int Options> class PastixLLT;
-template<typename _MatrixType, int Options> class PastixLDLT;
-
-namespace internal
-{
-    
-  template<class Pastix> struct pastix_traits;
-
-  template<typename _MatrixType>
-  struct pastix_traits< PastixLU<_MatrixType> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-
-  template<typename _MatrixType, int Options>
-  struct pastix_traits< PastixLDLT<_MatrixType,Options> >
-  {
-    typedef _MatrixType MatrixType;
-    typedef typename _MatrixType::Scalar Scalar;
-    typedef typename _MatrixType::RealScalar RealScalar;
-    typedef typename _MatrixType::StorageIndex StorageIndex;
-  };
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-  
-  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
-  {
-    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
-    if (nbrhs == 0) {x = NULL; nbrhs=1;}
-    z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm); 
-  }
-
-  // Convert the matrix  to Fortran-style Numbering
-  template <typename MatrixType>
-  void c_to_fortran_numbering (MatrixType& mat)
-  {
-    if ( !(mat.outerIndexPtr()[0]) ) 
-    { 
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        ++mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        ++mat.innerIndexPtr()[i];
-    }
-  }
-  
-  // Convert to C-style Numbering
-  template <typename MatrixType>
-  void fortran_to_c_numbering (MatrixType& mat)
-  {
-    // Check the Numbering
-    if ( mat.outerIndexPtr()[0] == 1 ) 
-    { // Convert to C-style numbering
-      int i;
-      for(i = 0; i <= mat.rows(); ++i)
-        --mat.outerIndexPtr()[i];
-      for(i = 0; i < mat.nonZeros(); ++i)
-        --mat.innerIndexPtr()[i];
-    }
-  }
-}
-
-// This is the base class to interface with PaStiX functions. 
-// Users should not used this class directly. 
-template <class Derived>
-class PastixBase : public SparseSolverBase<Derived>
-{
-  protected:
-    typedef SparseSolverBase<Derived> Base;
-    using Base::derived;
-    using Base::m_isInitialized;
-  public:
-    using Base::_solve_impl;
-    
-    typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType;
-    typedef _MatrixType MatrixType;
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef Matrix<Scalar,Dynamic,1> Vector;
-    typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
-    enum {
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    
-  public:
-    
-    PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0)
-    {
-      init();
-    }
-    
-    ~PastixBase() 
-    {
-      clean();
-    }
-    
-    template<typename Rhs,typename Dest>
-    bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
-    
-    /** Returns a reference to the integer vector IPARM of PaStiX parameters
-      * to modify the default parameters. 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<StorageIndex,IPARM_SIZE,1>& iparm()
-    {
-      return m_iparm; 
-    }
-    
-    /** Return a reference to a particular index parameter of the IPARM vector 
-     * \sa iparm()
-     */
-    
-    int& iparm(int idxparam)
-    {
-      return m_iparm(idxparam);
-    }
-    
-     /** Returns a reference to the double vector DPARM of PaStiX parameters 
-      * The statistics related to the different phases of factorization and solve are saved here as well
-      * \sa analyzePattern() factorize()
-      */
-    Array<double,DPARM_SIZE,1>& dparm()
-    {
-      return m_dparm; 
-    }
-    
-    
-    /** Return a reference to a particular index parameter of the DPARM vector 
-     * \sa dparm()
-     */
-    double& dparm(int idxparam)
-    {
-      return m_dparm(idxparam);
-    }
-    
-    inline Index cols() const { return m_size; }
-    inline Index rows() const { return m_size; }
-    
-     /** \brief Reports whether previous computation was successful.
