FullPivLU.h

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// 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_LU_H
#define EIGEN_LU_H
 
// IWYU pragma: private
#include "./InternalHeaderCheck.h"
 
namespace Eigen {
 
namespace internal {
template <typename MatrixType_, typename PermutationIndex_>
struct traits<FullPivLU<MatrixType_, PermutationIndex_> > : traits<MatrixType_> {
  typedef MatrixXpr XprKind;
  typedef SolverStorage StorageKind;
  typedef PermutationIndex_ StorageIndex;
  enum { Flags = 0 };
};
 
}  // end namespace internal
 
template <typename MatrixType_, typename PermutationIndex_>
class FullPivLU : public SolverBase<FullPivLU<MatrixType_, PermutationIndex_> > {
 public:
  typedef MatrixType_ MatrixType;
  typedef SolverBase<FullPivLU> Base;
  friend class SolverBase<FullPivLU>;
 
  EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
  enum {
    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
  };
  using PermutationIndex = PermutationIndex_;
  typedef typename internal::plain_row_type<MatrixType, PermutationIndex>::type IntRowVectorType;
  typedef typename internal::plain_col_type<MatrixType, PermutationIndex>::type IntColVectorType;
  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationQType;
  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> PermutationPType;
  typedef typename MatrixType::PlainObject PlainObject;
 
  FullPivLU();
 
  FullPivLU(Index rows, Index cols);
 
  template <typename InputType>
  explicit FullPivLU(const EigenBase<InputType>& matrix);
 
  template <typename InputType>
  explicit FullPivLU(EigenBase<InputType>& matrix);
 
  template <typename InputType>
  FullPivLU& compute(const EigenBase<InputType>& matrix) {
    m_lu = matrix.derived();
    computeInPlace();
    return *this;
  }
 
  inline const MatrixType& matrixLU() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return m_lu;
  }
 
  inline Index nonzeroPivots() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return m_nonzero_pivots;
  }
 
  RealScalar maxPivot() const { return m_maxpivot; }
 
  EIGEN_DEVICE_FUNC inline const PermutationPType& permutationP() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return m_p;
  }
 
  inline const PermutationQType& permutationQ() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return m_q;
  }
 
  inline const internal::kernel_retval<FullPivLU> kernel() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return internal::kernel_retval<FullPivLU>(*this);
  }
 
  inline const internal::image_retval<FullPivLU> image(const MatrixType& originalMatrix) const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return internal::image_retval<FullPivLU>(*this, originalMatrix);
  }
 
#ifdef EIGEN_PARSED_BY_DOXYGEN
  template <typename Rhs>
  inline const Solve<FullPivLU, Rhs> solve(const MatrixBase<Rhs>& b) const;
#endif
 
  inline RealScalar rcond() const {
    eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
    return internal::rcond_estimate_helper(m_l1_norm, *this);
  }
 
  typename internal::traits<MatrixType>::Scalar determinant() const;
 
  FullPivLU& setThreshold(const RealScalar& threshold) {
    m_usePrescribedThreshold = true;
    m_prescribedThreshold = threshold;
    return *this;
  }
 
  FullPivLU& setThreshold(Default_t) {
    m_usePrescribedThreshold = false;
    return *this;
  }
 
  RealScalar threshold() const {
    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
    return m_usePrescribedThreshold ? m_prescribedThreshold
                                    // this formula comes from experimenting (see "LU precision tuning" thread on the
                                    // list) and turns out to be identical to Higham's formula used already in LDLt.
                                    : NumTraits<Scalar>::epsilon() * RealScalar(m_lu.diagonalSize());
  }
 
  inline Index rank() const {
    using std::abs;
    eigen_assert(m_isInitialized && "LU is not initialized.");
    RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
    Index result = 0;
    for (Index i = 0; i < m_nonzero_pivots; ++i) result += (abs(m_lu.coeff(i, i)) > premultiplied_threshold);
    return result;
  }
 
  inline Index dimensionOfKernel() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return cols() - rank();
  }
 
  inline bool isInjective() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return rank() == cols();
  }
 
  inline bool isSurjective() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return rank() == rows();
  }
 
  inline bool isInvertible() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    return isInjective() && (m_lu.rows() == m_lu.cols());
  }
 
  inline const Inverse<FullPivLU> inverse() const {
    eigen_assert(m_isInitialized && "LU is not initialized.");
    eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!");
    return Inverse<FullPivLU>(*this);
  }
 
