Eigen/src/PardisoSupport/PardisoSupport.h - eigen/fuchsia - Git at Google

 /*
  Copyright (c) 2011, Intel Corporation. All rights reserved.

  Redistribution and use in source and binary forms, with or without modification,
  are permitted provided that the following conditions are met:

  * Redistributions of source code must retain the above copyright notice, this
    list of conditions and the following disclaimer.
  * Redistributions in binary form must reproduce the above copyright notice,
    this list of conditions and the following disclaimer in the documentation
    and/or other materials provided with the distribution.
  * Neither the name of Intel Corporation nor the names of its contributors may
    be used to endorse or promote products derived from this software without
    specific prior written permission.

  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
  ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
  ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

  ********************************************************************************
  *   Content : Eigen bindings to Intel(R) MKL PARDISO
  ********************************************************************************
 */

 #ifndef EIGEN_PARDISOSUPPORT_H
 #define EIGEN_PARDISOSUPPORT_H

 namespace Eigen {

 template<typename _MatrixType> class PardisoLU;
 template<typename _MatrixType, int Options=Upper> class PardisoLLT;
 template<typename _MatrixType, int Options=Upper> class PardisoLDLT;

 namespace internal
 {
   template<typename Index>
   struct pardiso_run_selector
   {
     static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
                       Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
     {
       Index error = 0;
       ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
       return error;
     }
   };
   template<>
   struct pardiso_run_selector<long long int>
   {
     typedef long long int Index;
     static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
                       Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x)
     {
       Index error = 0;
       ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
       return error;
     }
   };

   template<class Pardiso> struct pardiso_traits;

   template<typename _MatrixType>
   struct pardiso_traits< PardisoLU<_MatrixType> >
   {
     typedef _MatrixType MatrixType;
     typedef typename _MatrixType::Scalar Scalar;
     typedef typename _MatrixType::RealScalar RealScalar;
     typedef typename _MatrixType::Index Index;
   };

   template<typename _MatrixType, int Options>
   struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
   {
     typedef _MatrixType MatrixType;
     typedef typename _MatrixType::Scalar Scalar;
     typedef typename _MatrixType::RealScalar RealScalar;
     typedef typename _MatrixType::Index Index;
   };

   template<typename _MatrixType, int Options>
   struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
   {
     typedef _MatrixType MatrixType;
     typedef typename _MatrixType::Scalar Scalar;
     typedef typename _MatrixType::RealScalar RealScalar;
     typedef typename _MatrixType::Index Index;
   };

 }

 template<class Derived>
 class PardisoImpl : internal::noncopyable
 {
     typedef internal::pardiso_traits<Derived> Traits;
   public:
     typedef typename Traits::MatrixType MatrixType;
     typedef typename Traits::Scalar Scalar;
     typedef typename Traits::RealScalar RealScalar;
     typedef typename Traits::Index Index;
     typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType;
     typedef Matrix<Scalar,Dynamic,1> VectorType;
     typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
     typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
     typedef Array<Index,64,1,DontAlign> ParameterType;
     enum {
       ScalarIsComplex = NumTraits<Scalar>::IsComplex
     };

     PardisoImpl()
     {
       eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
       m_iparm.setZero();
       m_msglvl = 0; // No output
       m_initialized = false;
     }

     ~PardisoImpl()
     {
       pardisoRelease();
     }

     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 succesful,
       *          \c NumericalIssue if the matrix appears to be negative.
       */
     ComputationInfo info() const
     {
       eigen_assert(m_initialized && "Decomposition is not initialized.");
       return m_info;
     }

     /** \warning for advanced usage only.
       * \returns a reference to the parameter array controlling PARDISO.
       * See the PARDISO manual to know how to use it. */
     ParameterType& pardisoParameterArray()
     {
       return m_iparm;
     }

     /** Performs a symbolic decomposition on the sparcity of \a matrix.
       *
       * This function is particularly useful when solving for several problems having the same structure.
       *
       * \sa factorize()
       */
     Derived& analyzePattern(const MatrixType& matrix);

     /** Performs a numeric decomposition of \a matrix
       *
       * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
       *
       * \sa analyzePattern()
       */
     Derived& factorize(const MatrixType& matrix);

