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| | #ifndef EIGEN_ORDERING_H |
| | #define EIGEN_ORDERING_H |
| |
|
| | namespace Eigen { |
| | |
| | #include "Eigen_Colamd.h" |
| |
|
| | namespace internal { |
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| | |
| | template<typename MatrixType> |
| | void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat) |
| | { |
| | MatrixType C; |
| | C = A.transpose(); |
| | 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; |
| | } |
| | |
| | } |
| |
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| | |
| | template <typename StorageIndex> |
| | class AMDOrdering |
| | { |
| | public: |
| | typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; |
| | |
| | |
| | |
| | |
| | template <typename MatrixType> |
| | void operator()(const MatrixType& mat, PermutationType& perm) |
| | { |
| | |
| | SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm; |
| | internal::ordering_helper_at_plus_a(mat,symm); |
| | |
| | |
| | |
| | internal::minimum_degree_ordering(symm, perm); |
| | } |
| | |
| | |
| | template <typename SrcType, unsigned int SrcUpLo> |
| | void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm) |
| | { |
| | SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C; C = mat; |
| | |
| | |
| | |
| | internal::minimum_degree_ordering(C, perm); |
| | } |
| | }; |
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| | |
| | template <typename StorageIndex> |
| | class NaturalOrdering |
| | { |
| | public: |
| | typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; |
| | |
| | |
| | template <typename MatrixType> |
| | void operator()(const MatrixType& , PermutationType& perm) |
| | { |
| | perm.resize(0); |
| | } |
| | |
| | }; |
| |
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| | |
| | template<typename StorageIndex> |
| | class COLAMDOrdering |
| | { |
| | public: |
| | typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; |
| | typedef Matrix<StorageIndex, Dynamic, 1> IndexVector; |
| | |
| | |
| | |
| | |
| | template <typename MatrixType> |
| | 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()); |
| | |
| | StorageIndex Alen = internal::Colamd::recommended(nnz, m, n); |
| | |
| | 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]; |
| | |
| | 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; |
| | } |
| | }; |
| |
|
| | } |
| |
|
| | #endif |
| |
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