-      *
-      * \returns \c Success if computation was successful,
-      *          \c NumericalIssue if the PaStiX reports a problem
-      *          \c InvalidInput if the input matrix is invalid
-      *
-      * \sa iparm()          
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return m_info;
-    }
-    
-  protected:
-
-    // Initialize the Pastix data structure, check the matrix
-    void init(); 
-    
-    // Compute the ordering and the symbolic factorization
-    void analyzePattern(ColSpMatrix& mat);
-    
-    // Compute the numerical factorization
-    void factorize(ColSpMatrix& mat);
-    
-    // Free all the data allocated by Pastix
-    void clean()
-    {
-      eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); 
-      m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
-      m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
-      internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                             m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-    }
-    
-    void compute(ColSpMatrix& mat);
-    
-    int m_initisOk; 
-    int m_analysisIsOk;
-    int m_factorizationIsOk;
-    mutable ComputationInfo m_info; 
-    mutable pastix_data_t *m_pastixdata; // Data structure for pastix
-    mutable int m_comm; // The MPI communicator identifier
-    mutable Array<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
-    mutable Array<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
-    mutable Matrix<StorageIndex,Dynamic,1> m_perm;  // Permutation vector
-    mutable Matrix<StorageIndex,Dynamic,1> m_invp;  // Inverse permutation vector
-    mutable int m_size; // Size of the matrix 
-}; 
-
- /** Initialize the PaStiX data structure. 
-   *A first call to this function fills iparm and dparm with the default PaStiX parameters
-   * \sa iparm() dparm()
-   */
-template <class Derived>
-void PastixBase<Derived>::init()
-{
-  m_size = 0; 
-  m_iparm.setZero(IPARM_SIZE);
-  m_dparm.setZero(DPARM_SIZE);
-  
-  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
-  pastix(&m_pastixdata, MPI_COMM_WORLD,
-         0, 0, 0, 0,
-         0, 0, 0, 1, m_iparm.data(), m_dparm.data());
-  
-  m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
-  m_iparm[IPARM_VERBOSE]             = API_VERBOSE_NOT;
-  m_iparm[IPARM_ORDERING]            = API_ORDER_SCOTCH;
-  m_iparm[IPARM_INCOMPLETE]          = API_NO;
-  m_iparm[IPARM_OOC_LIMIT]           = 2000;
-  m_iparm[IPARM_RHS_MAKING]          = API_RHS_B;
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
-  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
-                         0, 0, 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER)) {
-    m_info = InvalidInput;
-    m_initisOk = false;
-  }
-  else { 
-    m_info = Success;
-    m_initisOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::compute(ColSpMatrix& mat)
-{
-  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
-  
-  analyzePattern(mat);  
-  factorize(mat);
-  
-  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
-}
-
-
-template <class Derived>
-void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat)
-{                         
-  eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
-  
-  // clean previous calls
-  if(m_size>0)
-    clean();
-  
-  m_size = internal::convert_index<int>(mat.rows());
-  m_perm.resize(m_size);
-  m_invp.resize(m_size);
-  
-  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
-  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_analysisIsOk = false;
-  }
-  else
-  { 
-    m_info = Success;
-    m_analysisIsOk = true;
-  }
-}
-
-template <class Derived>
-void PastixBase<Derived>::factorize(ColSpMatrix& mat)
-{
-//   if(&m_cpyMat != &mat) m_cpyMat = mat;
-  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
-  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
-  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
-  m_size = internal::convert_index<int>(mat.rows());
-  
-  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
-               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
-  
-  // Check the returned error
-  if(m_iparm(IPARM_ERROR_NUMBER))
-  {
-    m_info = NumericalIssue;
-    m_factorizationIsOk = false;
-    m_isInitialized = false;
-  }
-  else
-  {
-    m_info = Success;
-    m_factorizationIsOk = true;
-    m_isInitialized = true;
-  }
-}
-
-/* Solve the system */
-template<typename Base>
-template<typename Rhs,typename Dest>
-bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
-{
-  eigen_assert(m_isInitialized && "The matrix should be factorized first");
-  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
-                     THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
-  int rhs = 1;
-  
-  x = b; /* on return, x is overwritten by the computed solution */
-  
-  for (int i = 0; i < b.cols(); i++){
-    m_iparm[IPARM_START_TASK]          = API_TASK_SOLVE;
-    m_iparm[IPARM_END_TASK]            = API_TASK_REFINE;
-  
-    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
-                           m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
-  }
-  
-  // Check the returned error
-  m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue;
-  
-  return m_iparm(IPARM_ERROR_NUMBER)==0;
-}
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLU
-  * \brief Sparse direct LU solver based on PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B with a supernodal LU 
-  * factorization in the PaStiX library. The matrix A should be squared and nonsingular
-  * PaStiX requires that the matrix A has a symmetric structural pattern. 
-  * This interface can symmetrize the input matrix otherwise. 
-  * The vectors or matrices X and B can be either dense or sparse.