     Derived& compute(const MatrixType& matrix);

     /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
       *
       * \sa compute()
       */
     template<typename Rhs>
     inline const internal::solve_retval<PardisoImpl, Rhs>
     solve(const MatrixBase<Rhs>& b) const
     {
       eigen_assert(m_initialized && "Pardiso solver is not initialized.");
       eigen_assert(rows()==b.rows()
                 && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
       return internal::solve_retval<PardisoImpl, Rhs>(*this, b.derived());
     }

     /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
       *
       * \sa compute()
       */
     template<typename Rhs>
     inline const internal::sparse_solve_retval<PardisoImpl, Rhs>
     solve(const SparseMatrixBase<Rhs>& b) const
     {
       eigen_assert(m_initialized && "Pardiso solver is not initialized.");
       eigen_assert(rows()==b.rows()
                 && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
       return internal::sparse_solve_retval<PardisoImpl, Rhs>(*this, b.derived());
     }

     Derived& derived()
     {
       return *static_cast<Derived*>(this);
     }
     const Derived& derived() const
     {
       return *static_cast<const Derived*>(this);
     }

     template<typename BDerived, typename XDerived>
     bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const;

   protected:
     void pardisoRelease()
     {
       if(m_initialized) // Factorization ran at least once
       {
         internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0,
                                                    m_iparm.data(), m_msglvl, 0, 0);
       }
     }

     void pardisoInit(int type)
     {
       m_type = type;
       bool symmetric = std::abs(m_type) < 10;
       m_iparm[0] = 1;   // No solver default
       m_iparm[1] = 3;   // use Metis for the ordering
       m_iparm[2] = 1;   // Numbers of processors, value of OMP_NUM_THREADS
       m_iparm[3] = 0;   // No iterative-direct algorithm
       m_iparm[4] = 0;   // No user fill-in reducing permutation
       m_iparm[5] = 0;   // Write solution into x
       m_iparm[6] = 0;   // Not in use
       m_iparm[7] = 2;   // Max numbers of iterative refinement steps
       m_iparm[8] = 0;   // Not in use
       m_iparm[9] = 13;  // Perturb the pivot elements with 1E-13
       m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
       m_iparm[11] = 0;  // Not in use
       m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
                                         // Try m_iparm[12] = 1 in case of inappropriate accuracy
       m_iparm[13] = 0;  // Output: Number of perturbed pivots
       m_iparm[14] = 0;  // Not in use
       m_iparm[15] = 0;  // Not in use
       m_iparm[16] = 0;  // Not in use
       m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
       m_iparm[18] = -1; // Output: Mflops for LU factorization
       m_iparm[19] = 0;  // Output: Numbers of CG Iterations

       m_iparm[20] = 0;  // 1x1 pivoting
       m_iparm[26] = 0;  // No matrix checker
       m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
       m_iparm[34] = 1;  // C indexing
       m_iparm[59] = 1;  // Automatic switch between In-Core and Out-of-Core modes
     }

   protected:
     // cached data to reduce reallocation, etc.

     void manageErrorCode(Index error)
     {
       switch(error)
       {
         case 0:
           m_info = Success;
           break;
         case -4:
         case -7:
           m_info = NumericalIssue;
           break;
         default:
           m_info = InvalidInput;
       }
     }

     mutable SparseMatrixType m_matrix;
     ComputationInfo m_info;
     bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
     Index m_type, m_msglvl;
     mutable void *m_pt[64];
     mutable ParameterType m_iparm;
     mutable IntColVectorType m_perm;
     Index m_size;

 };

 template<class Derived>
 Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
 {
   m_size = a.rows();
   eigen_assert(a.rows() == a.cols());

   pardisoRelease();
   memset(m_pt, 0, sizeof(m_pt));
   m_perm.setZero(m_size);
   derived().getMatrix(a);

   Index error;
   error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, m_size,
                                                      m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
                                                      m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

   manageErrorCode(error);
   m_analysisIsOk = true;
   m_factorizationIsOk = true;
   m_initialized = true;
   return derived();
 }

 template<class Derived>
 Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
 {
   m_size = a.rows();
   eigen_assert(m_size == a.cols());

   pardisoRelease();
   memset(m_pt, 0, sizeof(m_pt));
   m_perm.setZero(m_size);
   derived().getMatrix(a);

