diff --git a/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
index cd996d5ba..5c320e2d2 100644
--- a/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
+++ b/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
@@ -66,9 +66,9 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
     }
 
     // unordered insertion
-    for(int k=0; k<nnz; ++k)
+    for(Index k=0; k<nnz; ++k)
     {
-      int i = indices[k];
+      Index i = indices[k];
       res.insertBackByOuterInnerUnordered(j,i) = values[i];
       mask[i] = false;
     }
@@ -76,8 +76,8 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
 #if 0
     // alternative ordered insertion code:
 
-    int t200 = rows/(log2(200)*1.39);
-    int t = (rows*100)/139;
+    Index t200 = rows/(log2(200)*1.39);
+    Index t = (rows*100)/139;
 
     // FIXME reserve nnz non zeros
     // FIXME implement fast sort algorithms for very small nnz
@@ -90,9 +90,9 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
     if(true)
     {
       if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
-      for(int k=0; k<nnz; ++k)
+      for(Index k=0; k<nnz; ++k)
       {
-        int i = indices[k];
+        Index i = indices[k];
         res.insertBackByOuterInner(j,i) = values[i];
         mask[i] = false;
       }
@@ -100,7 +100,7 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
     else
     {
       // dense path
-      for(int i=0; i<rows; ++i)
+      for(Index i=0; i<rows; ++i)
       {
         if(mask[i])
         {
diff --git a/Eigen/src/SparseCore/SparseDenseProduct.h b/Eigen/src/SparseCore/SparseDenseProduct.h
index 6132a4880..610833f3b 100644
--- a/Eigen/src/SparseCore/SparseDenseProduct.h
+++ b/Eigen/src/SparseCore/SparseDenseProduct.h
@@ -125,7 +125,7 @@ class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNes
     inline Scalar value() const { return Base::value() * m_factor; }
 
   protected:
-    int m_outer;
+    Index m_outer;
     Scalar m_factor;
 };
 
@@ -156,7 +156,7 @@ struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, t
   {
     for(Index c=0; c<rhs.cols(); ++c)
     {
-      int n = lhs.outerSize();
+      Index n = lhs.outerSize();
       for(Index j=0; j<n; ++j)
       {
         typename Res::Scalar tmp(0);
diff --git a/Eigen/src/SparseCore/SparseMatrix.h b/Eigen/src/SparseCore/SparseMatrix.h
index f2e234eee..0e7e760fb 100644
--- a/Eigen/src/SparseCore/SparseMatrix.h
+++ b/Eigen/src/SparseCore/SparseMatrix.h
@@ -402,7 +402,7 @@ class SparseMatrix
       * \sa insertBack, insertBackByOuterInner */
     inline void startVec(Index outer)
     {
-      eigen_assert(m_outerIndex[outer]==int(m_data.size()) && "You must call startVec for each inner vector sequentially");
+      eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially");
       eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
       m_outerIndex[outer+1] = m_outerIndex[outer];
     }
@@ -480,7 +480,7 @@ class SparseMatrix
       if(m_innerNonZeros != 0)
         return; 
       m_innerNonZeros = static_cast<Index*>(std::malloc(m_outerSize * sizeof(Index)));
-      for (int i = 0; i < m_outerSize; i++)
+      for (Index i = 0; i < m_outerSize; i++)
       {
         m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; 
       }
@@ -752,8 +752,8 @@ class SparseMatrix
         else
           for (Index i=0; i<m.outerSize(); ++i)
           {
-            int p = m.m_outerIndex[i];
-            int pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
+            Index p = m.m_outerIndex[i];
+            Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
             Index k=p;
             for (; k<pe; ++k)
               s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
@@ -1022,7 +1022,7 @@ void SparseMatrix<Scalar,_Options,_Index>::sumupDuplicates()
   wi.fill(-1);
   Index count = 0;
   // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
-  for(int j=0; j<outerSize(); ++j)
+  for(Index j=0; j<outerSize(); ++j)
   {
     Index start   = count;
     Index oldEnd  = m_outerIndex[j]+m_innerNonZeros[j];
diff --git a/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
index 1dccdfe1b..fcc18f5c9 100644
--- a/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
+++ b/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
@@ -27,7 +27,7 @@ static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& r
   // make sure to call innerSize/outerSize since we fake the storage order.
   Index rows = lhs.innerSize();
   Index cols = rhs.outerSize();
-  //int size = lhs.outerSize();
+  //Index size = lhs.outerSize();
   eigen_assert(lhs.outerSize() == rhs.innerSize());
 
