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diff --git a/runtimes/nn/depend/external/eigen/Eigen/src/QR/ColPivHouseholderQR.h b/runtimes/nn/depend/external/eigen/Eigen/src/QR/ColPivHouseholderQR.h
deleted file mode 100644
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--- a/runtimes/nn/depend/external/eigen/Eigen/src/QR/ColPivHouseholderQR.h
+++ /dev/null
@@ -1,653 +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_COLPIVOTINGHOUSEHOLDERQR_H
-#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
-
-namespace Eigen {
-
-namespace internal {
-template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> >
- : traits<_MatrixType>
-{
-  enum { Flags = 0 };
-};
-
-} // end namespace internal
-
-/** \ingroup QR_Module
-  *
-  * \class ColPivHouseholderQR
-  *
-  * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting
-  *
-  * \tparam _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 Q and \b R
-  * such that
-  * \f[
-  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
-  * \f]
-  * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
-  * upper triangular matrix.
-  *
-  * This decomposition performs column pivoting in order to be rank-revealing and improve
-  * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
-  *
-  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
-  * 
-  * \sa MatrixBase::colPivHouseholderQr()
-  */
-template<typename _MatrixType> class ColPivHouseholderQR
-{
-  public:
-
-    typedef _MatrixType MatrixType;
-    enum {
-      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
-      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
-      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
-      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
-    };
-    typedef typename MatrixType::Scalar Scalar;
-    typedef typename MatrixType::RealScalar RealScalar;
-    // FIXME should be int
-    typedef typename MatrixType::StorageIndex StorageIndex;
-    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
-    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
-    typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
-    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
-    typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
-    typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
-    typedef typename MatrixType::PlainObject PlainObject;
-
-  private:
-
-    typedef typename PermutationType::StorageIndex PermIndexType;
-
-  public:
-
-    /**
-    * \brief Default Constructor.
-    *
-    * The default constructor is useful in cases in which the user intends to
-    * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
-    */
-    ColPivHouseholderQR()
-      : m_qr(),
-        m_hCoeffs(),
-        m_colsPermutation(),
-        m_colsTranspositions(),
-        m_temp(),
-        m_colNormsUpdated(),
-        m_colNormsDirect(),
-        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 ColPivHouseholderQR()
-      */
-    ColPivHouseholderQR(Index rows, Index cols)
-      : m_qr(rows, cols),
-        m_hCoeffs((std::min)(rows,cols)),
-        m_colsPermutation(PermIndexType(cols)),
-        m_colsTranspositions(cols),
-        m_temp(cols),
-        m_colNormsUpdated(cols),
-        m_colNormsDirect(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
-      * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
-      * qr.compute(matrix);
-      * \endcode
-      *
-      * \sa compute()
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix)
-      : m_qr(matrix.rows(), matrix.cols()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      compute(matrix.derived());
-    }
-
-    /** \brief Constructs a QR factorization from a given matrix
-      *
-      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
-      *
-      * \sa ColPivHouseholderQR(const EigenBase&)
-      */
-    template<typename InputType>
-    explicit ColPivHouseholderQR(EigenBase<InputType>& matrix)
-      : m_qr(matrix.derived()),
-        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
-        m_colsPermutation(PermIndexType(matrix.cols())),
-        m_colsTranspositions(matrix.cols()),
-        m_temp(matrix.cols()),
-        m_colNormsUpdated(matrix.cols()),
-        m_colNormsDirect(matrix.cols()),
-        m_isInitialized(false),
-        m_usePrescribedThreshold(false)
-    {
-      computeInPlace();
-    }
-
-    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
-      * *this is the QR decomposition, if any exists.
-      *
-      * \param b the right-hand-side of the equation to solve.
-      *
-      * \returns a solution.
-      *
-      * \note_about_checking_solutions
-      *
-      * \note_about_arbitrary_choice_of_solution
-      *
-      * Example: \include ColPivHouseholderQR_solve.cpp
-      * Output: \verbinclude ColPivHouseholderQR_solve.out
-      */
-    template<typename Rhs>
-    inline const Solve<ColPivHouseholderQR, Rhs>
-    solve(const MatrixBase<Rhs>& b) const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return Solve<ColPivHouseholderQR, Rhs>(*this, b.derived());
-    }
-
-    HouseholderSequenceType householderQ() const;
-    HouseholderSequenceType matrixQ() const
-    {
-      return householderQ();
-    }
-
-    /** \returns a reference to the matrix where the Householder QR decomposition is stored
-      */
-    const MatrixType& matrixQR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    /** \returns a reference to the matrix where the result Householder QR is stored
-     * \warning The strict lower part of this matrix contains internal values.
