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diff --git a/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h b/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
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--- a/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h
+++ /dev/null
@@ -1,616 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
-// Copyright (C) 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_FULLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen { 
-
-namespace internal {
-
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType;
-
-template<typename MatrixType>
-struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-  typedef typename MatrixType::PlainObject ReturnType;
-};
-
-}
-
-/** \ingroup QR_Module
-  *
-  * \class FullPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting
-  *
-  * \param MatrixType the type of the matrix of which we are computing the QR decomposition
-  *
-  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
-  * such that 
-  * \f[
-  *  \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix 
-  * and \b R an upper triangular matrix.
-  *
-  * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
-  * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.
-  *
-  * \sa MatrixBase::fullPivHouseholderQr()
-  */
-template<typename _MatrixType> class FullPivHouseholderQR
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      Options = MatrixType::Options,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    typedef typename MatrixType::Index Index;
-    typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef Matrix<Index, 1,
-                   EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime,RowsAtCompileTime), RowMajor, 1,
-                   EIGEN_SIZE_MIN_PREFER_FIXED(MaxColsAtCompileTime,MaxRowsAtCompileTime)> IntDiagSizeVectorType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_col_type<MatrixType>::type ColVectorType;
-
-    /** \brief Default Constructor.
-      *
-      * The default constructor is useful in cases in which the user intends to
-      * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
-      */
-    FullPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_rows_transpositions(),
-        m_cols_transpositions(),
-        m_cols_permutation(),
-        m_temp(),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Default Constructor with memory preallocation
-      *
-      * Like the default constructor but with preallocation of the internal data
-      * according to the specified problem \a size.
-      * \sa FullPivHouseholderQR()
-      */
-    FullPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_rows_transpositions((std::min)(rows,cols)),
-        m_cols_transpositions((std::min)(rows,cols)),
-        m_cols_permutation(cols),
-        m_temp(cols),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false) {}
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This constructor computes the QR factorization of the matrix \a matrix by calling
-      * the method compute(). It is a short cut for:
-      * 
-      * \code
-      * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      * 
-      * \sa compute()
-      */
-    FullPivHouseholderQR(const MatrixType& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
-        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
-        m_cols_permutation(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix);
-    }
-
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * \c *this is the QR decomposition.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A,
-      * and an arbitrary solution otherwise.
-      *
-      * \note The case where b is a matrix is not yet implemented. Also, this
-      *       code is space inefficient.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include FullPivHouseholderQR_solve.cpp
-      * Output: \verbinclude FullPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const internal::solve_retval<FullPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return internal::solve_retval<FullPivHouseholderQR, Rhs>(*this, b.derived());
-    }
-
-    /** \returns Expression object representing the matrix Q
-      */
-    MatrixQReturnType matrixQ(void) const;
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    FullPivHouseholderQR& compute(const MatrixType& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_cols_permutation;
-    }
-
-    /** \returns a const reference to the vector of indices representing the rows transpositions */
-    const IntDiagSizeVectorType& rowsTranspositions() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return m_rows_transpositions;
-    }
-
-    /** \returns the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \warning a determinant can be very big or small, so for matrices
-      * of large enough dimension, there is a risk of overflow/underflow.
-      * One way to work around that is to use logAbsDeterminant() instead.
-      *
-      * \sa logAbsDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar absDeterminant() const;
-
-    /** \returns the natural log of the absolute value of the determinant of the matrix of which
-      * *this is the QR decomposition. It has only linear complexity
-      * (that is, O(n) where n is the dimension of the square matrix)
-      * as the QR decomposition has already been computed.
-      *
-      * \note This is only for square matrices.
-      *
-      * \note This method is useful to work around the risk of overflow/underflow that's inherent
-      * to determinant computation.
-      *
-      * \sa absDeterminant(), MatrixBase::determinant()
-      */
-    typename MatrixType::RealScalar logAbsDeterminant() const;
-
-    /** \returns the rank of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index rank() const
-    {
-      using std::abs;
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR 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_qr.coeff(i,i)) > premultiplied_threshold);
-      return result;
-    }
-
-    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline Index dimensionOfKernel() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return cols() - rank();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
-      *          linear map, i.e. has trivial kernel; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == cols();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
-      *          linear map; false otherwise.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isSurjective() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return rank() == rows();
-    }
-
-    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
-      *
-      * \note This method has to determine which pivots should be considered nonzero.
-      *       For that, it uses the threshold value that you can control by calling
-      *       setThreshold(const RealScalar&).
-      */
-    inline bool isInvertible() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return isInjective() && isSurjective();
-    }
-
-    /** \returns the inverse of the matrix of which *this is the QR decomposition.