  MatrixType reconstructedMatrix() const;
 
  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
 
#ifndef EIGEN_PARSED_BY_DOXYGEN
  template <typename RhsType, typename DstType>
  void _solve_impl(const RhsType& rhs, DstType& dst) const;
 
  template <bool Conjugate, typename RhsType, typename DstType>
  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
#endif
 
 protected:
  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
 
  void computeInPlace();
 
  MatrixType m_lu;
  PermutationPType m_p;
  PermutationQType m_q;
  IntColVectorType m_rowsTranspositions;
  IntRowVectorType m_colsTranspositions;
  Index m_nonzero_pivots;
  RealScalar m_l1_norm;
  RealScalar m_maxpivot, m_prescribedThreshold;
  signed char m_det_pq;
  bool m_isInitialized, m_usePrescribedThreshold;
};
 
template <typename MatrixType, typename PermutationIndex>
FullPivLU<MatrixType, PermutationIndex>::FullPivLU() : m_isInitialized(false), m_usePrescribedThreshold(false) {}
 
template <typename MatrixType, typename PermutationIndex>
FullPivLU<MatrixType, PermutationIndex>::FullPivLU(Index rows, Index cols)
    : m_lu(rows, cols),
      m_p(rows),
      m_q(cols),
      m_rowsTranspositions(rows),
      m_colsTranspositions(cols),
      m_isInitialized(false),
      m_usePrescribedThreshold(false) {}
 
template <typename MatrixType, typename PermutationIndex>
template <typename InputType>
FullPivLU<MatrixType, PermutationIndex>::FullPivLU(const EigenBase<InputType>& matrix)
    : m_lu(matrix.rows(), matrix.cols()),
      m_p(matrix.rows()),
      m_q(matrix.cols()),
      m_rowsTranspositions(matrix.rows()),
      m_colsTranspositions(matrix.cols()),
      m_isInitialized(false),
      m_usePrescribedThreshold(false) {
  compute(matrix.derived());
}
 
template <typename MatrixType, typename PermutationIndex>
template <typename InputType>
FullPivLU<MatrixType, PermutationIndex>::FullPivLU(EigenBase<InputType>& matrix)
    : m_lu(matrix.derived()),
      m_p(matrix.rows()),
      m_q(matrix.cols()),
      m_rowsTranspositions(matrix.rows()),
      m_colsTranspositions(matrix.cols()),
      m_isInitialized(false),
      m_usePrescribedThreshold(false) {
  computeInPlace();
}
 
template <typename MatrixType, typename PermutationIndex>
void FullPivLU<MatrixType, PermutationIndex>::computeInPlace() {
  eigen_assert(m_lu.rows() <= NumTraits<PermutationIndex>::highest() &&
               m_lu.cols() <= NumTraits<PermutationIndex>::highest());
 
  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
 
  const Index size = m_lu.diagonalSize();
  const Index rows = m_lu.rows();
  const Index cols = m_lu.cols();
 
  // will store the transpositions, before we accumulate them at the end.
  // can't accumulate on-the-fly because that will be done in reverse order for the rows.
  m_rowsTranspositions.resize(m_lu.rows());
  m_colsTranspositions.resize(m_lu.cols());
  Index number_of_transpositions = 0;  // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
 
  m_nonzero_pivots = size;  // the generic case is that in which all pivots are nonzero (invertible case)
  m_maxpivot = RealScalar(0);
 
  for (Index k = 0; k < size; ++k) {
    // First, we need to find the pivot.
 
