diff --git a/Eigen/src/CholmodSupport/CholmodSupport.h b/Eigen/src/CholmodSupport/CholmodSupport.h
index b612eb4f9..61156523d 100644
--- a/Eigen/src/CholmodSupport/CholmodSupport.h
+++ b/Eigen/src/CholmodSupport/CholmodSupport.h
@@ -78,11 +78,7 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat)
   {
     res.itype = CHOLMOD_INT;
   }
-<<<<<<< local
-  else if (internal::is_same<_Index,UF_long>::value)
-=======
-  else if (internal::is_same<_StorageIndex,SuiteSparse_long>::value)
->>>>>>> other
+  else if (internal::is_same<_Index,SuiteSparse_long>::value)
   {
     res.itype = CHOLMOD_LONG;
   }
diff --git a/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
index 4657c18c4..36138101d 100644
--- a/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
+++ b/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
@@ -32,7 +32,6 @@ namespace Eigen {
   } // End namespace internal
   
 /**
-<<<<<<< local
  * \ingroup SPQRSupport_Module
  * \class SPQR
  * \brief Sparse QR factorization based on SuiteSparseQR library
@@ -48,52 +47,21 @@ namespace Eigen {
  * 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
+ * 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<>
  * 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>
 class SPQR
 {
   public:
     typedef typename _MatrixType::Scalar Scalar;
     typedef typename _MatrixType::RealScalar RealScalar;
-<<<<<<< local
-    typedef UF_long Index ; 
+    typedef SuiteSparse_long Index ;
     typedef SparseMatrix<Scalar, ColMajor, Index> MatrixType;
     typedef PermutationMatrix<Dynamic, Dynamic> PermutationType;
-=======
-    typedef SuiteSparse_long StorageIndex ;
-    typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
-    typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
->>>>>>> other
   public:
     SPQR() 
       : m_isInitialized(false), m_ordering(SPQR_ORDERING_DEFAULT), m_allow_tol(SPQR_DEFAULT_TOL), m_tolerance (NumTraits<Scalar>::epsilon()), m_useDefaultThreshold(true)