diff options
Diffstat (limited to 'eigen/Eigen/src/QR')
| -rw-r--r-- | eigen/Eigen/src/QR/CMakeLists.txt | 6 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/ColPivHouseholderQR.h | 280 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h (renamed from eigen/Eigen/src/QR/ColPivHouseholderQR_MKL.h) | 47 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h | 562 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/FullPivHouseholderQR.h | 195 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/HouseholderQR.h | 124 | ||||
| -rw-r--r-- | eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h (renamed from eigen/Eigen/src/QR/HouseholderQR_MKL.h) | 29 |
7 files changed, 970 insertions, 273 deletions
diff --git a/eigen/Eigen/src/QR/CMakeLists.txt b/eigen/Eigen/src/QR/CMakeLists.txt deleted file mode 100644 index 96f43d7..0000000 --- a/eigen/Eigen/src/QR/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -FILE(GLOB Eigen_QR_SRCS "*.h") - -INSTALL(FILES - ${Eigen_QR_SRCS} - DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/QR COMPONENT Devel - ) diff --git a/eigen/Eigen/src/QR/ColPivHouseholderQR.h b/eigen/Eigen/src/QR/ColPivHouseholderQR.h index 567eab7..d35395d 100644 --- a/eigen/Eigen/src/QR/ColPivHouseholderQR.h +++ b/eigen/Eigen/src/QR/ColPivHouseholderQR.h @@ -11,7 +11,16 @@ #ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H #define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H -namespace Eigen { +namespace Eigen { + +namespace internal { +template<typename _MatrixType> struct traits<ColPivHouseholderQR<_MatrixType> > + : traits<_MatrixType> +{ + enum { Flags = 0 }; +}; + +} // end namespace internal /** \ingroup QR_Module * @@ -19,19 +28,21 @@ namespace Eigen { * * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting * - * \param MatrixType the type of the matrix of which we are computing the QR decomposition + * \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 + * 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 + * 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 @@ -42,25 +53,25 @@ template<typename _MatrixType> class ColPivHouseholderQR 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 Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType; + // 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::Index PermIndexType; - + + typedef typename PermutationType::StorageIndex PermIndexType; + public: /** @@ -75,7 +86,8 @@ template<typename _MatrixType> class ColPivHouseholderQR m_colsPermutation(), m_colsTranspositions(), m_temp(), - m_colSqNorms(), + m_colNormsUpdated(), + m_colNormsDirect(), m_isInitialized(false), m_usePrescribedThreshold(false) {} @@ -91,7 +103,8 @@ template<typename _MatrixType> class ColPivHouseholderQR m_colsPermutation(PermIndexType(cols)), m_colsTranspositions(cols), m_temp(cols), - m_colSqNorms(cols), + m_colNormsUpdated(cols), + m_colNormsDirect(cols), m_isInitialized(false), m_usePrescribedThreshold(false) {} @@ -99,25 +112,48 @@ template<typename _MatrixType> class ColPivHouseholderQR * * 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() */ - ColPivHouseholderQR(const MatrixType& matrix) + 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_colSqNorms(matrix.cols()), + m_colNormsUpdated(matrix.cols()), + m_colNormsDirect(matrix.cols()), m_isInitialized(false), m_usePrescribedThreshold(false) { - compute(matrix); + 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 @@ -127,9 +163,6 @@ template<typename _MatrixType> class ColPivHouseholderQR * * \returns a solution. * - * \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 @@ -138,17 +171,17 @@ template<typename _MatrixType> class ColPivHouseholderQR * Output: \verbinclude ColPivHouseholderQR_solve.out */ template<typename Rhs> - inline const internal::solve_retval<ColPivHouseholderQR, Rhs> + inline const Solve<ColPivHouseholderQR, Rhs> solve(const MatrixBase<Rhs>& b) const { eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return internal::solve_retval<ColPivHouseholderQR, Rhs>(*this, b.derived()); + return Solve<ColPivHouseholderQR, Rhs>(*this, b.derived()); } - HouseholderSequenceType householderQ(void) const; - HouseholderSequenceType matrixQ(void) const + HouseholderSequenceType householderQ() const; + HouseholderSequenceType matrixQ() const { - return householderQ(); + return householderQ(); } /** \returns a reference to the matrix where the Householder QR decomposition is stored @@ -158,14 +191,14 @@ template<typename _MatrixType> class ColPivHouseholderQR 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. + + /** \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>() + * For rank-deficient matrices, use + * \code + * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>() * \endcode */ const MatrixType& matrixR() const @@ -173,8 +206,9 @@ template<typename _MatrixType> class ColPivHouseholderQR eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); return m_qr; } - - ColPivHouseholderQR& compute(const MatrixType& matrix); + + template<typename InputType> + ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix); /** \returns a const reference to the column permutation matrix */ const PermutationType& colsPermutation() const @@ -284,20 +318,17 @@ template<typename _MatrixType> class ColPivHouseholderQR * \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<ColPivHouseholderQR, typename MatrixType::IdentityReturnType> - inverse() const + inline const Inverse<ColPivHouseholderQR> inverse() const { eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return internal::solve_retval<ColPivHouseholderQR,typename MatrixType::IdentityReturnType> - (*this, MatrixType::Identity(m_qr.rows(), m_qr.cols())); + 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; } @@ -370,12 +401,12 @@ template<typename _MatrixType> class ColPivHouseholderQR * 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 + * \note This function always returns \c Success. It