    // biggest coefficient in the remaining bottom-right corner (starting at row k, col k)
    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
    typedef typename Scoring::result_type Score;
    Score biggest_in_corner;
    biggest_in_corner = m_lu.bottomRightCorner(rows - k, cols - k)
                            .unaryExpr(Scoring())
                            .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
    row_of_biggest_in_corner += k;  // correct the values! since they were computed in the corner,
    col_of_biggest_in_corner += k;  // need to add k to them.
 
    if (numext::is_exactly_zero(biggest_in_corner)) {
      // before exiting, make sure to initialize the still uninitialized transpositions
      // in a sane state without destroying what we already have.
      m_nonzero_pivots = k;
      for (Index i = k; i < size; ++i) {
        m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
        m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
      }
      break;
    }
 
    RealScalar abs_pivot = internal::abs_knowing_score<Scalar>()(
        m_lu(row_of_biggest_in_corner, col_of_biggest_in_corner), biggest_in_corner);
    if (abs_pivot > m_maxpivot) m_maxpivot = abs_pivot;
 
    // Now that we've found the pivot, we need to apply the row/col swaps to
    // bring it to the location (k,k).
 
    m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
    m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
    if (k != row_of_biggest_in_corner) {
      m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
      ++number_of_transpositions;
    }
    if (k != col_of_biggest_in_corner) {
      m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
      ++number_of_transpositions;
    }
 
    // Now that the pivot is at the right location, we update the remaining
    // bottom-right corner by Gaussian elimination.
 
    if (k < rows - 1) m_lu.col(k).tail(rows - k - 1) /= m_lu.coeff(k, k);
    if (k < size - 1)
      m_lu.block(k + 1, k + 1, rows - k - 1, cols - k - 1).noalias() -=
          m_lu.col(k).tail(rows - k - 1) * m_lu.row(k).tail(cols - k - 1);
  }
 
  // the main loop is over, we still have to accumulate the transpositions to find the
  // permutations P and Q
 
  m_p.setIdentity(rows);
  for (Index k = size - 1; k >= 0; --k) m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k));
 
  m_q.setIdentity(cols);
  for (Index k = 0; k < size; ++k) m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
 
  m_det_pq = (number_of_transpositions % 2) ? -1 : 1;
 
  m_isInitialized = true;
}
 
template <typename MatrixType, typename PermutationIndex>
typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType, PermutationIndex>::determinant() const {
  eigen_assert(m_isInitialized && "LU is not initialized.");
  eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!");
  return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
}
 
template <typename MatrixType, typename PermutationIndex>
MatrixType FullPivLU<MatrixType, PermutationIndex>::reconstructedMatrix() const {
  eigen_assert(m_isInitialized && "LU is not initialized.");
  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
  // LU
  MatrixType res(m_lu.rows(), m_lu.cols());
  // FIXME the .toDenseMatrix() should not be needed...
  res = m_lu.leftCols(smalldim).template triangularView<UnitLower>().toDenseMatrix() *
        m_lu.topRows(smalldim).template triangularView<Upper>().toDenseMatrix();
 
  // P^{-1}(LU)
  res = m_p.inverse() * res;
 
  // (P^{-1}LU)Q^{-1}
  res = res * m_q.inverse();
 
  return res;
}
 
/********* Implementation of kernel() **************************************************/
 
namespace internal {
template <typename MatrixType_, typename PermutationIndex_>
struct kernel_retval<FullPivLU<MatrixType_, PermutationIndex_> >
    : kernel_retval_base<FullPivLU<MatrixType_, PermutationIndex_> > {
  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
  EIGEN_MAKE_KERNEL_HELPERS(DecompositionType)
 
  enum {
    MaxSmallDimAtCompileTime = min_size_prefer_fixed(MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime)
  };
 
  template <typename Dest>
  void evalTo(Dest& dst) const {
    using std::abs;
    const Index cols = dec().matrixLU().cols(), dimker = cols - rank();
    if (dimker == 0) {
      // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
      // avoid crashing/asserting as that depends on floating point calculations. Let's
      // just return a single column vector filled with zeros.
      dst.setZero();
      return;
    }
 
