diff options
Diffstat (limited to 'src/3rdparty/eigen/Eigen/src/QR/FullPivHouseholderQR.h')
| -rw-r--r-- | src/3rdparty/eigen/Eigen/src/QR/FullPivHouseholderQR.h | 713 |
1 files changed, 713 insertions, 0 deletions
diff --git a/src/3rdparty/eigen/Eigen/src/QR/FullPivHouseholderQR.h b/src/3rdparty/eigen/Eigen/src/QR/FullPivHouseholderQR.h new file mode 100644 index 000000000..d0664a1d8 --- /dev/null +++ b/src/3rdparty/eigen/Eigen/src/QR/FullPivHouseholderQR.h @@ -0,0 +1,713 @@ +// 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 traits<FullPivHouseholderQR<_MatrixType> > + : traits<_MatrixType> +{ + typedef MatrixXpr XprKind; + typedef SolverStorage StorageKind; + typedef int StorageIndex; + enum { Flags = 0 }; +}; + +template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType; + +template<typename MatrixType> +struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> > +{ + typedef typename MatrixType::PlainObject ReturnType; +}; + +} // end namespace internal + +/** \ingroup QR_Module + * + * \class FullPivHouseholderQR + * + * \brief Householder rank-revealing QR decomposition of a matrix with full 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 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. + * + * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism. + * + * \sa MatrixBase::fullPivHouseholderQr() + */ +template<typename _MatrixType> class FullPivHouseholderQR + : public SolverBase<FullPivHouseholderQR<_MatrixType> > +{ + public: + + typedef _MatrixType MatrixType; + typedef SolverBase<FullPivHouseholderQR> Base; + friend class SolverBase<FullPivHouseholderQR>; + + EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR) + enum { + MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; + typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType; + typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; + typedef Matrix<StorageIndex, 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; + typedef typename MatrixType::PlainObject PlainObject; + + /** \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() + */ + template<typename InputType> + explicit FullPivHouseholderQR(const EigenBase<InputType>& 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.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 FullPivHouseholderQR(const EigenBase&) + */ + template<typename InputType> + explicit FullPivHouseholderQR(EigenBase<InputType>& matrix) + : m_qr(matrix.derived()), + 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) + { + computeInPlace(); + } + + #ifdef EIGEN_PARSED_BY_DOXYGEN + /** 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_about_checking_solutions + * + * \note_about_arbitrary_choice_of_solution + * + * Example: \include FullPivHouseholderQR_solve.cpp + * Output: \verbinclude FullPivHouseholderQR_solve.out + */ + template<typename Rhs> + inline const Solve<FullPivHouseholderQR, Rhs> + solve(const MatrixBase<Rhs>& b) const; + #endif + + /** \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; + } + + template<typename InputType> + FullPivHouseholderQR& compute(const EigenBase<InputType>& 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 Inverse<FullPivHouseholderQR> inverse() const + { + eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); + return Inverse<FullPivHouseholderQR>(*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) + */ + 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; } + + #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: + + static void check_template_parameters() + { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar); + } + + void computeInPlace(); + + 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> +template<typename InputType> +FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix) +{ + m_qr = matrix.derived(); + computeInPlace(); + return *this; +} + +template<typename MatrixType> +void FullPivHouseholderQR<MatrixType>::computeInPlace() +{ + check_template_parameters(); + + using std::abs; + Index rows = m_qr.rows(); + Index cols = m_qr.cols(); + Index size = (std::min)(rows,cols); + + + 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; + typedef internal::scalar_score_coeff_op<Scalar> Scoring; + typedef typename Scoring::result_type Score; + + Score score = m_qr.bottomRightCorner(rows-k, cols-k) + .unaryExpr(Scoring()) + .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner); + row_of_biggest_in_corner += k; + col_of_biggest_in_corner += k; + RealScalar biggest_in_corner = internal::abs_knowing_score<Scalar>()(m_qr(row_of_biggest_in_corner, col_of_biggest_in_corner), score); + 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) = internal::convert_index<StorageIndex>(i); + m_cols_transpositions.coeffRef(i) = internal::convert_index<StorageIndex>(i); + m_hCoeffs.coeffRef(i) = Scalar(0); + } + break; + } + + m_rows_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner); + m_cols_transpositions.coeffRef(k) = internal::convert_index<StorageIndex>(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; +} + +#ifndef EIGEN_PARSED_BY_DOXYGEN +template<typename _MatrixType> +template<typename RhsType, typename DstType> +void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const +{ + const Index l_rank = rank(); + + // FIXME introduce nonzeroPivots() and use it here. and more generally, + // make the same improvements in this dec as in FullPivLU. + if(l_rank==0) + { + dst.setZero(); + return; + } + + typename RhsType::PlainObject c(rhs); + + Matrix<typename RhsType::Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols()); + for (Index k = 0; k < l_rank; ++k) + { + Index remainingSize = rows()-k; + c.row(k).swap(c.row(m_rows_transpositions.coeff(k))); + c.bottomRightCorner(remainingSize, rhs.cols()) + .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1), + m_hCoeffs.coeff(k), &temp.coeffRef(0)); + } + + m_qr.topLeftCorner(l_rank, l_rank) + .template triangularView<Upper>() + .solveInPlace(c.topRows(l_rank)); + + for(Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i); + for(Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero(); +} + +template<typename _MatrixType> +template<bool Conjugate, typename RhsType, typename DstType> +void FullPivHouseholderQR<_MatrixType>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const +{ + const Index l_rank = rank(); + + if(l_rank == 0) + { + dst.setZero(); + return; + } + + typename RhsType::PlainObject c(m_cols_permutation.transpose()*rhs); + + m_qr.topLeftCorner(l_rank, l_rank) + .template triangularView<Upper>() + .transpose().template conjugateIf<Conjugate>() + .solveInPlace(c.topRows(l_rank)); + + dst.topRows(l_rank) = c.topRows(l_rank); + dst.bottomRows(rows()-l_rank).setZero(); + + Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols()); + const Index size = (std::min)(rows(), cols()); + for (Index k = size-1; k >= 0; --k) + { + Index remainingSize = rows()-k; + + dst.bottomRightCorner(remainingSize, dst.cols()) + .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1).template conjugateIf<!Conjugate>(), + m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0)); + + dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k))); + } +} +#endif + +namespace internal { + +template<typename DstXprType, typename MatrixType> +struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivHouseholderQR<MatrixType>::Scalar>, Dense2Dense> +{ + typedef FullPivHouseholderQR<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())); + } +}; + +/** \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 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; +}; + +// template<typename MatrixType> +// struct evaluator<FullPivHouseholderQRMatrixQReturnType<MatrixType> > +// : public evaluator<ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > > +// {}; + +} // 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); +} + +/** \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()); +} + +} // end namespace Eigen + +#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H |