-      *
-      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
-      *       Use isInvertible() to first determine whether this matrix is invertible.
-      */    inline const
-    internal::solve_retval<FullPivHouseholderQR, typename MatrixType::IdentityReturnType>
-    inverse() const
-    {
-      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-      return internal::solve_retval<FullPivHouseholderQR,typename MatrixType::IdentityReturnType>
-               (*this, MatrixType::Identity(m_qr.rows(), m_qr.cols()));
-    }
-
-    inline Index rows() const { return m_qr.rows(); }
-    inline Index cols() const { return m_qr.cols(); }
-    
-    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
-      * 
-      * For advanced uses only.
-      */
-    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
-
-    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
-      * who need to determine when pivots are to be considered nonzero. This is not used for the
-      * QR decomposition itself.
-      *
-      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
-      * uses a formula to automatically determine a reasonable threshold.
-      * Once you have called the present method setThreshold(const RealScalar&),
-      * your value is used instead.
-      *
-      * \param threshold The new value to use as the threshold.
-      *
-      * A pivot will be considered nonzero if its absolute value is strictly greater than
-      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
-      * where maxpivot is the biggest pivot.
-      *
-      * If you want to come back to the default behavior, call setThreshold(Default_t)
-      */
-    FullPivHouseholderQR& setThreshold(const RealScalar& threshold)
-    {
-      m_usePrescribedThreshold = true;
-      m_prescribedThreshold = threshold;
-      return *this;
-    }
-
-    /** Allows to come back to the default behavior, letting Eigen use its default formula for
-      * determining the threshold.
-      *
-      * You should pass the special object Eigen::Default as parameter here.
-      * \code qr.setThreshold(Eigen::Default); \endcode
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    FullPivHouseholderQR& setThreshold(Default_t)
-    {
-      m_usePrescribedThreshold = false;
-      return *this;
-    }
-
-    /** Returns the threshold that will be used by certain methods such as rank().
-      *
-      * See the documentation of setThreshold(const RealScalar&).
-      */
-    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_qr.diagonalSize());
-    }
-
-    /** \returns the number of nonzero pivots in the QR decomposition.
-      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
-      * So that notion isn't really intrinsically interesting, but it is
-      * still useful when implementing algorithms.
-      *
-      * \sa rank()
-      */
-    inline Index nonzeroPivots() const
-    {
-      eigen_assert(m_isInitialized && "LU is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of U.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-  protected:
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    IntDiagSizeVectorType m_rows_transpositions;
-    IntDiagSizeVectorType m_cols_transpositions;
-    PermutationType m_cols_permutation;
-    RowVectorType m_temp;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    RealScalar m_precision;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return abs(m_qr.diagonal().prod());
-}
-
-template<typename MatrixType>
-typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
-  return m_qr.diagonal().cwiseAbs().array().log().sum();
-}
-
-/** Performs the QR factorization of the given matrix \a matrix. The result of
-  * the factorization is stored into \c *this, and a reference to \c *this
-  * is returned.
-  *
-  * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
-{
-  using std::abs;
-  Index rows = matrix.rows();
-  Index cols = matrix.cols();
-  Index size = (std::min)(rows,cols);
-
-  m_qr = matrix;
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
-
-  m_rows_transpositions.resize(size);
-  m_cols_transpositions.resize(size);
-  Index number_of_transpositions = 0;
-
-  RealScalar biggest(0);
-
-  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)
-  {
-    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
-    RealScalar biggest_in_corner;
-
-    biggest_in_corner = m_qr.bottomRightCorner(rows-k, cols-k)
-                            .cwiseAbs()
-                            .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
-    row_of_biggest_in_corner += k;
-    col_of_biggest_in_corner += k;
-    if(k==0) biggest = biggest_in_corner;
-
-    // if the corner is negligible, then we have less than full rank, and we can finish early
-    if(internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision))
-    {
-      m_nonzero_pivots = k;
-      for(Index i = k; i < size; i++)
-      {
-        m_rows_transpositions.coeffRef(i) = i;
-        m_cols_transpositions.coeffRef(i) = i;
-        m_hCoeffs.coeffRef(i) = Scalar(0);
-      }
-      break;
-    }
-
-    m_rows_transpositions.coeffRef(k) = row_of_biggest_in_corner;
-    m_cols_transpositions.coeffRef(k) = col_of_biggest_in_corner;
-    if(k != row_of_biggest_in_corner) {
-      m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k));
-      ++number_of_transpositions;
-    }
-    if(k != col_of_biggest_in_corner) {
-      m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner));
-      ++number_of_transpositions;
-    }
-
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    m_qr.bottomRightCorner(rows-k, cols-k-1)
-        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
-  }
-
-  m_cols_permutation.setIdentity(cols);
-  for(Index k = 0; k < size; ++k)
-    m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-
-  return *this;
-}
-
-namespace internal {
-
-template<typename _MatrixType, typename Rhs>
-struct solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
-  : solve_retval_base<FullPivHouseholderQR<_MatrixType>, Rhs>
-{
-  EIGEN_MAKE_SOLVE_HELPERS(FullPivHouseholderQR<_MatrixType>,Rhs)
-
-  template<typename Dest> void evalTo(Dest& dst) const
-  {
-    const Index rows = dec().rows(), cols = dec().cols();
-    eigen_assert(rhs().rows() == rows);
-
-    // FIXME introduce nonzeroPivots() and use it here. and more generally,
-    // make the same improvements in this dec as in FullPivLU.