is provided for compatibility * with other factorization routines. - * \returns \c Success + * \returns \c Success */ ComputationInfo info() const { @@ -383,19 +414,29 @@ template<typename _MatrixType> class ColPivHouseholderQR return Success; } + #ifndef EIGEN_PARSED_BY_DOXYGEN + template<typename RhsType, typename DstType> + 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_colSqNorms; + RealRowVectorType m_colNormsUpdated; + RealRowVectorType m_colNormsDirect; bool m_isInitialized, m_usePrescribedThreshold; RealScalar m_prescribedThreshold, m_maxpivot; Index m_nonzero_pivots; @@ -426,51 +467,57 @@ typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDetermina * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&) */ template<typename MatrixType> -ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix) +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(); - - using std::abs; - Index rows = matrix.rows(); - Index cols = matrix.cols(); - Index size = matrix.diagonalSize(); - + // the column permutation is stored as int indices, so just to be sure: - eigen_assert(cols<=NumTraits<int>::highest()); + 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_qr = matrix; m_hCoeffs.resize(size); m_temp.resize(cols); - m_colsTranspositions.resize(matrix.cols()); + m_colsTranspositions.resize(m_qr.cols()); Index number_of_transpositions = 0; - m_colSqNorms.resize(cols); - for(Index k = 0; k < cols; ++k) - m_colSqNorms.coeffRef(k) = m_qr.col(k).squaredNorm(); + 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 = m_colSqNorms.maxCoeff() * numext::abs2(NumTraits<Scalar>::epsilon()) / RealScalar(rows); + RealScalar threshold_helper = numext::abs2<Scalar>(m_colNormsUpdated.maxCoeff() * NumTraits<Scalar>::epsilon()) / RealScalar(rows); + RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<Scalar>::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_colSqNorms which column has the biggest squared norm + // first, we look up in our table m_colNormsUpdated which column has the biggest norm Index biggest_col_index; - RealScalar biggest_col_sq_norm = m_colSqNorms.tail(cols-k).maxCoeff(&biggest_col_index); + RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols-k).maxCoeff(&biggest_col_index)); biggest_col_index += k; - // since our table m_colSqNorms accumulates imprecision at every step, we must now recompute - // the actual squared norm of the selected column. - // Note that not doing so does result in solve() sometimes returning inf/nan values - // when running the unit test with 1000 repetitions. - biggest_col_sq_norm = m_qr.col(biggest_col_index).tail(rows-k).squaredNorm(); - - // we store that back into our table: it can't hurt to correct our table. - m_colSqNorms.coeffRef(biggest_col_index) = biggest_col_sq_norm; - // 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)) @@ -480,7 +527,8 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const 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_colSqNorms.coeffRef(k), m_colSqNorms.coeffRef(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; } @@ -498,8 +546,28 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const 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 squared norms of the columns - m_colSqNorms.tail(cols-k-1) -= m_qr.row(k).tail(cols-k-1).cwiseAbs2(); + // 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) != 0) { + RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j); + temp = (RealScalar(1) + temp) * (RealScalar(1) - temp); + temp = temp < 0 ? 0 : temp; + RealScalar temp2 = temp * numext::abs2<Scalar>(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)); @@ -508,46 +576,50 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const m_det_pq = (number_of_transpositions%2) ? -1 : 1; m_isInitialized = true; - - return *this; } -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs> - : solve_retval_base<ColPivHouseholderQR<_MatrixType>, Rhs> +#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_MAKE_SOLVE_HELPERS(ColPivHouseholderQR<_MatrixType>,Rhs) + eigen_assert(rhs.rows() == rows()); + + const Index nonzero_pivots = nonzeroPivots(); - template<typename Dest> void evalTo(Dest& dst) const + if(nonzero_pivots == 0) { - eigen_assert(rhs().rows() == dec().rows()); + dst.setZero(); + return; + } - const Index cols = dec().cols(), - nonzero_pivots = dec().nonzeroPivots(); + typename RhsType::PlainObject c(rhs); - if(nonzero_pivots == 0) - { - dst.setZero(); - return; - } + // 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() + ); - typename Rhs::PlainObject c(rhs()); + m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots) + .template triangularView<Upper>() + .solveInPlace(c.topRows(nonzero_pivots)); - // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T - c.applyOnTheLeft(householderSequence(dec().matrixQR(), dec().hCoeffs()) - .setLength(dec().nonzeroPivots()) - .transpose() - ); + 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 - dec().matrixR() - .topLeftCorner(nonzero_pivots, nonzero_pivots) - .template triangularView<Upper>() - .solveInPlace(c.topRows(nonzero_pivots)); +namespace internal { - for(Index i = 0; i < nonzero_pivots; ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i); - for(Index i = nonzero_pivots; i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero(); +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())); } }; diff --git a/eigen/Eigen/src/QR/ColPivHouseholderQR_MKL.h b/eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h index 7b6ba0a..4e9651f 100644 --- a/eigen/Eigen/src/QR/ColPivHouseholderQR_MKL.h +++ b/eigen/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h @@ -25,26 +25,24 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL + * Content : Eigen bindings to LAPACKe * Householder QR decomposition of a matrix with column pivoting based on * LAPACKE_?geqp3 function. ******************************************************************************** */ -#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H -#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" +#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H +#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H namespace Eigen { -/** \internal Specialization for the data types supported by MKL */ +/** \internal Specialization for the data types supported by LAPACKe */ -#define EIGEN_MKL_QR_COLPIV(EIGTYPE, MKLTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \ -template<> inline \ +#define EIGEN_LAPACKE_QR_COLPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW) \ +template<> template<typename InputType> inline \ ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \ ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \ - const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix) \ + const EigenBase<InputType>& matrix) \ \ { \ using std::abs; \ @@ -52,9 +50,9 @@ ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynami typedef MatrixType::RealScalar RealScalar; \ Index rows = matrix.rows();\ Index cols = matrix.cols();\ - Index size = matrix.diagonalSize();\ \ m_qr = matrix;\ + Index size = m_qr.diagonalSize();\ m_hCoeffs.resize(size);\ \ m_colsTranspositions.resize(cols);\ @@ -65,34 +63,35 @@ ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynami m_colsPermutation.resize(cols); \ m_colsPermutation.indices().setZero(); \ \ - lapack_int lda = m_qr.outerStride(), i; \ - lapack_int matrix_order = MKLCOLROW; \ - LAPACKE_##MKLPREFIX##geqp3( matrix_order, rows, cols, (MKLTYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (MKLTYPE*)m_hCoeffs.data()); \ + lapack_int lda = internal::convert_index<lapack_int,Index>(m_qr.outerStride()); \ + lapack_int matrix_order = LAPACKE_COLROW; \ + LAPACKE_##LAPACKE_PREFIX##geqp3( matrix_order, internal::convert_index<lapack_int,Index>(rows), internal::convert_index<lapack_int,Index>(cols), \ + (LAPACKE_TYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (LAPACKE_TYPE*)m_hCoeffs.data()); \ m_isInitialized = true; \ m_maxpivot=m_qr.diagonal().cwiseAbs().maxCoeff(); \ m_hCoeffs.adjointInPlace(); \ RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); \ lapack_int *perm = m_colsPermutation.indices().data(); \ - for(i=0;i<size;i++) { \ + for(Index i=0;i<size;i++) { \ m_nonzero_pivots += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);\ } \ - for(i=0;i<cols;i++) perm[i]--;\ + for(Index i=0;i<cols;i++) perm[i]--;\ \ /*m_det_pq = (number_of_transpositions%2) ? -1 : 1; // TODO: It's not needed now; fix upon availability in Eigen */ \ \ return *this; \ } -EIGEN_MKL_QR_COLPIV(double, double, d, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(float, float, s, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8, c, ColMajor, LAPACK_COL_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(double, double, d, ColMajor, LAPACK_COL_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(float, float, s, ColMajor, LAPACK_COL_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, ColMajor, LAPACK_COL_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float, c, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(double, double, d, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(float, float, s, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8, c, RowMajor, LAPACK_ROW_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(double, double, d, RowMajor, LAPACK_ROW_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(float, float, s, RowMajor, LAPACK_ROW_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(dcomplex, lapack_complex_double, z, RowMajor, LAPACK_ROW_MAJOR) +EIGEN_LAPACKE_QR_COLPIV(scomplex, lapack_complex_float, c, RowMajor, LAPACK_ROW_MAJOR) } // end namespace Eigen -#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H +#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H diff --git a/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h new file mode 100644 index 0000000..13b61fc --- /dev/null +++ b/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h @@ -0,0 +1,562 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.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_COMPLETEORTHOGONALDECOMPOSITION_H +#define EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H + +namespace Eigen { + +namespace internal { +template <typename _MatrixType> +struct traits<CompleteOrthogonalDecomposition<_MatrixType> > + : traits<_MatrixType> { + enum { Flags = 0 }; +}; + +} // end namespace internal + +/** \ingroup QR_Module + * + * \class CompleteOrthogonalDecomposition + * + * \brief Complete orthogonal decomposition (COD) of a matrix. + * + * \param MatrixType the type of the matrix of which we are computing the COD. + * + * This class performs a rank-revealing complete orthogonal decomposition of a + * matrix \b A into matrices \b P, \b Q, \b T, and \b Z such that + * \f[ + * \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, + * \begin{bmatrix} \mathbf{T} & \mathbf{0} \\ + * \mathbf{0} & \mathbf{0} \end{bmatrix} \, \mathbf{Z} + * \f] + * by using Householder transformations. Here, \b P is a permutation matrix, + * \b Q and \b Z are unitary matrices and \b T an upper triangular matrix of + * size rank-by-rank. \b A may be rank deficient. + * + * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism. + * + * \sa MatrixBase::completeOrthogonalDecomposition() + */ +template <typename _MatrixType> +class CompleteOrthogonalDecomposition { + 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; + 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::Index PermIndexType; + + public: + /** + * \brief Default Constructor. + * + * The default constructor is useful in cases in which the user intends to + * perform decompositions via + * \c CompleteOrthogonalDecomposition::compute(const* MatrixType&). + */ + CompleteOrthogonalDecomposition() : m_cpqr(), m_zCoeffs(), m_temp() {} + + /** \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 