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Fix miss use of hg resolve when backporting previous changeset

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This commit is contained in:
Gael Guennebaud 2015-10-12 16:24:19 +02:00
parent 7aa90a3b0f
commit dc0ef2cbed
2 changed files with 3 additions and 39 deletions
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6
Eigen/src/CholmodSupport/CholmodSupport.h
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@ -78,11 +78,7 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
{ {
res.itype = CHOLMOD_INT; res.itype = CHOLMOD_INT;
} }
<<<<<<< local else if (internal::is_same<_Index,SuiteSparse_long>::value)
else if (internal::is_same<_Index,UF_long>::value)
=======
else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
>>>>>>> other
{ {
res.itype = CHOLMOD_LONG; res.itype = CHOLMOD_LONG;
} }

36
Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
View File

@ -32,7 +32,6 @@ namespace Eigen {
} // End namespace internal } // End namespace internal
/** /**
<<<<<<< local
* \ingroup SPQRSupport_Module * \ingroup SPQRSupport_Module
* \class SPQR * \class SPQR
* \brief Sparse QR factorization based on SuiteSparseQR library * \brief Sparse QR factorization based on SuiteSparseQR library
@ -48,52 +47,21 @@ namespace Eigen {
* You can then apply it to a vector. * You can then apply it to a vector.
* *
* R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. * 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 * NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
* *
* \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
* NOTE * 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 SuiteSparse_long. You can get it with SPQR::Index
*
* \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<>
*
* \implsparsesolverconcept
*
*
*/
>>>>>>> other
template<typename _MatrixType> template<typename _MatrixType>
class SPQR class SPQR
{ {
public: public:
typedef typename _MatrixType::Scalar Scalar; typedef typename _MatrixType::Scalar Scalar;
typedef typename _MatrixType::RealScalar RealScalar; typedef typename _MatrixType::RealScalar RealScalar;
<<<<<<< local typedef SuiteSparse_long Index ;
typedef UF_long Index ;
typedef SparseMatrix<Scalar, ColMajor, Index> MatrixType; typedef SparseMatrix<Scalar, ColMajor, Index> MatrixType;
typedef PermutationMatrix<Dynamic, Dynamic> PermutationType; typedef PermutationMatrix<Dynamic, Dynamic> PermutationType;
=======
typedef SuiteSparse_long StorageIndex ;
typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
>>>>>>> other
public: public:
SPQR() SPQR()
: m_isInitialized(false), m_ordering(SPQR_ORDERING_DEFAULT), m_allow_tol(SPQR_DEFAULT_TOL), m_tolerance (NumTraits<Scalar>::epsilon()), m_useDefaultThreshold(true) : m_isInitialized(false), m_ordering(SPQR_ORDERING_DEFAULT), m_allow_tol(SPQR_DEFAULT_TOL), m_tolerance (NumTraits<Scalar>::epsilon()), m_useDefaultThreshold(true)