-     * Only the upper triangular part should be referenced. To get it, use
-     * \code matrixR().template triangularView<Upper>() \endcode
-     * For rank-deficient matrices, use
-     * \code
-     * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
-     * \endcode
-     */
-    const MatrixType& matrixR() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_qr;
-    }
-
-    template<typename InputType>
-    ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
-
-    /** \returns a const reference to the column permutation matrix */
-    const PermutationType& colsPermutation() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return m_colsPermutation;
-    }
-
-    /** \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 && "ColPivHouseholderQR 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 && "ColPivHouseholderQR 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 && "ColPivHouseholderQR 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 && "ColPivHouseholderQR 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 && "ColPivHouseholderQR 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 Inverse<ColPivHouseholderQR> inverse() const
-    {
-      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-      return Inverse<ColPivHouseholderQR>(*this);
-    }
-
-    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)
-      */
-    ColPivHouseholderQR& 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&).
-      */
-    ColPivHouseholderQR& 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 && "ColPivHouseholderQR is not initialized.");
-      return m_nonzero_pivots;
-    }
-
-    /** \returns the absolute value of the biggest pivot, i.e. the biggest
-      *          diagonal coefficient of R.
-      */
-    RealScalar maxPivot() const { return m_maxpivot; }
-
-    /** \brief Reports whether the QR factorization was succesful.
-      *
-      * \note This function always returns \c Success. It is provided for compatibility
-      * with other factorization routines.
-      * \returns \c Success
-      */
-    ComputationInfo info() const
-    {
-      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
-      return Success;
-    }
-
-    #ifndef EIGEN_PARSED_BY_DOXYGEN
-    template<typename RhsType, typename DstType>
-    EIGEN_DEVICE_FUNC
-    void _solve_impl(const RhsType &rhs, DstType &dst) const;
-    #endif
-
-  protected:
-
-    friend class CompleteOrthogonalDecomposition<MatrixType>;
-
-    static void check_template_parameters()
-    {
-      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
-    }
-
-    void computeInPlace();
-
-    MatrixType m_qr;
-    HCoeffsType m_hCoeffs;
-    PermutationType m_colsPermutation;
-    IntRowVectorType m_colsTranspositions;
-    RowVectorType m_temp;
-    RealRowVectorType m_colNormsUpdated;
-    RealRowVectorType m_colNormsDirect;
-    bool m_isInitialized, m_usePrescribedThreshold;
-    RealScalar m_prescribedThreshold, m_maxpivot;
-    Index m_nonzero_pivots;
-    Index m_det_pq;
-};
-
-template<typename MatrixType>
-typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::absDeterminant() const
-{
-  using std::abs;
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR 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 ColPivHouseholderQR<MatrixType>::logAbsDeterminant() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR 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 ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
-  */
-template<typename MatrixType>
-template<typename InputType>
-ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
-{
-  m_qr = matrix.derived();
-  computeInPlace();
-  return *this;
-}
-
-template<typename MatrixType>
-void ColPivHouseholderQR<MatrixType>::computeInPlace()
-{
-  check_template_parameters();
-
-  // the column permutation is stored as int indices, so just to be sure:
-  eigen_assert(m_qr.cols()<=NumTraits<int>::highest());
-
-  using std::abs;
-
-  Index rows = m_qr.rows();
-  Index cols = m_qr.cols();
-  Index size = m_qr.diagonalSize();
-
-  m_hCoeffs.resize(size);
-
-  m_temp.resize(cols);
-
-  m_colsTranspositions.resize(m_qr.cols());
-  Index number_of_transpositions = 0;
-
-  m_colNormsUpdated.resize(cols);
-  m_colNormsDirect.resize(cols);
-  for (Index k = 0; k < cols; ++k) {
-    // colNormsDirect(k) caches the most recent directly computed norm of
-    // column k.
-    m_colNormsDirect.coeffRef(k) = m_qr.col(k).norm();
-    m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
-  }
-
-  RealScalar threshold_helper =  numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
-  RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
-
-  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 look up in our table m_colNormsUpdated which column has the biggest norm
-    Index biggest_col_index;
-    RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols-k).maxCoeff(&biggest_col_index));
-    biggest_col_index += k;
-
-    // Track the number of meaningful pivots but do not stop the decomposition to make
-    // sure that the initial matrix is properly reproduced. See bug 941.