   Index error;
   error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, m_size,
                                                      m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
                                                      m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

   manageErrorCode(error);
   m_analysisIsOk = true;
   m_factorizationIsOk = false;
   m_initialized = true;
   return derived();
 }

 template<class Derived>
 Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
 {
   eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
   eigen_assert(m_size == a.rows() && m_size == a.cols());

   derived().getMatrix(a);

   Index error;
   error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, m_size,
                                                      m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
                                                      m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

   manageErrorCode(error);
   m_factorizationIsOk = true;
   return derived();
 }

 template<class Base>
 template<typename BDerived,typename XDerived>
 bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
 {
   if(m_iparm[0] == 0) // Factorization was not computed
     return false;

   //Index n = m_matrix.rows();
   Index nrhs = Index(b.cols());
   eigen_assert(m_size==b.rows());
   eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
   eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
   eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));


 //  switch (transposed) {
 //    case SvNoTrans    : m_iparm[11] = 0 ; break;
 //    case SvTranspose  : m_iparm[11] = 2 ; break;
 //    case SvAdjoint    : m_iparm[11] = 1 ; break;
 //    default:
 //      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
 //      m_iparm[11] = 0;
 //  }

   Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
   Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;

   // Pardiso cannot solve in-place
   if(rhs_ptr == x.derived().data())
   {
     tmp = b;
     rhs_ptr = tmp.data();
   }

   Index error;
   error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size,
                                                      m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
                                                      m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
                                                      rhs_ptr, x.derived().data());

   return error==0;
 }


 /** \ingroup PardisoSupport_Module
   * \class PardisoLU
   * \brief A sparse direct LU factorization and solver based on the PARDISO library
   *
   * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
   * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
   * 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<>
   *
   * \sa \ref TutorialSparseDirectSolvers
   */
 template<typename MatrixType>
 class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
 {
   protected:
     typedef PardisoImpl< PardisoLU<MatrixType> > Base;
     typedef typename Base::Scalar Scalar;
     typedef typename Base::RealScalar RealScalar;
     using Base::pardisoInit;
     using Base::m_matrix;
     friend class PardisoImpl< PardisoLU<MatrixType> >;

   public:

     using Base::compute;
     using Base::solve;

     PardisoLU()
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? 13 : 11);
     }

     PardisoLU(const MatrixType& matrix)
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? 13 : 11);
       compute(matrix);
     }
   protected:
     void getMatrix(const MatrixType& matrix)
     {
       m_matrix = matrix;
     }
 };

 /** \ingroup PardisoSupport_Module
   * \class PardisoLLT
   * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
   *
   * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
   * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
   * 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 can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
   *         Upper|Lower can be used to tell both triangular parts can be used as input.
   *
   * \sa \ref TutorialSparseDirectSolvers
   */
 template<typename MatrixType, int _UpLo>
 class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
 {
   protected:
     typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
     typedef typename Base::Scalar Scalar;
     typedef typename Base::Index Index;
     typedef typename Base::RealScalar RealScalar;
     using Base::pardisoInit;
     using Base::m_matrix;
     friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;

   public:

     enum { UpLo = _UpLo };
     using Base::compute;
     using Base::solve;

     PardisoLLT()
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? 4 : 2);
     }

     PardisoLLT(const MatrixType& matrix)
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? 4 : 2);
       compute(matrix);
     }

   protected:

     void getMatrix(const MatrixType& matrix)
     {
       // PARDISO supports only upper, row-major matrices
       PermutationMatrix<Dynamic,Dynamic,Index> p_null;
       m_matrix.resize(matrix.rows(), matrix.cols());
       m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
     }
 };

