eigen/Eigen/src/PARDISOSupport/PARDISOSupport.h

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

#ifndef EIGEN_PARDISOSUPPORT_H
#define EIGEN_PARDISOSUPPORT_H

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

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

}

template<class Derived>
class PardisoImpl
{
  public:
    typedef typename internal::pardiso_traits<Derived>::MatrixType MatrixType;
    typedef typename internal::pardiso_traits<Derived>::Scalar Scalar;
    typedef typename internal::pardiso_traits<Derived>::RealScalar RealScalar;
    typedef typename internal::pardiso_traits<Derived>::Index Index;
    typedef Matrix<Scalar,Dynamic,1> VectorType;
    typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
    typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
    enum {
      ScalarIsComplex = NumTraits<Scalar>::IsComplex
    };

    PardisoImpl(int flags) : m_flags(flags)
    {
      eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type");
      memset(m_iparm, 0, sizeof(m_iparm));
      m_msglvl = 0; /* No output */
      m_initialized = false;
    }

    ~PardisoImpl()
    {
      pardisoRelease();
    }

    inline Index cols() const { return m_matrix.cols(); }
    inline Index rows() const { return m_matrix.rows(); }
  
    /** \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;
    }

    int orderingMethod() const
    {
      return m_flags&OrderingMask;
    }

    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 int transposed = SvNoTrans) const
    {
      eigen_assert(m_initialized && "SimplicialCholesky 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(), transposed);
    }

    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 int transposed = SvNoTrans) 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_matrix.rows(), NULL, NULL, NULL, m_perm.data(), 0,
            m_iparm, m_msglvl, NULL, NULL);
        memset(m_iparm, 0, sizeof(m_iparm));
      }
    }
  protected:
    // cached data to reduce reallocation, etc.

    ComputationInfo m_info;
    bool m_symmetric, m_initialized, m_succeeded;
    int m_flags;
    Index m_type, m_msglvl;
    mutable void *m_pt[64];
    mutable Index m_iparm[64];
    mutable SparseMatrix<Scalar, RowMajor> m_matrix;
    mutable IntColVectorType m_perm;
};

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

  pardisoRelease();
  memset(m_pt, 0, sizeof(m_pt));
  m_initialized = true;

  m_symmetric = abs(m_type) < 10;

  switch (orderingMethod())
  {
    case MinimumDegree_AT_PLUS_A  : m_iparm[1] = 0; break;
    case NaturalOrdering          : m_iparm[5] = 1; break;
    case Metis                    : m_iparm[1] = 3; break;
    default:
      //std::cerr << "Eigen: ordering method \"" << Base::orderingMethod() << "\" not supported by the PARDISO backend\n";
      m_iparm[1] = 0;
  };

  m_iparm[0] = 1; /* No solver default */
  /* Numbers of processors, value of OMP_NUM_THREADS */
  m_iparm[2] = 1;
  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] = m_symmetric ? 0 : 1; /* Use nonsymmetric permutation and scaling MPS */
  m_iparm[11] = 0; /* Not in use */
  m_iparm[12] = m_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] = 0; /* Fortran indexing */
  m_iparm[59] = 1; /* Automatic switch between In-Core and Out-of-Core modes */

  m_perm.resize(n);
  if(orderingMethod() == NaturalOrdering)
  {
    for(Index i = 0; i < n; i++)
      m_perm[i] = i;
  }

  m_matrix = a;

  /* Convert to Fortran-style indexing */
  for(i = 0; i <= m_matrix.rows(); ++i)
    ++m_matrix._outerIndexPtr()[i];
  for(i = 0; i < m_matrix.nonZeros(); ++i)
    ++m_matrix._innerIndexPtr()[i];

  Index error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, n, 
      m_matrix._valuePtr(), m_matrix._outerIndexPtr(), m_matrix._innerIndexPtr(),
      m_perm.data(), 0, m_iparm, m_msglvl, NULL, NULL);

  switch(error)
  {
    case 0:
      m_succeeded = true;
	  m_info = Success;
      return derived();
    case -4:
    case -7:
      m_info = NumericalIssue;
      break;
    default:
      m_info = InvalidInput;
  }
  m_succeeded = false;
  return derived();
}

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

  Index n = m_matrix.rows();
  Index nrhs = b.cols();
  eigen_assert(n==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()));

  x.derived().resizeLike(b);

  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;
  }

  Index error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, n, 
      m_matrix._valuePtr(), m_matrix._outerIndexPtr(), m_matrix._innerIndexPtr(),
      m_perm.data(), nrhs, m_iparm, m_msglvl, const_cast<Scalar*>(&b(0, 0)), &x(0, 0));

  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::m_type;

  public:

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

    PardisoLU(int flags = Metis)
      : Base(flags)
    {
      m_type = Base::ScalarIsComplex ? 13 : 11;
    }

    PardisoLU(const MatrixType& matrix, int flags = Metis)
      : Base(flags)
    {
      m_type = Base::ScalarIsComplex ? 13 : 11;
      compute(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<>
  *
  * \sa \ref TutorialSparseDirectSolvers
  */
template<typename MatrixType>
class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType> >
{
  protected:
    typedef PardisoImpl< PardisoLLT<MatrixType> > Base;
    typedef typename Base::Scalar Scalar;
    typedef typename Base::RealScalar RealScalar;
    using Base::m_type;

  public:

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

    PardisoLLT(int flags = Metis)
      : Base(flags)
    {
      m_type = Base::ScalarIsComplex ? 4 : 2;
    }

    PardisoLLT(const MatrixType& matrix, int flags = Metis)
      : Base(flags)
    {
      m_type = Base::ScalarIsComplex ? 4 : 2;
      compute(matrix);
    }
};

/** \ingroup PARDISOSupport_Module
  * \class PardisoLDLT
  * \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 LDL^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<>
  *
  * \sa \ref TutorialSparseDirectSolvers
  */
template<typename MatrixType>
class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType> >
{
  protected:
    typedef PardisoImpl< PardisoLDLT<MatrixType> > Base;
    typedef typename Base::Scalar Scalar;
    typedef typename Base::RealScalar RealScalar;
    using Base::m_type;

  public:

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

    PardisoLDLT(int flags = Metis)
      : Base(flags)
    {
      m_type = Base::ScalarIsComplex ? -4 : -2;
    }

    PardisoLDLT(const MatrixType& matrix, int flags = Metis, bool hermitian = true)
      : Base(flags)
    {
      compute(matrix, hermitian);
    }

    void compute(const MatrixType& matrix, bool hermitian = true)
    {
      m_type = Base::ScalarIsComplex ? (hermitian ? -4 : 6) : -2;
      Base::compute(matrix);
    }

};

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)

  solve_retval(const PardisoImpl<_Derived>& dec, const Rhs& rhs, const int transposed)
    : Base(dec, rhs), m_transposed(transposed) {}

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

  int m_transposed;
};

}

#endif // EIGEN_PARDISOSUPPORT_H