CompleteOrthogonalDecomposition() + */ + CompleteOrthogonalDecomposition(Index rows, Index cols) + : m_cpqr(rows, cols), m_zCoeffs((std::min)(rows, cols)), m_temp(cols) {} + + /** \brief Constructs a complete orthogonal decomposition from a given + * matrix. + * + * This constructor computes the complete orthogonal decomposition of the + * matrix \a matrix by calling the method compute(). The default + * threshold for rank determination will be used. It is a short cut for: + * + * \code + * CompleteOrthogonalDecomposition<MatrixType> cod(matrix.rows(), + * matrix.cols()); + * cod.setThreshold(Default); + * cod.compute(matrix); + * \endcode + * + * \sa compute() + */ + template <typename InputType> + explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix) + : m_cpqr(matrix.rows(), matrix.cols()), + m_zCoeffs((std::min)(matrix.rows(), matrix.cols())), + m_temp(matrix.cols()) + { + compute(matrix.derived()); + } + + /** \brief Constructs a complete orthogonal decomposition from a given matrix + * + * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref. + * + * \sa CompleteOrthogonalDecomposition(const EigenBase&) + */ + template<typename InputType> + explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix) + : m_cpqr(matrix.derived()), + m_zCoeffs((std::min)(matrix.rows(), matrix.cols())), + m_temp(matrix.cols()) + { + computeInPlace(); + } + + + /** This method computes the minimum-norm solution X to a least squares + * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of + * which \c *this is the complete orthogonal decomposition. + * + * \param b the right-hand sides of the problem to solve. + * + * \returns a solution. + * + */ + template <typename Rhs> + inline const Solve<CompleteOrthogonalDecomposition, Rhs> solve( + const MatrixBase<Rhs>& b) const { + eigen_assert(m_cpqr.m_isInitialized && + "CompleteOrthogonalDecomposition is not initialized."); + return Solve<CompleteOrthogonalDecomposition, Rhs>(*this, b.derived()); + } + + HouseholderSequenceType householderQ(void) const; + HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); } + + /** \returns the matrix \b Z. + */ + MatrixType matrixZ() const { + MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols()); + applyZAdjointOnTheLeftInPlace(Z); + return Z.adjoint(); + } + + /** \returns a reference to the matrix where the complete orthogonal + * decomposition is stored + */ + const MatrixType& matrixQTZ() const { return m_cpqr.matrixQR(); } + + /** \returns a reference to the matrix where the complete orthogonal + * decomposition is stored. + * \warning The strict lower part and \code cols() - rank() \endcode right + * columns of this matrix contains internal values. + * Only the upper triangular part should be referenced. To get it, use + * \code matrixT().template triangularView<Upper>() \endcode + * For rank-deficient matrices, use + * \code + * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>() + * \endcode + */ + const MatrixType& matrixT() const { return m_cpqr.matrixQR(); } + + template <typename InputType> + CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) { + // Compute the column pivoted QR factorization A P = Q R. + m_cpqr.compute(matrix); + computeInPlace(); + return *this; + } + + /** \returns a const reference to the column permutation matrix */ + const PermutationType& colsPermutation() const { + return m_cpqr.colsPermutation(); + } + + /** \returns the absolute value of the determinant of the matrix of which + * *this is the complete orthogonal decomposition. It has only linear + * complexity (that is, O(n) where n is the dimension of the square matrix) + * as the complete orthogonal 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 complete orthogonal decomposition. It has + * only linear complexity (that is, O(n) where n is the dimension of the + * square matrix) as the complete orthogonal 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 complete orthogonal + * 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 { return m_cpqr.rank(); } + + /** \returns the dimension of the kernel of the matrix of which *this is the + * complete orthogonal 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 { return m_cpqr.dimensionOfKernel(); } + + /** \returns true if the matrix of which *this is the 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 { return m_cpqr.isInjective(); } + + /** \returns true if the matrix of which *this is the 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 { return m_cpqr.isSurjective(); } + + /** \returns true if the matrix of which *this is the complete orthogonal + * 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 { return m_cpqr.isInvertible(); } + + /** \returns the pseudo-inverse of the matrix of which *this is the complete + * orthogonal decomposition. + * \warning: Do not compute \c this->pseudoInverse()*rhs to solve a linear systems. + * It is more efficient and numerically stable to call \c this->solve(rhs). + */ + inline const Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const + { + return Inverse<CompleteOrthogonalDecomposition>(*this); + } + + inline Index rows() const { return m_cpqr.rows(); } + inline Index cols() const { return m_cpqr.cols(); } + + /** \returns a const reference to the vector of Householder coefficients used + * to represent the factor \c Q. + * + * For advanced uses only. + */ + inline const HCoeffsType& hCoeffs() const { return m_cpqr.hCoeffs(); } + + /** \returns a const reference to the vector of Householder coefficients + * used to represent the factor \c Z. + * + * For advanced uses only. + */ + const HCoeffsType& zCoeffs() const { return m_zCoeffs; } + + /** 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. + * Most be called before calling compute(). + * + * 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) + */ + CompleteOrthogonalDecomposition& setThreshold(const