-    if(dec().rank()==0)
-    {
-      dst.setZero();
-      return;
-    }
-
-    typename Rhs::PlainObject c(rhs());
-
-    Matrix<Scalar,1,Rhs::ColsAtCompileTime> temp(rhs().cols());
-    for (Index k = 0; k < dec().rank(); ++k)
-    {
-      Index remainingSize = rows-k;
-      c.row(k).swap(c.row(dec().rowsTranspositions().coeff(k)));
-      c.bottomRightCorner(remainingSize, rhs().cols())
-       .applyHouseholderOnTheLeft(dec().matrixQR().col(k).tail(remainingSize-1),
-                                  dec().hCoeffs().coeff(k), &temp.coeffRef(0));
-    }
-
-    dec().matrixQR()
-       .topLeftCorner(dec().rank(), dec().rank())
-       .template triangularView<Upper>()
-       .solveInPlace(c.topRows(dec().rank()));
-
-    for(Index i = 0; i < dec().rank(); ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
-    for(Index i = dec().rank(); i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
-  }
-};
-
-/** \ingroup QR_Module
-  *
-  * \brief Expression type for return value of FullPivHouseholderQR::matrixQ()
-  *
-  * \tparam MatrixType type of underlying dense matrix
-  */
-template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType
-  : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
-{
-public:
-  typedef typename MatrixType::Index Index;
-  typedef typename FullPivHouseholderQR<MatrixType>::IntDiagSizeVectorType IntDiagSizeVectorType;
-  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-  typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1,
-                 MatrixType::MaxRowsAtCompileTime> WorkVectorType;
-
-  FullPivHouseholderQRMatrixQReturnType(const MatrixType&       qr,
-                                        const HCoeffsType&      hCoeffs,
-                                        const IntDiagSizeVectorType& rowsTranspositions)
-    : m_qr(qr),
-      m_hCoeffs(hCoeffs),
-      m_rowsTranspositions(rowsTranspositions)
-      {}
-
-  template <typename ResultType>
-  void evalTo(ResultType& result) const
-  {
-    const Index rows = m_qr.rows();
-    WorkVectorType workspace(rows);
-    evalTo(result, workspace);
-  }
-
-  template <typename ResultType>
-  void evalTo(ResultType& result, WorkVectorType& workspace) const
-  {
-    using numext::conj;
-    // compute the product H'_0 H'_1 ... H'_n-1,
-    // where H_k is the k-th Householder transformation I - h_k v_k v_k'
-    // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
-    const Index rows = m_qr.rows();
-    const Index cols = m_qr.cols();
-    const Index size = (std::min)(rows, cols);
-    workspace.resize(rows);
-    result.setIdentity(rows, rows);
-    for (Index k = size-1; k >= 0; k--)
-    {
-      result.block(k, k, rows-k, rows-k)
-            .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k));
-      result.row(k).swap(result.row(m_rowsTranspositions.coeff(k)));
-    }
-  }
-
-    Index rows() const { return m_qr.rows(); }
-    Index cols() const { return m_qr.rows(); }
-
-protected:
-  typename MatrixType::Nested m_qr;
-  typename HCoeffsType::Nested m_hCoeffs;
-  typename IntDiagSizeVectorType::Nested m_rowsTranspositions;
-};
-
-} // end namespace internal
-
-template<typename MatrixType>
-inline typename FullPivHouseholderQR<MatrixType>::MatrixQReturnType FullPivHouseholderQR<MatrixType>::matrixQ() const
-{
-  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
-  return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions);
-}
-
-#ifndef __CUDACC__
-/** \return the full-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class FullPivHouseholderQR
-  */
-template<typename Derived>
-const FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::fullPivHouseholderQr() const
-{
-  return FullPivHouseholderQR<PlainObject>(eval());
-}
-#endif // __CUDACC__
-
-} // end namespace Eigen
-
-#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H