    /* Let us use the following lemma:
     *
     * Lemma: If the matrix A has the LU decomposition PAQ = LU,
     * then Ker A = Q(Ker U).
     *
     * Proof: trivial: just keep in mind that P, Q, L are invertible.
     */
 
    /* Thus, all we need to do is to compute Ker U, and then apply Q.
     *
     * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
     * Thus, the diagonal of U ends with exactly
     * dimKer zero's. Let us use that to construct dimKer linearly
     * independent vectors in Ker U.
     */
 
    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
    Index p = 0;
    for (Index i = 0; i < dec().nonzeroPivots(); ++i)
      if (abs(dec().matrixLU().coeff(i, i)) > premultiplied_threshold) pivots.coeffRef(p++) = i;
    eigen_internal_assert(p == rank());
 
    // we construct a temporaty trapezoid matrix m, by taking the U matrix and
    // permuting the rows and cols to bring the nonnegligible pivots to the top of
    // the main diagonal. We need that to be able to apply our triangular solvers.
    // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified
    Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, traits<MatrixType>::Options, MaxSmallDimAtCompileTime,
           MatrixType::MaxColsAtCompileTime>
        m(dec().matrixLU().block(0, 0, rank(), cols));
    for (Index i = 0; i < rank(); ++i) {
      if (i) m.row(i).head(i).setZero();
      m.row(i).tail(cols - i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols - i);
    }
    m.block(0, 0, rank(), rank());
    m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero();
    for (Index i = 0; i < rank(); ++i) m.col(i).swap(m.col(pivots.coeff(i)));
 
    // ok, we have our trapezoid matrix, we can apply the triangular solver.
    // notice that the math behind this suggests that we should apply this to the
    // negative of the RHS, but for performance we just put the negative sign elsewhere, see below.
    m.topLeftCorner(rank(), rank()).template triangularView<Upper>().solveInPlace(m.topRightCorner(rank(), dimker));
 
    // now we must undo the column permutation that we had applied!
    for (Index i = rank() - 1; i >= 0; --i) m.col(i).swap(m.col(pivots.coeff(i)));
 
    // see the negative sign in the next line, that's what we were talking about above.
    for (Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker);
    for (Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero();
    for (Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank() + k), k) = Scalar(1);
  }
};
 
/***** Implementation of image() *****************************************************/
 
template <typename MatrixType_, typename PermutationIndex_>
struct image_retval<FullPivLU<MatrixType_, PermutationIndex_> >
    : image_retval_base<FullPivLU<MatrixType_, PermutationIndex_> > {
  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
  EIGEN_MAKE_IMAGE_HELPERS(DecompositionType)
 
  enum {
    MaxSmallDimAtCompileTime = min_size_prefer_fixed(MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime)
  };
 
  template <typename Dest>
  void evalTo(Dest& dst) const {
    using std::abs;
    if (rank() == 0) {
      // The Image is just {0}, so it doesn't have a basis properly speaking, but let's
      // avoid crashing/asserting as that depends on floating point calculations. Let's
      // just return a single column vector filled with zeros.
      dst.setZero();
      return;
    }
 
    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
    Index p = 0;
    for (Index i = 0; i < dec().nonzeroPivots(); ++i)
      if (abs(dec().matrixLU().coeff(i, i)) > premultiplied_threshold) pivots.coeffRef(p++) = i;
    eigen_internal_assert(p == rank());
 
    for (Index i = 0; i < rank(); ++i)
      dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i)));
  }
};
 
/***** Implementation of solve() *****************************************************/
 