 /** \ingroup PardisoSupport_Module
   * \class PardisoLDLT
   * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
   *
   * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
   * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
   * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
   * 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 Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
   *         Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
   *         Upper|Lower can be used to tell both triangular parts can be used as input.
   *
   * \sa \ref TutorialSparseDirectSolvers
   */
 template<typename MatrixType, int Options>
 class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
 {
   protected:
     typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
     typedef typename Base::Scalar Scalar;
     typedef typename Base::Index Index;
     typedef typename Base::RealScalar RealScalar;
     using Base::pardisoInit;
     using Base::m_matrix;
     friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;

   public:

     using Base::compute;
     using Base::solve;
     enum { UpLo = Options&(Upper|Lower) };

     PardisoLDLT()
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
     }

     PardisoLDLT(const MatrixType& matrix)
       : Base()
     {
       pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
       compute(matrix);
     }

     void getMatrix(const MatrixType& matrix)
     {
       // PARDISO supports only upper, row-major matrices
       PermutationMatrix<Dynamic,Dynamic,Index> p_null;
       m_matrix.resize(matrix.rows(), matrix.cols());
       m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
     }
 };

 namespace internal {

 template<typename _Derived, typename Rhs>
 struct solve_retval<PardisoImpl<_Derived>, Rhs>
   : solve_retval_base<PardisoImpl<_Derived>, Rhs>
 {
   typedef PardisoImpl<_Derived> Dec;
   EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)

   template<typename Dest> void evalTo(Dest& dst) const
   {
     dec()._solve(rhs(),dst);
   }
 };

 template<typename Derived, typename Rhs>
 struct sparse_solve_retval<PardisoImpl<Derived>, Rhs>
   : sparse_solve_retval_base<PardisoImpl<Derived>, Rhs>
 {
   typedef PardisoImpl<Derived> Dec;
   EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)

   template<typename Dest> void evalTo(Dest& dst) const
   {
     this->defaultEvalTo(dst);
   }
 };

 } // end namespace internal

 } // end namespace Eigen

 #endif // EIGEN_PARDISOSUPPORT_H
	/*
	Copyright (c) 2011, Intel Corporation. All rights reserved.

	Redistribution and use in source and binary forms, with or without modification,
	are permitted provided that the following conditions are met:

	* Redistributions of source code must retain the above copyright notice, this
	list of conditions and the following disclaimer.
	* Redistributions in binary form must reproduce the above copyright notice,
	this list of conditions and the following disclaimer in the documentation
	and/or other materials provided with the distribution.
	* Neither the name of Intel Corporation nor the names of its contributors may
	be used to endorse or promote products derived from this software without
	specific prior written permission.

	THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
	ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
	WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
	DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
	ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
	(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
	LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
	ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
	(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
	SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

	********************************************************************************
	* Content : Eigen bindings to Intel(R) MKL PARDISO
	********************************************************************************
	*/

	#ifndef EIGEN_PARDISOSUPPORT_H
	#define EIGEN_PARDISOSUPPORT_H

	namespace Eigen {

	template<typename _MatrixType> class PardisoLU;
	template<typename _MatrixType, int Options=Upper> class PardisoLLT;
	template<typename _MatrixType, int Options=Upper> class PardisoLDLT;

	namespace internal
	{
	template<typename Index>
	struct pardiso_run_selector
	{
	static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
	Index ia, Index ja, Index perm, Index nrhs, Index iparm, Index msglvl, void b, void x)
	{
	Index error = 0;
	::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
	return error;
	}
	};
	template<>
	struct pardiso_run_selector<long long int>
	{
	typedef long long int Index;
	static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a,
	Index ia, Index ja, Index perm, Index nrhs, Index iparm, Index msglvl, void b, void x)
	{
	Index error = 0;
	::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
	return error;
	}
	};

	template<class Pardiso> struct pardiso_traits;

	template<typename _MatrixType>
	struct pardiso_traits< PardisoLU<_MatrixType> >
	{
	typedef _MatrixType MatrixType;
	typedef typename _MatrixType::Scalar Scalar;
	typedef typename _MatrixType::RealScalar RealScalar;
	typedef typename _MatrixType::Index Index;
	};

	template<typename _MatrixType, int Options>
	struct pardiso_traits< PardisoLLT<_MatrixType, Options> >
	{
	typedef _MatrixType MatrixType;
	typedef typename _MatrixType::Scalar Scalar;
	typedef typename _MatrixType::RealScalar RealScalar;
	typedef typename _MatrixType::Index Index;
	};

	template<typename _MatrixType, int Options>
	struct pardiso_traits< PardisoLDLT<_MatrixType, Options> >
	{
	typedef _MatrixType MatrixType;
	typedef typename _MatrixType::Scalar Scalar;
	typedef typename _MatrixType::RealScalar RealScalar;
	typedef typename _MatrixType::Index Index;
	};