RealScalar& threshold) { + m_cpqr.setThreshold(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&). + */ + CompleteOrthogonalDecomposition& setThreshold(Default_t) { + m_cpqr.setThreshold(Default); + 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 { return m_cpqr.threshold(); } + + /** \returns the number of nonzero pivots in the complete orthogonal + * 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 { return m_cpqr.nonzeroPivots(); } + + /** \returns the absolute value of the biggest pivot, i.e. the biggest + * diagonal coefficient of R. + */ + inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); } + + /** \brief Reports whether the complete orthogonal decomposition 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_cpqr.m_isInitialized && "Decomposition is not initialized."); + return Success; + } + +#ifndef EIGEN_PARSED_BY_DOXYGEN + template <typename RhsType, typename DstType> + void _solve_impl(const RhsType& rhs, DstType& dst) const; +#endif + + protected: + static void check_template_parameters() { + EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar); + } + + void computeInPlace(); + + /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$. + */ + template <typename Rhs> + void applyZAdjointOnTheLeftInPlace(Rhs& rhs) const; + + ColPivHouseholderQR<MatrixType> m_cpqr; + HCoeffsType m_zCoeffs; + RowVectorType m_temp; +}; + +template <typename MatrixType> +typename MatrixType::RealScalar +CompleteOrthogonalDecomposition<MatrixType>::absDeterminant() const { + return m_cpqr.absDeterminant(); +} + +template <typename MatrixType> +typename MatrixType::RealScalar +CompleteOrthogonalDecomposition<MatrixType>::logAbsDeterminant() const { + return m_cpqr.logAbsDeterminant(); +} + +/** Performs the complete orthogonal decomposition 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 CompleteOrthogonalDecomposition, + * CompleteOrthogonalDecomposition(const MatrixType&) + */ +template <typename MatrixType> +void CompleteOrthogonalDecomposition<MatrixType>::computeInPlace() +{ + check_template_parameters(); + + // the column permutation is stored as int indices, so just to be sure: + eigen_assert(m_cpqr.cols() <= NumTraits<int>::highest()); + + const Index rank = m_cpqr.rank(); + const Index cols = m_cpqr.cols(); + const Index rows = m_cpqr.rows(); + m_zCoeffs.resize((std::min)(rows, cols)); + m_temp.resize(cols); + + if (rank < cols) { + // We have reduced the (permuted) matrix to the form + // [R11 R12] + // [ 0 R22] + // where R11 is r-by-r (r = rank) upper triangular, R12 is + // r-by-(n-r), and R22 is empty or the norm of R22 is negligible. + // We now compute the complete orthogonal decomposition by applying + // Householder transformations from the right to the upper trapezoidal + // matrix X = [R11 R12] to zero out R12 and obtain the factorization + // [R11 R12] = [T11 0] * Z, where T11 is r-by-r upper triangular and + // Z = Z(0) * Z(1) ... Z(r-1) is an n-by-n orthogonal matrix. + // We store the data representing Z in R12 and m_zCoeffs. + for (Index k = rank - 1; k >= 0; --k) { + if (k != rank - 1) { + // Given the API for Householder reflectors, it is more convenient if + // we swap the leading parts of columns k and r-1 (zero-based) to form + // the matrix X_k = [X(0:k, k), X(0:k, r:n)] + m_cpqr.m_qr.col(k).head(k + 1).swap( + m_cpqr.m_qr.col(rank - 1).head(k + 1)); + } + // Construct Householder reflector Z(k) to zero out the last row of X_k, + // i.e. choose Z(k) such that + // [X(k, k), X(k, r:n)] * Z(k) = [beta, 0, .., 0]. + RealScalar beta; + m_cpqr.m_qr.row(k) + .tail(cols - rank + 1) + .makeHouseholderInPlace(m_zCoeffs(k), beta); + m_cpqr.m_qr(k, rank - 1) = beta; + if (k > 0) { + // Apply Z(k) to the first k rows of X_k + m_cpqr.m_qr.topRightCorner(k, cols - rank + 1) + .applyHouseholderOnTheRight( + m_cpqr.m_qr.row(k).tail(cols - rank).transpose(), m_zCoeffs(k), + &m_temp(0)); + } + if (k != rank - 1) { + // Swap X(0:k,k) back to its proper location. + m_cpqr.m_qr.col(k).head(k + 1).swap( + m_cpqr.m_qr.col(rank - 1).head(k + 1)); + } + } + } +} + +template <typename MatrixType> +template <typename Rhs> +void CompleteOrthogonalDecomposition<MatrixType>::applyZAdjointOnTheLeftInPlace( + Rhs& rhs) const { + const Index cols = this->cols(); + const Index nrhs = rhs.cols(); + const Index rank = this->rank(); + Matrix<typename MatrixType::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs)); + for (Index k = 0; k < rank; ++k) { + if (k != rank - 1) { + rhs.row(k).swap(rhs.row(rank - 1)); + } + rhs.middleRows(rank - 1, cols - rank + 1) + .applyHouseholderOnTheLeft( + matrixQTZ().row(k).tail(cols - rank).adjoint(), zCoeffs()(k), + &temp(0)); + if (k != rank - 1) { + rhs.row(k).swap(rhs.row(rank - 1)); + } + } +} + +#ifndef EIGEN_PARSED_BY_DOXYGEN +template <typename _MatrixType> +template <typename RhsType, typename DstType> +void CompleteOrthogonalDecomposition<_MatrixType>::_solve_impl( + const RhsType& rhs, DstType& dst) const { + eigen_assert(rhs.rows() == this->rows()); + + const Index rank = this->rank(); + if (rank == 0) { + dst.setZero(); + return; + } + + // Compute c = Q^* * rhs + // Note that the matrix Q = H_0^* H_1^*... so its inverse is + // Q^* = (H_0 H_1 ...)