-  * 
-  * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
-  * NOTE : Note that if the analysis and factorization phase are called separately, 
-  * the input matrix will be symmetrized at each call, hence it is advised to 
-  * symmetrize the matrix in a end-user program and set \p IsStrSym to true
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SparseLU
-  * 
-  */
-template<typename _MatrixType, bool IsStrSym>
-class PastixLU : public PastixBase< PastixLU<_MatrixType> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLU<MatrixType> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    
-  public:
-    PastixLU() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLU(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-    /** Compute the LU supernodal factorization of \p matrix. 
-      * iparm and dparm can be used to tune the PaStiX parameters. 
-      * see the PaStiX user's manual
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-    /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. 
-      * Several ordering methods can be used at this step. See the PaStiX user's manual. 
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      m_structureIsUptodate = false;
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-
-    /** Compute the LU supernodal factorization of \p matrix
-      * WARNING The matrix \p matrix should have the same structural pattern 
-      * as the same used in the analysis phase.
-      * \sa analyzePattern()
-      */ 
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    
-    void init()
-    {
-      m_structureIsUptodate = false;
-      m_iparm(IPARM_SYM) = API_SYM_NO;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      if(IsStrSym)
-        out = matrix;
-      else
-      {
-        if(!m_structureIsUptodate)
-        {
-          // update the transposed structure
-          m_transposedStructure = matrix.transpose();
-          
-          // Set the elements of the matrix to zero 
-          for (Index j=0; j<m_transposedStructure.outerSize(); ++j) 
-            for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it)
-              it.valueRef() = 0.0;
-
-          m_structureIsUptodate = true;
-        }
-        
-        out = m_transposedStructure + matrix;
-      }
-      internal::c_to_fortran_numbering(out);
-    }
-    
-    using Base::m_iparm;
-    using Base::m_dparm;
-    
-    ColSpMatrix m_transposedStructure;
-    bool m_structureIsUptodate;
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base;
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLLT() : Base()
-    {
-      init();
-    }
-    
-    explicit PastixLLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L factor of the LL^T supernodal factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-     /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-      /** Compute the LL^T supernodal numerical factorization of \p matrix 
-        * \sa analyzePattern()
-        */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      out.resize(matrix.rows(), matrix.cols());
-      // Pastix supports only lower, column-major matrices 
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-/** \ingroup PaStiXSupport_Module
-  * \class PastixLDLT
-  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
-  * 
-  * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
-  * available in the PaStiX library. The matrix A should be symmetric and positive definite
-  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
-  * The vectors or matrices X and B can be either dense or sparse
-  * 
-  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
-  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
-  *
-  * \implsparsesolverconcept
-  *
-  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
-  */
-template<typename _MatrixType, int _UpLo>
-class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> >
-{
-  public:
-    typedef _MatrixType MatrixType;
-    typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; 
-    typedef typename Base::ColSpMatrix ColSpMatrix;
-    
-  public:
-    enum { UpLo = _UpLo };
-    PastixLDLT():Base()
-    {
-      init();
-    }
-    
-    explicit PastixLDLT(const MatrixType& matrix):Base()
-    {
-      init();
-      compute(matrix);
-    }
-
-    /** Compute the L and D factors of the LDL^T factorization of \p matrix 
-      * \sa analyzePattern() factorize()
-      */
-    void compute (const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::compute(temp);
-    }
-
-    /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
-      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
-      * \sa factorize()
-      */
-    void analyzePattern(const MatrixType& matrix)
-    { 
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::analyzePattern(temp);
-    }
-    /** Compute the LDL^T supernodal numerical factorization of \p matrix 
-      * 
-      */
-    void factorize(const MatrixType& matrix)
-    {
-      ColSpMatrix temp;
-      grabMatrix(matrix, temp);
-      Base::factorize(temp);
-    }
-
-  protected:
-    using Base::m_iparm;
-    
-    void init()
-    {
-      m_iparm(IPARM_SYM) = API_SYM_YES;
-      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
-    }
-    
-    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
-    {
-      // Pastix supports only lower, column-major matrices 
-      out.resize(matrix.rows(), matrix.cols());
-      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
-      internal::c_to_fortran_numbering(out);
-    }
-};
-
-} // end namespace Eigen
-
-#endif