From 64242b8bf367752df4d28170cbbb6b86037ff988 Mon Sep 17 00:00:00 2001 From: Gael Guennebaud Date: Thu, 8 Oct 2015 10:50:39 +0200 Subject: Doc: add link to doc of sparse solver concept --- Eigen/src/SPQRSupport/SuiteSparseQRSupport.h | 44 +++++++++++++++------------- 1 file changed, 23 insertions(+), 21 deletions(-) (limited to 'Eigen/src/SPQRSupport') diff --git a/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h index 4ad22f8b4..ac2de9b04 100644 --- a/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h +++ b/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h @@ -33,27 +33,29 @@ namespace Eigen { } // End namespace internal /** - * \ingroup SPQRSupport_Module - * \class SPQR - * \brief Sparse QR factorization based on SuiteSparseQR library - * - * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition - * of sparse matrices. The result is then used to solve linear leasts_square systems. - * Clearly, a QR factorization is returned such that A*P = Q*R where : - * - * P is the column permutation. Use colsPermutation() to get it. - * - * Q is the orthogonal matrix represented as Householder reflectors. - * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. - * You can then apply it to a vector. - * - * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. - * NOTE : The Index type of R is always UF_long. You can get it with SPQR::Index - * - * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> - * NOTE - * - */ + * \ingroup SPQRSupport_Module + * \class SPQR + * \brief Sparse QR factorization based on SuiteSparseQR library + * + * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition + * of sparse matrices. The result is then used to solve linear leasts_square systems. + * Clearly, a QR factorization is returned such that A*P = Q*R where : + * + * P is the column permutation. Use colsPermutation() to get it. + * + * Q is the orthogonal matrix represented as Householder reflectors. + * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. + * You can then apply it to a vector. + * + * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. + * NOTE : The Index type of R is always UF_long. You can get it with SPQR::Index + * + * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> + * + * \implsparsesolverconcept + * + * + */ template class SPQR : public SparseSolverBase > { -- cgit v1.2.3