^T + typename RhsType::PlainObject c(rhs); + c.applyOnTheLeft( + householderSequence(matrixQTZ(), hCoeffs()).setLength(rank).transpose()); + + // Solve T z = c(1:rank, :) + dst.topRows(rank) = matrixT() + .topLeftCorner(rank, rank) + .template triangularView<Upper>() + .solve(c.topRows(rank)); + + const Index cols = this->cols(); + if (rank < cols) { + // Compute y = Z^* * [ z ] + // [ 0 ] + dst.bottomRows(cols - rank).setZero(); + applyZAdjointOnTheLeftInPlace(dst); + } + + // Undo permutation to get x = P^{-1} * y. + dst = colsPermutation() * dst; +} +#endif + +namespace internal { + +template<typename DstXprType, typename MatrixType> +struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType> >, internal::assign_op<typename DstXprType::Scalar,typename CompleteOrthogonalDecomposition<MatrixType>::Scalar>, Dense2Dense> +{ + typedef CompleteOrthogonalDecomposition<MatrixType> CodType; + typedef Inverse<CodType> SrcXprType; + static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename CodType::Scalar> &) + { + dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.rows())); + } +}; + +} // end namespace internal + +/** \returns the matrix Q as a sequence of householder transformations */ +template <typename MatrixType> +typename CompleteOrthogonalDecomposition<MatrixType>::HouseholderSequenceType +CompleteOrthogonalDecomposition<MatrixType>::householderQ() const { + return m_cpqr.householderQ(); +} + +/** \return the complete orthogonal decomposition of \c *this. + * + * \sa class CompleteOrthogonalDecomposition + */ +template <typename Derived> +const CompleteOrthogonalDecomposition<typename MatrixBase<Derived>::PlainObject> +MatrixBase<Derived>::completeOrthogonalDecomposition() const { + return CompleteOrthogonalDecomposition<PlainObject>(eval()); +} + +} // end namespace Eigen + +#endif // EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H diff --git a/eigen/Eigen/src/QR/FullPivHouseholderQR.h b/eigen/Eigen/src/QR/FullPivHouseholderQR.h index 0b39966..c31e47c 100644 --- a/eigen/Eigen/src/QR/FullPivHouseholderQR.h +++ b/eigen/Eigen/src/QR/FullPivHouseholderQR.h @@ -15,6 +15,12 @@ namespace Eigen { namespace internal { +template<typename _MatrixType> struct traits<FullPivHouseholderQR<_MatrixType> > + : traits<_MatrixType> +{ + enum { Flags = 0 }; +}; + template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType; template<typename MatrixType> @@ -23,7 +29,7 @@ struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> > typedef typename MatrixType::PlainObject ReturnType; }; -} +} // end namespace internal /** \ingroup QR_Module * @@ -31,19 +37,21 @@ struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> > * * \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 + * \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 + * 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{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R} + * \mathbf{P} \, \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. + * 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 @@ -54,21 +62,22 @@ template<typename _MatrixType> class FullPivHouseholderQR 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; + // FIXME should be int + typedef typename MatrixType::StorageIndex StorageIndex; typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType; typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; - typedef Matrix<Index, 1, + 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. * @@ -113,7 +122,8 @@ template<typename _MatrixType> class FullPivHouseholderQR * * \sa compute() */ - FullPivHouseholderQR(const MatrixType& matrix) + 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())), @@ -123,7 +133,27 @@ template<typename _MatrixType> class FullPivHouseholderQR m_isInitialized(false), m_usePrescribedThreshold(false) { - compute(matrix); + 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(); } /** This method finds a solution x to the equation Ax=b, where A is the matrix of which @@ -134,9 +164,6 @@ template<typename _MatrixType> class FullPivHouseholderQR * \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 @@ -145,11 +172,11 @@ template<typename _MatrixType> class FullPivHouseholderQR * Output: \verbinclude FullPivHouseholderQR_solve.out */ template<typename Rhs> - inline const internal::solve_retval<FullPivHouseholderQR, Rhs> + inline const Solve<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()); + return Solve<FullPivHouseholderQR, Rhs>(*this, b.derived()); } /** \returns Expression object representing the matrix Q @@ -164,7 +191,8 @@ template<typename _MatrixType> class FullPivHouseholderQR return m_qr; } - FullPivHouseholderQR& compute(const MatrixType& matrix); + template<typename InputType> + FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix); /** \returns a const reference to the column permutation matrix */ const PermutationType& colsPermutation() const @@ -280,13 +308,11 @@ template<typename _MatrixType> class FullPivHouseholderQR * * \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 + */ + inline const Inverse<FullPivHouseholderQR> 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())); + return Inverse<FullPivHouseholderQR>(*this); } inline Index rows() const { return m_qr.rows(); } @@ -367,13 +393,20 @@ template<typename _MatrixType> class FullPivHouseholderQR */ 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; + #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; @@ -411,16 +444,25 @@ typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDetermin * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&) */ template<typename MatrixType> -FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix) +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 = matrix.rows(); - Index cols = matrix.cols(); + Index rows = m_qr.rows(); + Index cols = m_qr.cols(); Index size = (std::min)(rows,cols); - m_qr = matrix; + m_hCoeffs.resize(size); m_temp.resize(cols); @@ -439,13 +481,15 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons for (Index k = 0; k < size; ++k) { Index row_of_biggest_in_corner, col_of_biggest_in_corner; - RealScalar biggest_in_corner; + typedef internal::scalar_score_coeff_op<Scalar> Scoring; + typedef typename Scoring::result_type Score; - biggest_in_corner = m_qr.bottomRightCorner(rows-k, cols-k) - .cwiseAbs() - .