	}

	template<class Derived>
	class PardisoImpl : internal::noncopyable
	{
	typedef internal::pardiso_traits<Derived> Traits;
	public:
	typedef typename Traits::MatrixType MatrixType;
	typedef typename Traits::Scalar Scalar;
	typedef typename Traits::RealScalar RealScalar;
	typedef typename Traits::Index Index;
	typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType;
	typedef Matrix<Scalar,Dynamic,1> VectorType;
	typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
	typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
	typedef Array<Index,64,1,DontAlign> ParameterType;
	enum {
	ScalarIsComplex = NumTraits<Scalar>::IsComplex
	};

	PardisoImpl()
	{
	eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
	m_iparm.setZero();
	m_msglvl = 0; // No output
	m_initialized = false;
	}

	~PardisoImpl()
	{
	pardisoRelease();
	}

	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 succesful,
	* \c NumericalIssue if the matrix appears to be negative.
	*/
	ComputationInfo info() const
	{
	eigen_assert(m_initialized && "Decomposition is not initialized.");
	return m_info;
	}

	/** \warning for advanced usage only.
	* \returns a reference to the parameter array controlling PARDISO.
	* See the PARDISO manual to know how to use it. */
	ParameterType& pardisoParameterArray()
	{
	return m_iparm;
	}

	/** Performs a symbolic decomposition on the sparcity of \a matrix.
	*
	* This function is particularly useful when solving for several problems having the same structure.
	*
	* \sa factorize()
	*/
	Derived& analyzePattern(const MatrixType& matrix);

	/** Performs a numeric decomposition of \a matrix
	*
	* The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
	*
	* \sa analyzePattern()
	*/
	Derived& factorize(const MatrixType& matrix);

	Derived& compute(const MatrixType& matrix);

	/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
	*
	* \sa compute()
	*/
	template<typename Rhs>
	inline const internal::solve_retval<PardisoImpl, Rhs>
	solve(const MatrixBase<Rhs>& b) const
	{
	eigen_assert(m_initialized && "Pardiso solver is not initialized.");
	eigen_assert(rows()==b.rows()
	&& "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
	return internal::solve_retval<PardisoImpl, Rhs>(*this, b.derived());
	}

	/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
	*
	* \sa compute()
	*/
	template<typename Rhs>
	inline const internal::sparse_solve_retval<PardisoImpl, Rhs>
	solve(const SparseMatrixBase<Rhs>& b) const
	{
	eigen_assert(m_initialized && "Pardiso solver is not initialized.");
	eigen_assert(rows()==b.rows()
	&& "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b");
	return internal::sparse_solve_retval<PardisoImpl, Rhs>(*this, b.derived());
	}

	Derived& derived()
	{
	return static_cast<Derived>(this);
	}
	const Derived& derived() const
	{
	return static_cast<const Derived>(this);
	}

	template<typename BDerived, typename XDerived>
	bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const;

	protected:
	void pardisoRelease()
	{
	if(m_initialized) // Factorization ran at least once
	{
	internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0,
	m_iparm.data(), m_msglvl, 0, 0);
	}
	}

	void pardisoInit(int type)
	{
	m_type = type;
	bool symmetric = std::abs(m_type) < 10;
	m_iparm[0] = 1; // No solver default
	m_iparm[1] = 3; // use Metis for the ordering
	m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS
	m_iparm[3] = 0; // No iterative-direct algorithm
	m_iparm[4] = 0; // No user fill-in reducing permutation
	m_iparm[5] = 0; // Write solution into x
	m_iparm[6] = 0; // Not in use
	m_iparm[7] = 2; // Max numbers of iterative refinement steps
	m_iparm[8] = 0; // Not in use
	m_iparm[9] = 13; // Perturb the pivot elements with 1E-13
	m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
	m_iparm[11] = 0; // Not in use
	m_iparm[12] = symmetric ? 0 : 1; // Maximum weighted matching algorithm is switched-off (default for symmetric).
	// Try m_iparm[12] = 1 in case of inappropriate accuracy
	m_iparm[13] = 0; // Output: Number of perturbed pivots
	m_iparm[14] = 0; // Not in use
	m_iparm[15] = 0; // Not in use
	m_iparm[16] = 0; // Not in use
	m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
	m_iparm[18] = -1; // Output: Mflops for LU factorization
	m_iparm[19] = 0; // Output: Numbers of CG Iterations

