diff --git a/Eigen/src/OrderingMethods/Ordering.h b/Eigen/src/OrderingMethods/Ordering.h
index b4da6531a..4e0609784 100644
--- a/Eigen/src/OrderingMethods/Ordering.h
+++ b/Eigen/src/OrderingMethods/Ordering.h
@@ -109,7 +109,7 @@ class NaturalOrdering
   * \class COLAMDOrdering
   *
   * Functor computing the \em column \em approximate \em minimum \em degree ordering 
-  * The matrix should be in column-major format
+  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
   */
 template<typename Index>
 class COLAMDOrdering
@@ -118,10 +118,14 @@ class COLAMDOrdering
     typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; 
     typedef Matrix<Index, Dynamic, 1> IndexVector;
     
-    /** Compute the permutation vector form a sparse matrix */
+    /** 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 <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");
+      
       Index m = mat.rows();
       Index n = mat.cols();
       Index nnz = mat.nonZeros();
diff --git a/Eigen/src/SparseQR/SparseQR.h b/Eigen/src/SparseQR/SparseQR.h
index afda43bfc..90f95f4e7 100644
--- a/Eigen/src/SparseQR/SparseQR.h
+++ b/Eigen/src/SparseQR/SparseQR.h
@@ -58,6 +58,7 @@ namespace internal {
   * \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module 
   *  OrderingMethods \endlink module for the list of built-in and external ordering methods.
   * 
+  * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
   * 
   */
 template<typename _MatrixType, typename _OrderingType>
@@ -77,10 +78,23 @@ class SparseQR
     SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
     { }
     
+    /** Construct a QR factorization of the matrix \a mat.
+      * 
+      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+      * 
+      * \sa compute()
+      */
     SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
     {
       compute(mat);
     }
+    
+    /** Computes the QR factorization of the sparse matrix \a mat.
+      * 
+      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+      * 
+      * \sa analyzePattern(), factorize()
+      */
     void compute(const MatrixType& mat)
     {
       analyzePattern(mat);
@@ -255,6 +269,8 @@ class SparseQR
 };
 
 /** \brief Preprocessing step of a QR factorization 
+  * 
+  * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
   * 
   * In this step, the fill-reducing permutation is computed and applied to the columns of A
   * and the column elimination tree is computed as well. Only the sparcity pattern of \a mat is exploited.
@@ -264,6 +280,7 @@ class SparseQR
 template <typename MatrixType, typename OrderingType>
 void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
 {
+  eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
   // Compute the column fill reducing ordering
   OrderingType ord; 
   ord(mat, m_perm_c);