SparseLU.h

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@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_SPARSELU_H
#define EIGEN_SPARSELU_H

enum {
    SvNoTrans   = 0,
    SvTranspose = 1,
    SvAdjoint   = 2
};

template<typename _MatrixType, typename Backend = DefaultBackend>
class SparseLU
  {
  protected:
    typedef typename _MatrixType::Scalar Scalar;
    typedef typename NumTraits<typename _MatrixType::Scalar>::Real RealScalar;
    typedef SparseMatrix<Scalar> LUMatrixType;

    enum {
      MatrixLUIsDirty             = 0x10000
    };

  public:
    typedef _MatrixType MatrixType;

    SparseLU(int flags = 0)
      : m_flags(flags), m_status(0)
    {
      m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision();
    }

    SparseLU(const _MatrixType& matrix, int flags = 0)
      : /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0)
    {
      m_precision = RealScalar(0.1) * Eigen::NumTraits<RealScalar>::dummy_precision();
      compute(matrix);
    }

    void setPrecision(RealScalar v) { m_precision = v; }

    RealScalar precision() const { return m_precision; }

    void setFlags(int f) { m_flags = f; }
    int flags() const { return m_flags; }

    void setOrderingMethod(int m)
    {
      eigen_assert( (m&~OrderingMask) == 0 && m!=0 && "invalid ordering method");
      m_flags = m_flags&~OrderingMask | m&OrderingMask;
    }

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

    void compute(const _MatrixType& matrix);

    //inline const _MatrixType& matrixL() const { return m_matrixL; }

    //inline const _MatrixType& matrixU() const { return m_matrixU; }

    template<typename BDerived, typename XDerived>
    bool solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x,
               const int transposed = SvNoTrans) const;

    inline bool succeeded(void) const { return m_succeeded; }

  protected:
    RealScalar m_precision;
    int m_flags;
    mutable int m_status;
    bool m_succeeded;
};

template<typename _MatrixType, typename Backend>
void SparseLU<_MatrixType,Backend>::compute(const _MatrixType& )
{
  eigen_assert(false && "not implemented yet");
}

template<typename _MatrixType, typename Backend>
template<typename BDerived, typename XDerived>
bool SparseLU<_MatrixType,Backend>::solve(const MatrixBase<BDerived> &, MatrixBase<XDerived>* , const int ) const
{
  eigen_assert(false && "not implemented yet");
  return false;
}

#endif // EIGEN_SPARSELU_H