}  // end namespace internal
 
#ifndef EIGEN_PARSED_BY_DOXYGEN
template <typename MatrixType_, typename PermutationIndex_>
template <typename RhsType, typename DstType>
void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.
   * So we proceed as follows:
   * Step 1: compute c = P * rhs.
   * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible.
   * Step 3: replace c by the solution x to Ux = c. May or may not exist.
   * Step 4: result = Q * c;
   */
 
  const Index rows = this->rows(), cols = this->cols(), nonzero_pivots = this->rank();
  const Index smalldim = (std::min)(rows, cols);
 
  if (nonzero_pivots == 0) {
    dst.setZero();
    return;
  }
 
  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
 
  // Step 1
  c = permutationP() * rhs;
 
  // Step 2
  m_lu.topLeftCorner(smalldim, smalldim).template triangularView<UnitLower>().solveInPlace(c.topRows(smalldim));
  if (rows > cols) c.bottomRows(rows - cols) -= m_lu.bottomRows(rows - cols) * c.topRows(cols);
 
  // Step 3
  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
      .template triangularView<Upper>()
      .solveInPlace(c.topRows(nonzero_pivots));
 
  // Step 4
  for (Index i = 0; i < nonzero_pivots; ++i) dst.row(permutationQ().indices().coeff(i)) = c.row(i);
  for (Index i = nonzero_pivots; i < m_lu.cols(); ++i) dst.row(permutationQ().indices().coeff(i)).setZero();
}
 
template <typename MatrixType_, typename PermutationIndex_>
template <bool Conjugate, typename RhsType, typename DstType>
void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1},
   * and since permutations are real and unitary, we can write this
   * as   A^T = Q U^T L^T P,
   * So we proceed as follows:
   * Step 1: compute c = Q^T rhs.
   * Step 2: replace c by the solution x to U^T x = c. May or may not exist.
   * Step 3: replace c by the solution x to L^T x = c.
   * Step 4: result = P^T c.
   * If Conjugate is true, replace "^T" by "^*" above.
   */
 
  const Index rows = this->rows(), cols = this->cols(), nonzero_pivots = this->rank();
  const Index smalldim = (std::min)(rows, cols);
 
  if (nonzero_pivots == 0) {
    dst.setZero();
    return;
  }
 
  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
 
  // Step 1
  c = permutationQ().inverse() * rhs;
 
  // Step 2
  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
      .template triangularView<Upper>()
      .transpose()
      .template conjugateIf<Conjugate>()
      .solveInPlace(c.topRows(nonzero_pivots));
 
  // Step 3
  m_lu.topLeftCorner(smalldim, smalldim)
      .template triangularView<UnitLower>()
      .transpose()
      .template conjugateIf<Conjugate>()
      .solveInPlace(c.topRows(smalldim));
 
  // Step 4
  PermutationPType invp = permutationP().inverse().eval();
  for (Index i = 0; i < smalldim; ++i) dst.row(invp.indices().coeff(i)) = c.row(i);
  for (Index i = smalldim; i < rows; ++i) dst.row(invp.indices().coeff(i)).setZero();
}
 
#endif
 
namespace internal {
 
/***** Implementation of inverse() *****************************************************/
template <typename DstXprType, typename MatrixType, typename PermutationIndex>
struct Assignment<
    DstXprType, Inverse<FullPivLU<MatrixType, PermutationIndex> >,
    internal::assign_op<typename DstXprType::Scalar, typename FullPivLU<MatrixType, PermutationIndex>::Scalar>,
    Dense2Dense> {
  typedef FullPivLU<MatrixType, PermutationIndex> LuType;
  typedef Inverse<LuType> SrcXprType;
  static void run(DstXprType& dst, const SrcXprType& src,
                  const internal::assign_op<typename DstXprType::Scalar, typename MatrixType::Scalar>&) {
    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
  }
};
}  // end namespace internal
 
/******* MatrixBase methods *****************************************************************/
 
template <typename Derived>
template <typename PermutationIndex>
inline const FullPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex> MatrixBase<Derived>::fullPivLu()
    const {
  return FullPivLU<PlainObject, PermutationIndex>(eval());
}
 
}  // end namespace Eigen
 
#endif  // EIGEN_LU_H