   // allocate a temporary buffer
diff --git a/test/sparse.h b/test/sparse.h
index a09c65e5f..433d48ec4 100644
--- a/test/sparse.h
+++ b/test/sparse.h
@@ -154,16 +154,16 @@ initSparse(double density,
   sparseMat.finalize();
 }
 
-template<typename Scalar> void
+template<typename Scalar,int Options,typename Index> void
 initSparse(double density,
            Matrix<Scalar,Dynamic,1>& refVec,
-           SparseVector<Scalar>& sparseVec,
+           SparseVector<Scalar,Options,Index>& sparseVec,
            std::vector<int>* zeroCoords = 0,
            std::vector<int>* nonzeroCoords = 0)
 {
   sparseVec.reserve(int(refVec.size()*density));
   sparseVec.setZero();
-  for(int i=0; i<refVec.size(); i++)
+  for(Index i=0; i<refVec.size(); i++)
   {
     Scalar v = (internal::random<double>(0,1) < density) ? internal::random<Scalar>() : Scalar(0);
     if (v!=Scalar(0))
diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
index 664e33887..948b0026b 100644
--- a/test/sparse_product.cpp
+++ b/test/sparse_product.cpp
@@ -244,6 +244,7 @@ void test_sparse_product()
     CALL_SUBTEST_1( (sparse_product<SparseMatrix<double,RowMajor> >()) );
     CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, ColMajor > >()) );
     CALL_SUBTEST_2( (sparse_product<SparseMatrix<std::complex<double>, RowMajor > >()) );
+    CALL_SUBTEST_3( (sparse_product<SparseMatrix<float,ColMajor,long int> >()) );
     CALL_SUBTEST_4( (sparse_product_regression_test<SparseMatrix<double,RowMajor>, Matrix<double, Dynamic, Dynamic, RowMajor> >()) );
   }
 }
diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp
index ec5877b6a..0c9476803 100644
--- a/test/sparse_vector.cpp
+++ b/test/sparse_vector.cpp
@@ -9,14 +9,14 @@
 
 #include "sparse.h"
 
-template<typename Scalar> void sparse_vector(int rows, int cols)
+template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
 {
   double densityMat = (std::max)(8./(rows*cols), 0.01);
   double densityVec = (std::max)(8./float(rows), 0.1);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
-  typedef SparseVector<Scalar> SparseVectorType;
-  typedef SparseMatrix<Scalar> SparseMatrixType;
+  typedef SparseVector<Scalar,0,Index> SparseVectorType;
+  typedef SparseMatrix<Scalar,0,Index> SparseMatrixType;
   Scalar eps = 1e-6;
 
   SparseMatrixType m1(rows,rows);
@@ -101,9 +101,10 @@ template<typename Scalar> void sparse_vector(int rows, int cols)
 void test_sparse_vector()
 {
   for(int i = 0; i < g_repeat; i++) {
-    CALL_SUBTEST_1( sparse_vector<double>(8, 8) );
-    CALL_SUBTEST_2( sparse_vector<std::complex<double> >(16, 16) );
-    CALL_SUBTEST_1( sparse_vector<double>(299, 535) );
+    CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
+    CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(16, 16) ));
+    CALL_SUBTEST_1(( sparse_vector<double,long int>(299, 535) ));
+    CALL_SUBTEST_1(( sparse_vector<double,short>(299, 535) ));
   }
 }