diff options
Diffstat (limited to 'eigen/Eigen/src/SparseQR/SparseQR.h')
| -rw-r--r-- | eigen/Eigen/src/SparseQR/SparseQR.h | 187 |
1 files changed, 106 insertions, 81 deletions
diff --git a/eigen/Eigen/src/SparseQR/SparseQR.h b/eigen/Eigen/src/SparseQR/SparseQR.h index a00bd5d..2d4498b 100644 --- a/eigen/Eigen/src/SparseQR/SparseQR.h +++ b/eigen/Eigen/src/SparseQR/SparseQR.h @@ -21,8 +21,12 @@ namespace internal { template <typename SparseQRType> struct traits<SparseQRMatrixQReturnType<SparseQRType> > { typedef typename SparseQRType::MatrixType ReturnType; - typedef typename ReturnType::Index Index; + typedef typename ReturnType::StorageIndex StorageIndex; typedef typename ReturnType::StorageKind StorageKind; + enum { + RowsAtCompileTime = Dynamic, + ColsAtCompileTime = Dynamic + }; }; template <typename SparseQRType> struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> > { @@ -58,24 +62,36 @@ namespace internal { * \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module * OrderingMethods \endlink module for the list of built-in and external ordering methods. * + * \implsparsesolverconcept + * * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()). * */ template<typename _MatrixType, typename _OrderingType> -class SparseQR +class SparseQR : public SparseSolverBase<SparseQR<_MatrixType,_OrderingType> > { + protected: + typedef SparseSolverBase<SparseQR<_MatrixType,_OrderingType> > Base; + using Base::m_isInitialized; public: + using Base::_solve_impl; typedef _MatrixType MatrixType; typedef _OrderingType OrderingType; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef SparseMatrix<Scalar,ColMajor,Index> QRMatrixType; - typedef Matrix<Index, Dynamic, 1> IndexVector; + typedef typename MatrixType::StorageIndex StorageIndex; + typedef SparseMatrix<Scalar,ColMajor,StorageIndex> QRMatrixType; + typedef Matrix<StorageIndex, Dynamic, 1> IndexVector; typedef Matrix<Scalar, Dynamic, 1> ScalarVector; - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; + typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; + + enum { + ColsAtCompileTime = MatrixType::ColsAtCompileTime, + MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime + }; + public: - SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) + SparseQR () : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) { } /** Construct a QR factorization of the matrix \a mat. @@ -84,7 +100,7 @@ class SparseQR * * \sa compute() */ - SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) + explicit SparseQR(const MatrixType& mat) : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false) { compute(mat); } @@ -112,6 +128,17 @@ class SparseQR inline Index cols() const { return m_pmat.cols();} /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization. + * \warning The entries of the returned matrix are not sorted. This means that using it in algorithms + * expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()), + * and coefficient-wise operations. Matrix products and triangular solves are fine though. + * + * To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix + * is required, you can copy it again: + * \code + * SparseMatrix<double> R = qr.matrixR(); // column-major, not sorted! + * SparseMatrix<double,RowMajor> Rr = qr.matrixR(); // row-major, sorted + * SparseMatrix<double> Rc = Rr; // column-major, sorted + * \endcode */ const QRMatrixType& matrixR() const { return m_R; } @@ -119,7 +146,7 @@ class SparseQR * * \sa setPivotThreshold() */ - Index rank() const + Index rank() const { eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); return m_nonzeropivots; @@ -162,7 +189,7 @@ class SparseQR /** \internal */ template<typename Rhs, typename Dest> - bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const + bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const { eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); @@ -175,10 +202,10 @@ class SparseQR b = y; // Solve with the triangular matrix R - y.resize((std::max)(cols(),Index(y.rows())),y.cols()); + y.resize((std::max<Index>)(cols(),y.rows()),y.cols()); y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank)); y.bottomRows(y.rows()-rank).setZero(); - + // Apply the column permutation if (m_perm_c.size()) dest = colsPermutation() * y.topRows(cols()); else dest = y.topRows(cols()); @@ -186,7 +213,6 @@ class SparseQR m_info = Success; return true; } - /** Sets the threshold that is used to determine linearly dependent columns during the factorization. * @@ -204,18 +230,18 @@ class SparseQR * \sa compute() */ template<typename Rhs> - inline const internal::solve_retval<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const + inline const Solve<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const { eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); - return internal::solve_retval<SparseQR, Rhs>(*this, B.derived()); + return Solve<SparseQR, Rhs>(*this, B.derived()); } template<typename Rhs> - inline const internal::sparse_solve_retval<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const + inline const Solve<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const { eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); - return internal::sparse_solve_retval<SparseQR, Rhs>(*this, B.derived()); + return Solve<SparseQR, Rhs>(*this, B.derived()); } /** \brief Reports whether previous computation was successful. @@ -232,8 +258,9 @@ class SparseQR return m_info; } - protected: - inline void sort_matrix_Q() + + /** \internal */ + inline void _sort_matrix_Q() { if(this->m_isQSorted) return; // The matrix Q is sorted during the transposition @@ -244,7 +271,6 @@ class SparseQR protected: - bool m_isInitialized; bool m_analysisIsok; bool m_factorizationIsok; mutable ComputationInfo m_info; @@ -258,14 +284,13 @@ class SparseQR PermutationType m_outputPerm_c; // The final column permutation RealScalar m_threshold; // Threshold to determine null Householder reflections bool m_useDefaultThreshold; // Use default threshold - Index m_nonzeropivots; // Number of non zero pivots found + Index m_nonzeropivots; // Number of non zero pivots found IndexVector m_etree; // Column elimination tree IndexVector m_firstRowElt; // First element in each row bool m_isQSorted; // whether Q is sorted or not bool m_isEtreeOk; // whether the elimination tree match the initial input matrix template <typename, typename > friend struct SparseQR_QProduct; - template <typename > friend struct SparseQRMatrixQReturnType; }; @@ -294,7 +319,7 @@ void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat) if (!m_perm_c.size()) { m_perm_c.resize(n); - m_perm_c.indices().setLinSpaced(n, 0,n-1); + m_perm_c.indices().setLinSpaced(n, 0,StorageIndex(n-1)); } // Compute the column elimination tree of the permuted matrix @@ -323,12 +348,11 @@ template <typename MatrixType, typename OrderingType> void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) { using std::abs; - using std::max; eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step"); - Index m = mat.rows(); - Index n = mat.cols(); - Index diagSize = (std::min)(m,n); + StorageIndex m = StorageIndex(mat.rows()); + StorageIndex n = StorageIndex(mat.cols()); + StorageIndex diagSize = (std::min)(m,n); IndexVector mark((std::max)(m,n)); mark.setConstant(-1); // Record the visited nodes IndexVector Ridx(n), Qidx(m); // Store temporarily the row indexes for the current column of R and Q Index nzcolR, nzcolQ; // Number of nonzero for the current column of R and Q @@ -353,7 +377,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) // otherwise directly use the input matrix // IndexVector originalOuterIndicesCpy; - const Index *originalOuterIndices = mat.outerIndexPtr(); + const StorageIndex *originalOuterIndices = mat.outerIndexPtr(); if(MatrixType::IsRowMajor) { originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(),n+1); @@ -375,7 +399,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) if(m_useDefaultThreshold) { RealScalar max2Norm = 0.0; - for (int j = 0; j < n; j++) max2Norm = (max)(max2Norm, m_pmat.col(j).norm()); + for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm()); if(max2Norm==RealScalar(0)) max2Norm = RealScalar(1); pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon(); @@ -384,11 +408,11 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) // Initialize the numerical permutation m_pivotperm.setIdentity(n); - Index nonzeroCol = 0; // Record the number of valid pivots + StorageIndex nonzeroCol = 0; // Record the number of valid pivots m_Q.startVec(0); // Left looking rank-revealing QR factorization: compute a column of R and Q at a time - for (Index col = 0; col < n; ++col) + for (StorageIndex col = 0; col < n; ++col) { mark.setConstant(-1); m_R.startVec(col); @@ -404,12 +428,12 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) // thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found. for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp) { - Index curIdx = nonzeroCol; - if(itp) curIdx = itp.row(); + StorageIndex curIdx = nonzeroCol; + if(itp) curIdx = StorageIndex(itp.row()); if(curIdx == nonzeroCol) found_diag = true; // Get the nonzeros indexes of the current column of R - Index st = m_firstRowElt(curIdx); // The traversal of the etree starts here + StorageIndex st = m_firstRowElt(curIdx); // The traversal of the etree starts here if (st < 0 ) { m_lastError = "Empty row found during numerical factorization"; @@ -466,7 +490,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) { for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq) { - Index iQ = itq.row(); + StorageIndex iQ = StorageIndex(itq.row()); if (mark(iQ) != col) { Qidx(nzcolQ++) = iQ; // Add this row to the pattern of Q, @@ -476,7 +500,7 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) } } // End update current column - Scalar tau = 0; + Scalar tau = RealScalar(0); RealScalar beta = 0; if(nonzeroCol < diagSize) @@ -572,40 +596,11 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) m_info = Success; } -namespace internal { - -template<typename _MatrixType, typename OrderingType, typename Rhs> -struct solve_retval<SparseQR<_MatrixType,OrderingType>, Rhs> - : solve_retval_base<SparseQR<_MatrixType,OrderingType>, Rhs> -{ - typedef SparseQR<_MatrixType,OrderingType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; -template<typename _MatrixType, typename OrderingType, typename Rhs> -struct sparse_solve_retval<SparseQR<_MatrixType, OrderingType>, Rhs> - : sparse_solve_retval_base<SparseQR<_MatrixType, OrderingType>, Rhs> -{ - typedef SparseQR<_MatrixType, OrderingType> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec, Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; -} // end namespace internal - template <typename SparseQRType, typename Derived> struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> > { typedef typename SparseQRType::QRMatrixType MatrixType; typedef typename SparseQRType::Scalar Scalar; - typedef typename SparseQRType::Index Index; // Get the references SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose) : m_qr(qr),m_other(other),m_transpose(transpose) {} @@ -661,10 +656,13 @@ struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived template<typename SparseQRType> struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> > { - typedef typename SparseQRType::Index Index; typedef typename SparseQRType::Scalar Scalar; typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {} + enum { + RowsAtCompileTime = Dynamic, + ColsAtCompileTime = Dynamic + }; + explicit SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {} template<typename Derived> SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other) { @@ -681,26 +679,13 @@ struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<Sp { return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr); } - template<typename Dest> void evalTo(MatrixBase<Dest>& dest) const - { - dest.derived() = m_qr.matrixQ() * Dest::Identity(m_qr.rows(), m_qr.rows()); - } - template<typename Dest> void evalTo(SparseMatrixBase<Dest>& dest) const - { - Dest idMat(m_qr.rows(), m_qr.rows()); - idMat.setIdentity(); - // Sort the sparse householder reflectors if needed - const_cast<SparseQRType *>(&m_qr)->sort_matrix_Q(); - dest.derived() = SparseQR_QProduct<SparseQRType, Dest>(m_qr, idMat, false); - } - const SparseQRType& m_qr; }; template<typename SparseQRType> struct SparseQRMatrixQTransposeReturnType { - SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {} + explicit SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {} template<typename Derived> SparseQR_QProduct<SparseQRType,Derived> operator*(const MatrixBase<Derived>& other) { @@ -709,6 +694,46 @@ struct SparseQRMatrixQTransposeReturnType const SparseQRType& m_qr; }; +namespace internal { + +template<typename SparseQRType> +struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> > +{ + typedef typename SparseQRType::MatrixType MatrixType; + typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind; + typedef SparseShape Shape; +}; + +template< typename DstXprType, typename SparseQRType> +struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Sparse> +{ + typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType; + typedef typename DstXprType::Scalar Scalar; + typedef typename DstXprType::StorageIndex StorageIndex; + static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/) + { + typename DstXprType::PlainObject idMat(src.m_qr.rows(), src.m_qr.rows()); + idMat.setIdentity(); + // Sort the sparse householder reflectors if needed + const_cast<SparseQRType *>(&src.m_qr)->_sort_matrix_Q(); + dst = SparseQR_QProduct<SparseQRType, DstXprType>(src.m_qr, idMat, false); + } +}; + +template< typename DstXprType, typename SparseQRType> +struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Dense> +{ + typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType; + typedef typename DstXprType::Scalar Scalar; + typedef typename DstXprType::StorageIndex StorageIndex; + static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/) + { + dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows()); + } +}; + +} // end namespace internal + } // end namespace Eigen #endif |