	m_iparm[20] = 0; // 1x1 pivoting
	m_iparm[26] = 0; // No matrix checker
	m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
	m_iparm[34] = 1; // C indexing
	m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes
	}

	protected:
	// cached data to reduce reallocation, etc.

	void manageErrorCode(Index error)
	{
	switch(error)
	{
	case 0:
	m_info = Success;
	break;
	case -4:
	case -7:
	m_info = NumericalIssue;
	break;
	default:
	m_info = InvalidInput;
	}
	}

	mutable SparseMatrixType m_matrix;
	ComputationInfo m_info;
	bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
	Index m_type, m_msglvl;
	mutable void *m_pt[64];
	mutable ParameterType m_iparm;
	mutable IntColVectorType m_perm;
	Index m_size;

	};

	template<class Derived>
	Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
	{
	m_size = a.rows();
	eigen_assert(a.rows() == a.cols());

	pardisoRelease();
	memset(m_pt, 0, sizeof(m_pt));
	m_perm.setZero(m_size);
	derived().getMatrix(a);

	Index error;
	error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, m_size,
	m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
	m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

	manageErrorCode(error);
	m_analysisIsOk = true;
	m_factorizationIsOk = true;
	m_initialized = true;
	return derived();
	}

	template<class Derived>
	Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
	{
	m_size = a.rows();
	eigen_assert(m_size == a.cols());

	pardisoRelease();
	memset(m_pt, 0, sizeof(m_pt));
	m_perm.setZero(m_size);
	derived().getMatrix(a);

	Index error;
	error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, m_size,
	m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
	m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

	manageErrorCode(error);
	m_analysisIsOk = true;
	m_factorizationIsOk = false;
	m_initialized = true;
	return derived();
	}

	template<class Derived>
	Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
	{
	eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
	eigen_assert(m_size == a.rows() && m_size == a.cols());

	derived().getMatrix(a);

	Index error;
	error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, m_size,
	m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
	m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);

	manageErrorCode(error);
	m_factorizationIsOk = true;
	return derived();
	}

	template<class Base>
	template<typename BDerived,typename XDerived>
	bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
	{
	if(m_iparm[0] == 0) // Factorization was not computed
	return false;

	//Index n = m_matrix.rows();
	Index nrhs = Index(b.cols());
	eigen_assert(m_size==b.rows());
	eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 \|\| nrhs == 1) && "Row-major right hand sides are not supported");
	eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 \|\| nrhs == 1) && "Row-major matrices of unknowns are not supported");
	eigen_assert(((nrhs == 1) \|\| b.outerStride() == b.rows()));


	// switch (transposed) {
	// case SvNoTrans : m_iparm[11] = 0 ; break;
	// case SvTranspose : m_iparm[11] = 2 ; break;
	// case SvAdjoint : m_iparm[11] = 1 ; break;
	// default:
	// //std::cerr << "Eigen: transposition option \"" << transposed << "\" not supported by the PARDISO backend\n";
	// m_iparm[11] = 0;
	// }

	Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
	Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;

	// Pardiso cannot solve in-place
	if(rhs_ptr == x.derived().data())
	{
	tmp = b;
	rhs_ptr = tmp.data();
	}

