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// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2012 Désiré Nuentsa-Wakam // // 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_ORDERING_H #define EIGEN_ORDERING_H // IWYU pragma: private #include "./InternalHeaderCheck.h" #include "Eigen_Colamd.h" namespace Eigen { namespace internal { /** \internal * \ingroup OrderingMethods_Module * \param[in] A the input non-symmetric matrix * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A. * FIXME: The values should not be considered here */ template void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat) { MatrixType C; C = A.transpose(); // NOTE: Could be costly for (int i = 0; i < C.rows(); i++) { for (typename MatrixType::InnerIterator it(C, i); it; ++it) it.valueRef() = typename MatrixType::Scalar(0); } symmat = C + A; } } // namespace internal /** \ingroup OrderingMethods_Module * \class AMDOrdering * * Functor computing the \em approximate \em minimum \em degree ordering * If the matrix is not structurally symmetric, an ordering of A^T+A is computed * \tparam StorageIndex The type of indices of the matrix * \sa COLAMDOrdering */ template class AMDOrdering { public: typedef PermutationMatrix PermutationType; /** Compute the permutation vector from a sparse matrix * This routine is much faster if the input matrix is column-major */ template void operator()(const MatrixType& mat, PermutationType& perm) { // Compute the symmetric pattern SparseMatrix symm; internal::ordering_helper_at_plus_a(mat, symm); // Call the AMD routine // m_mat.prune(keep_diag()); internal::minimum_degree_ordering(symm, perm); } /** Compute the permutation with a selfadjoint matrix */ template void operator()(const SparseSelfAdjointView& mat, PermutationType& perm) { SparseMatrix C; C = mat; // Call the AMD routine // m_mat.prune(keep_diag()); //Remove the diagonal elements internal::minimum_degree_ordering(C, perm); } }; /** \ingroup OrderingMethods_Module * \class NaturalOrdering * * Functor computing the natural ordering (identity) * * \note Returns an empty permutation matrix * \tparam StorageIndex The type of indices of the matrix */ template class NaturalOrdering { public: typedef PermutationMatrix PermutationType; /** Compute the permutation vector from a column-major sparse matrix */ template void operator()(const MatrixType& /*mat*/, PermutationType& perm) { perm.resize(0); } }; /** \ingroup OrderingMethods_Module * \class COLAMDOrdering * * \tparam StorageIndex The type of indices of the matrix * * Functor computing the \em column \em approximate \em minimum \em degree ordering * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()). */ template class COLAMDOrdering { public: typedef PermutationMatrix PermutationType; typedef Matrix IndexVector; /** Compute the permutation vector \a perm form the sparse matrix \a mat * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()). */ template void operator()(const MatrixType& mat, PermutationType& perm) { eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it " "to COLAMDOrdering"); StorageIndex m = StorageIndex(mat.rows()); StorageIndex n = StorageIndex(mat.cols()); StorageIndex nnz = StorageIndex(mat.nonZeros()); // Get the recommended value of Alen to be used by colamd StorageIndex Alen = internal::Colamd::recommended(nnz, m, n); // Set the default parameters double knobs[internal::Colamd::NKnobs]; StorageIndex stats[internal::Colamd::NStats]; internal::Colamd::set_defaults(knobs); IndexVector p(n + 1), A(Alen); for (StorageIndex i = 0; i <= n; i++) p(i) = mat.outerIndexPtr()[i]; for (StorageIndex i = 0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i]; // Call Colamd routine to compute the ordering StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats); EIGEN_UNUSED_VARIABLE(info); eigen_assert(info && "COLAMD failed "); perm.resize(n); for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i; } }; } // end namespace Eigen #endif