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner); + 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 @@ -489,50 +533,55 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons 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> +#ifndef EIGEN_PARSED_BY_DOXYGEN +template<typename _MatrixType> +template<typename RhsType, typename DstType> +void FullPivHouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const { - EIGEN_MAKE_SOLVE_HELPERS(FullPivHouseholderQR<_MatrixType>,Rhs) + eigen_assert(rhs.rows() == rows()); + const Index l_rank = rank(); - template<typename Dest> void evalTo(Dest& dst) const + // FIXME introduce nonzeroPivots() and use it here. and more generally, + // make the same improvements in this dec as in FullPivLU. + if(l_rank==0) { - const Index rows = dec().rows(), cols = dec().cols(); - eigen_assert(rhs().rows() == rows); + dst.setZero(); + return; + } - // 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 RhsType::PlainObject c(rhs); - typename Rhs::PlainObject c(rhs()); + Matrix<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)); + } - 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)); - } + m_qr.topLeftCorner(l_rank, l_rank) + .template triangularView<Upper>() + .solveInPlace(c.topRows(l_rank)); - dec().matrixQR() - .topLeftCorner(dec().rank(), dec().rank()) - .template triangularView<Upper>() - .solveInPlace(c.topRows(dec().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(); +} +#endif - 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(); +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())); } }; @@ -546,7 +595,6 @@ 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, @@ -558,7 +606,7 @@ public: : m_qr(qr), m_hCoeffs(hCoeffs), m_rowsTranspositions(rowsTranspositions) - {} + {} template <typename ResultType> void evalTo(ResultType& result) const @@ -588,8 +636,8 @@ public: } } - Index rows() const { return m_qr.rows(); } - Index cols() const { return m_qr.rows(); } + Index rows() const { return m_qr.rows(); } + Index cols() const { return m_qr.rows(); } protected: typename MatrixType::Nested m_qr; @@ -597,6 +645,11 @@ protected: typename IntDiagSizeVectorType::Nested m_rowsTranspositions; }; +// template<typename MatrixType> +// struct evaluator<FullPivHouseholderQRMatrixQReturnType<MatrixType> > +// : public evaluator<ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > > +// {}; + } // end namespace internal template<typename MatrixType> diff --git a/eigen/Eigen/src/QR/HouseholderQR.h b/eigen/Eigen/src/QR/HouseholderQR.h index 343a664..762b21c 100644 --- a/eigen/Eigen/src/QR/HouseholderQR.h +++ b/eigen/Eigen/src/QR/HouseholderQR.h @@ -21,7 +21,7 @@ namespace Eigen { * * \brief Householder QR decomposition of a matrix * - * \param MatrixType the type of the matrix of which we are computing the QR decomposition + * \tparam _MatrixType the type of the matrix of which we are computing the QR decomposition * * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R * such that @@ -37,6 +37,8 @@ namespace Eigen { * This Householder QR decomposition is faster, but less numerically stable and less feature-full than * FullPivHouseholderQR or ColPivHouseholderQR. * + * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism. + * * \sa MatrixBase::householderQr() */ template<typename _MatrixType> class HouseholderQR @@ -47,13 +49,13 @@ template<typename _MatrixType> class HouseholderQR 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; + // FIXME should be int + typedef typename MatrixType::StorageIndex StorageIndex; typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType; typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; typedef typename internal::plain_row_type<MatrixType>::type RowVectorType; @@ -91,13 +93,32 @@ template<typename _MatrixType> class HouseholderQR * * \sa compute() */ - HouseholderQR(const MatrixType& matrix) + template<typename InputType> + explicit HouseholderQR(const EigenBase<InputType>& matrix) : m_qr(matrix.rows(), matrix.cols()), m_hCoeffs((std::min)(matrix.rows(),matrix.cols())), m_temp(matrix.cols()), m_isInitialized(false) { - compute(matrix); + 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 HouseholderQR(const EigenBase&) + */ + template<typename InputType> + explicit HouseholderQR(EigenBase<InputType>& matrix) + : m_qr(matrix.derived()), + m_hCoeffs((std::min)(matrix.rows(),matrix.cols())), + m_temp(matrix.cols()), + m_isInitialized(false) + { + computeInPlace(); } /** This method finds a solution x to the equation Ax=b, where A is the matrix of which @@ -107,9 +128,6 @@ template<typename _MatrixType> class HouseholderQR * * \returns a solution. * - * \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 @@ -118,11 +136,11 @@ template<typename _MatrixType> class HouseholderQR * Output: \verbinclude HouseholderQR_solve.out */ template<typename Rhs> - inline const internal::solve_retval<HouseholderQR, Rhs> + inline const Solve<HouseholderQR, Rhs> solve(const MatrixBase<Rhs>& b) const { eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - return internal::solve_retval<HouseholderQR, Rhs>(*this, b.derived()); + return Solve<HouseholderQR, Rhs>(*this, b.derived()); } /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations. @@ -148,7 +166,12 @@ template<typename _MatrixType> class HouseholderQR return m_qr; } - HouseholderQR& compute(const MatrixType& matrix); + template<typename InputType> + HouseholderQR& compute(const EigenBase<InputType>& matrix) { + m_qr = matrix.derived(); + computeInPlace(); + return *this; + } /** \returns the absolute value of the determinant of the matrix of which * *this is the QR decomposition. It has only linear complexity @@ -181,20 +204,27 @@ template<typename _MatrixType> class HouseholderQR 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; } + #ifndef EIGEN_PARSED_BY_DOXYGEN + template<typename RhsType, typename DstType> + void _solve_impl(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; RowVectorType m_temp; @@ -224,7 +254,6 @@ namespace internal { template<typename MatrixQR, typename HCoeffs> void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0) { - typedef typename MatrixQR::Index Index; typedef typename MatrixQR::Scalar Scalar; typedef typename MatrixQR::RealScalar