	Index error;
	error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size,
	m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
	m_perm.data(), nrhs, m_iparm.data(), m_msglvl,
	rhs_ptr, x.derived().data());

	return error==0;
	}


	/** \ingroup PardisoSupport_Module
	* \class PardisoLU
	* \brief A sparse direct LU factorization and solver based on the PARDISO library
	*
	* This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
	* using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
	* 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<>
	*
	* \sa \ref TutorialSparseDirectSolvers
	*/
	template<typename MatrixType>
	class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
	{
	protected:
	typedef PardisoImpl< PardisoLU<MatrixType> > Base;
	typedef typename Base::Scalar Scalar;
	typedef typename Base::RealScalar RealScalar;
	using Base::pardisoInit;
	using Base::m_matrix;
	friend class PardisoImpl< PardisoLU<MatrixType> >;

	public:

	using Base::compute;
	using Base::solve;

	PardisoLU()
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? 13 : 11);
	}

	PardisoLU(const MatrixType& matrix)
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? 13 : 11);
	compute(matrix);
	}
	protected:
	void getMatrix(const MatrixType& matrix)
	{
	m_matrix = matrix;
	}
	};

	/** \ingroup PardisoSupport_Module
	* \class PardisoLLT
	* \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
	*
	* This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
	* using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
	* 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 can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
	* Upper\|Lower can be used to tell both triangular parts can be used as input.
	*
	* \sa \ref TutorialSparseDirectSolvers
	*/
	template<typename MatrixType, int _UpLo>
	class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> >
	{
	protected:
	typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base;
	typedef typename Base::Scalar Scalar;
	typedef typename Base::Index Index;
	typedef typename Base::RealScalar RealScalar;
	using Base::pardisoInit;
	using Base::m_matrix;
	friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >;

	public:

	enum { UpLo = _UpLo };
	using Base::compute;
	using Base::solve;

	PardisoLLT()
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? 4 : 2);
	}

	PardisoLLT(const MatrixType& matrix)
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? 4 : 2);
	compute(matrix);
	}

	protected:

	void getMatrix(const MatrixType& matrix)
	{
	// PARDISO supports only upper, row-major matrices
	PermutationMatrix<Dynamic,Dynamic,Index> p_null;
	m_matrix.resize(matrix.rows(), matrix.cols());
	m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
	}
	};

	/** \ingroup PardisoSupport_Module
	* \class PardisoLDLT
	* \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
	*
	* This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
	* using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
	* For complex matrices, A can also be symmetric only, see the \a Options template parameter.
	* 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 Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
	* Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
	* Upper\|Lower can be used to tell both triangular parts can be used as input.
	*
	* \sa \ref TutorialSparseDirectSolvers
	*/
	template<typename MatrixType, int Options>
	class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
	{
	protected:
	typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
	typedef typename Base::Scalar Scalar;
	typedef typename Base::Index Index;
	typedef typename Base::RealScalar RealScalar;
	using Base::pardisoInit;
	using Base::m_matrix;
	friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;

	public:

	using Base::compute;
	using Base::solve;
	enum { UpLo = Options&(Upper\|Lower) };

	PardisoLDLT()
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
	}

	PardisoLDLT(const MatrixType& matrix)
	: Base()
	{
	pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
	compute(matrix);
	}

	void getMatrix(const MatrixType& matrix)
	{
	// PARDISO supports only upper, row-major matrices
	PermutationMatrix<Dynamic,Dynamic,Index> p_null;
	m_matrix.resize(matrix.rows(), matrix.cols());
	m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
	}
	};

	namespace internal {

	template<typename _Derived, typename Rhs>
	struct solve_retval<PardisoImpl<_Derived>, Rhs>
	: solve_retval_base<PardisoImpl<_Derived>, Rhs>
	{
	typedef PardisoImpl<_Derived> Dec;
	EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs)

	template<typename Dest> void evalTo(Dest& dst) const
	{
	dec()._solve(rhs(),dst);
	}
	};

	template<typename Derived, typename Rhs>
	struct sparse_solve_retval<PardisoImpl<Derived>, Rhs>
	: sparse_solve_retval_base<PardisoImpl<Derived>, Rhs>
	{
	typedef PardisoImpl<Derived> Dec;
	EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs)

	template<typename Dest> void evalTo(Dest& dst) const
	{
	this->defaultEvalTo(dst);
	}
	};

	} // end namespace internal

	} // end namespace Eigen

	#endif // EIGEN_PARDISOSUPPORT_H