-    if(m_nonzero_pivots==size && biggest_col_sq_norm < threshold_helper * RealScalar(rows-k))
-      m_nonzero_pivots = k;
-
-    // apply the transposition to the columns
-    m_colsTranspositions.coeffRef(k) = biggest_col_index;
-    if(k != biggest_col_index) {
-      m_qr.col(k).swap(m_qr.col(biggest_col_index));
-      std::swap(m_colNormsUpdated.coeffRef(k), m_colNormsUpdated.coeffRef(biggest_col_index));
-      std::swap(m_colNormsDirect.coeffRef(k), m_colNormsDirect.coeffRef(biggest_col_index));
-      ++number_of_transpositions;
-    }
-
-    // generate the householder vector, store it below the diagonal
-    RealScalar beta;
-    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
-
-    // apply the householder transformation to the diagonal coefficient
-    m_qr.coeffRef(k,k) = beta;
-
-    // remember the maximum absolute value of diagonal coefficients
-    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
-
-    // apply the householder transformation
-    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));
-
-    // update our table of norms of the columns
-    for (Index j = k + 1; j < cols; ++j) {
-      // The following implements the stable norm downgrade step discussed in
-      // http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
-      // and used in LAPACK routines xGEQPF and xGEQP3.
-      // See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
-      if (m_colNormsUpdated.coeffRef(j) != RealScalar(0)) {
-        RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
-        temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
-        temp = temp <  RealScalar(0) ? RealScalar(0) : temp;
-        RealScalar temp2 = temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) /
-                                                           m_colNormsDirect.coeffRef(j));
-        if (temp2 <= norm_downdate_threshold) {
-          // The updated norm has become too inaccurate so re-compute the column
-          // norm directly.
-          m_colNormsDirect.coeffRef(j) = m_qr.col(j).tail(rows - k - 1).norm();
-          m_colNormsUpdated.coeffRef(j) = m_colNormsDirect.coeffRef(j);
-        } else {
-          m_colNormsUpdated.coeffRef(j) *= numext::sqrt(temp);
-        }
-      }
-    }
-  }
-
-  m_colsPermutation.setIdentity(PermIndexType(cols));
-  for(PermIndexType k = 0; k < size/*m_nonzero_pivots*/; ++k)
-    m_colsPermutation.applyTranspositionOnTheRight(k, PermIndexType(m_colsTranspositions.coeff(k)));
-
-  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
-  m_isInitialized = true;
-}
-
-#ifndef EIGEN_PARSED_BY_DOXYGEN
-template<typename _MatrixType>
-template<typename RhsType, typename DstType>
-void ColPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const
-{
-  eigen_assert(rhs.rows() == rows());
-
-  const Index nonzero_pivots = nonzeroPivots();
-
-  if(nonzero_pivots == 0)
-  {
-    dst.setZero();
-    return;
-  }
-
-  typename RhsType::PlainObject c(rhs);
-
-  // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T
-  c.applyOnTheLeft(householderSequence(m_qr, m_hCoeffs)
-                    .setLength(nonzero_pivots)
-                    .transpose()
-    );
-
-  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
-      .template triangularView<Upper>()
-      .solveInPlace(c.topRows(nonzero_pivots));
-
-  for(Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
-  for(Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
-}
-#endif
-
-namespace internal {
-
-template<typename DstXprType, typename MatrixType>
-struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename ColPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense>
-{
-  typedef ColPivHouseholderQR<MatrixType> QrType;
-  typedef Inverse<QrType> SrcXprType;
-  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
-  {
-    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
-  }
-};
-
-} // end namespace internal
-
-/** \returns the matrix Q as a sequence of householder transformations.
-  * You can extract the meaningful part only by using:
-  * \code qr.householderQ().setLength(qr.nonzeroPivots()) \endcode*/
-template<typename MatrixType>
-typename ColPivHouseholderQR<MatrixType>::HouseholderSequenceType ColPivHouseholderQR<MatrixType>
-  ::householderQ() const
-{
-  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
-  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
-}
-
-/** \return the column-pivoting Householder QR decomposition of \c *this.
-  *
-  * \sa class ColPivHouseholderQR
-  */
-template<typename Derived>
-const ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject>
-MatrixBase<Derived>::colPivHouseholderQr() const
-{
-  return ColPivHouseholderQR<PlainObject>(eval());
-}
-
-} // end namespace Eigen
-
-#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H