RealScalar; Index rows = mat.rows(); @@ -263,11 +292,9 @@ template<typename MatrixQR, typename HCoeffs, struct householder_qr_inplace_blocked { // This is specialized for MKL-supported Scalar types in HouseholderQR_MKL.h - static void run(MatrixQR& mat, HCoeffs& hCoeffs, - typename MatrixQR::Index maxBlockSize=32, + static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index maxBlockSize=32, typename MatrixQR::Scalar* tempData = 0) { - typedef typename MatrixQR::Index Index; typedef typename MatrixQR::Scalar Scalar; typedef Block<MatrixQR,Dynamic,Dynamic> BlockType; @@ -289,8 +316,8 @@ struct householder_qr_inplace_blocked for (k = 0; k < size; k += blockSize) { Index bs = (std::min)(size-k,blockSize); // actual size of the block - Index tcols = cols - k - bs; // trailing columns - Index brows = rows-k; // rows of the block + Index tcols = cols - k - bs; // trailing columns + Index brows = rows-k; // rows of the block // partition the matrix: // A00 | A01 | A02 @@ -308,43 +335,38 @@ struct householder_qr_inplace_blocked if(tcols) { BlockType A21_22 = mat.block(k,k+bs,brows,tcols); - apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment.adjoint()); + apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment, false); // false == backward } } } }; -template<typename _MatrixType, typename Rhs> -struct solve_retval<HouseholderQR<_MatrixType>, Rhs> - : solve_retval_base<HouseholderQR<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(HouseholderQR<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - const Index rows = dec().rows(), cols = dec().cols(); - const Index rank = (std::min)(rows, cols); - eigen_assert(rhs().rows() == rows); +} // end namespace internal - typename Rhs::PlainObject c(rhs()); +#ifndef EIGEN_PARSED_BY_DOXYGEN +template<typename _MatrixType> +template<typename RhsType, typename DstType> +void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) const +{ + const Index rank = (std::min)(rows(), cols()); + eigen_assert(rhs.rows() == rows()); - // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T - c.applyOnTheLeft(householderSequence( - dec().matrixQR().leftCols(rank), - dec().hCoeffs().head(rank)).transpose() - ); + typename RhsType::PlainObject c(rhs); - dec().matrixQR() - .topLeftCorner(rank, rank) - .template triangularView<Upper>() - .solveInPlace(c.topRows(rank)); + // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T + c.applyOnTheLeft(householderSequence( + m_qr.leftCols(rank), + m_hCoeffs.head(rank)).transpose() + ); - dst.topRows(rank) = c.topRows(rank); - dst.bottomRows(cols-rank).setZero(); - } -}; + m_qr.topLeftCorner(rank, rank) + .template triangularView<Upper>() + .solveInPlace(c.topRows(rank)); -} // end namespace internal + dst.topRows(rank) = c.topRows(rank); + dst.bottomRows(cols()-rank).setZero(); +} +#endif /** 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 @@ -353,15 +375,14 @@ struct solve_retval<HouseholderQR<_MatrixType>, Rhs> * \sa class HouseholderQR, HouseholderQR(const MatrixType&) */ template<typename MatrixType> -HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix) +void HouseholderQR<MatrixType>::computeInPlace() { check_template_parameters(); - Index rows = matrix.rows(); - Index cols = matrix.cols(); + Index rows = m_qr.rows(); + Index cols = m_qr.cols(); Index size = (std::min)(rows,cols); - m_qr = matrix; m_hCoeffs.resize(size); m_temp.resize(cols); @@ -369,7 +390,6 @@ HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data()); m_isInitialized = true; - return *this; } /** \return the Householder QR decomposition of \c *this. diff --git a/eigen/Eigen/src/QR/HouseholderQR_MKL.h b/eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h index b80f1b4..1dc7d53 100644 --- a/eigen/Eigen/src/QR/HouseholderQR_MKL.h +++ b/eigen/Eigen/src/QR/HouseholderQR_LAPACKE.h @@ -25,47 +25,44 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL + * Content : Eigen bindings to LAPACKe * Householder QR decomposition of a matrix w/o pivoting based on * LAPACKE_?geqrf function. ******************************************************************************** */ -#ifndef EIGEN_QR_MKL_H -#define EIGEN_QR_MKL_H - -#include "../Core/util/MKL_support.h" +#ifndef EIGEN_QR_LAPACKE_H +#define EIGEN_QR_LAPACKE_H namespace Eigen { - namespace internal { +namespace internal { - /** \internal Specialization for the data types supported by MKL */ +/** \internal Specialization for the data types supported by LAPACKe */ -#define EIGEN_MKL_QR_NOPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \ +#define EIGEN_LAPACKE_QR_NOPIV(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX) \ template<typename MatrixQR, typename HCoeffs> \ struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> \ { \ - static void run(MatrixQR& mat, HCoeffs& hCoeffs, \ - typename MatrixQR::Index = 32, \ + static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index = 32, \ typename MatrixQR::Scalar* = 0) \ { \ lapack_int m = (lapack_int) mat.rows(); \ lapack_int n = (lapack_int) mat.cols(); \ lapack_int lda = (lapack_int) mat.outerStride(); \ lapack_int matrix_order = (MatrixQR::IsRowMajor) ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ - LAPACKE_##MKLPREFIX##geqrf( matrix_order, m, n, (MKLTYPE*)mat.data(), lda, (MKLTYPE*)hCoeffs.data()); \ + LAPACKE_##LAPACKE_PREFIX##geqrf( matrix_order, m, n, (LAPACKE_TYPE*)mat.data(), lda, (LAPACKE_TYPE*)hCoeffs.data()); \ hCoeffs.adjointInPlace(); \ } \ }; -EIGEN_MKL_QR_NOPIV(double, double, d) -EIGEN_MKL_QR_NOPIV(float, float, s) -EIGEN_MKL_QR_NOPIV(dcomplex, MKL_Complex16, z) -EIGEN_MKL_QR_NOPIV(scomplex, MKL_Complex8, c) +EIGEN_LAPACKE_QR_NOPIV(double, double, d) +EIGEN_LAPACKE_QR_NOPIV(float, float, s) +EIGEN_LAPACKE_QR_NOPIV(dcomplex, lapack_complex_double, z) +EIGEN_LAPACKE_QR_NOPIV(scomplex, lapack_complex_float, c) } // end namespace internal } // end namespace Eigen -#endif // EIGEN_QR_MKL_H +#endif // EIGEN_QR_LAPACKE_H |