diff --git a/Eigen/src/Sparse/SparseProduct.h b/Eigen/src/Sparse/SparseProduct.h
index d3ef57455..c98a71e99 100644
--- a/Eigen/src/Sparse/SparseProduct.h
+++ b/Eigen/src/Sparse/SparseProduct.h
@@ -171,6 +171,55 @@ class SparseProduct : ei_no_assignment_operator,
     RhsNested m_rhs;
 };
 
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template<typename Lhs, typename Rhs, typename ResultType>
+static void ei_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+  typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  int rows = lhs.innerSize();
+  int cols = rhs.outerSize();
+  //int size = lhs.outerSize();
+  ei_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<Scalar> tempVector(rows);
+
+  // estimate the number of non zero entries
+  float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
+  float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
+  float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
+
+  res.resize(rows, cols);
+  res.startFill(int(ratioRes*rows*cols));
+  for (int j=0; j<cols; ++j)
+  {
+    // let's do a more accurate determination of the nnz ratio for the current column j of res
+    //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
+    // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
+    float ratioColRes = ratioRes;
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+      tempVector.restart();
+      Scalar x = rhsIt.value();
+      for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
+      {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
+      if (ResultType::Flags&RowMajorBit)
+        res.fill(j,it.index()) = it.value();
+      else
+        res.fill(it.index(), j) = it.value();
+  }
+  res.endFill();
+}
+
 template<typename Lhs, typename Rhs, typename ResultType,
   int LhsStorageOrder = ei_traits<Lhs>::Flags&RowMajorBit,
   int RhsStorageOrder = ei_traits<Rhs>::Flags&RowMajorBit,
@@ -184,58 +233,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
 
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
-    // make sure to call innerSize/outerSize since we fake the storage order.
-    int rows = lhs.innerSize();
-    int cols = rhs.outerSize();
-    //int size = lhs.outerSize();
-    ei_assert(lhs.outerSize() == rhs.innerSize());
-
-    // allocate a temporary buffer
-    AmbiVector<Scalar> tempVector(rows);
-
-    // estimate the number of non zero entries
-    float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
-    float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
-    float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
-
-    res.resize(rows, cols);
-    res.startFill(int(ratioRes*rows*cols));
-    for (int j=0; j<cols; ++j)
-    {
-      // let's do a more accurate determination of the nnz ratio for the current column j of res
-      //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
-      // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
-      float ratioColRes = ratioRes;
-      tempVector.init(ratioColRes);
-      tempVector.setZero();
-      for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
-      {
-        // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
-        tempVector.restart();
-        Scalar x = rhsIt.value();
-        for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
-        {
-          tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
-        }
-      }
-      for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
-        if (ResultType::Flags&RowMajorBit)
-          res.fill(j,it.index()) = it.value();
-        else
-          res.fill(it.index(), j) = it.value();
-    }
-    res.endFill();
+    typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
+    ei_sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
+    res.swap(_res);
   }
 };
 
 template<typename Lhs, typename Rhs, typename ResultType>
 struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
 {
-  typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
     SparseTemporaryType _res(res.rows(), res.cols());
-    ei_sparse_product_selector<Lhs,Rhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>::run(lhs, rhs, _res);
+    ei_sparse_product_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res);
     res = _res;
   }
 };
@@ -246,20 +258,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
     // let's transpose the product to get a column x column product
-    ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,ColMajor>::run(rhs, lhs, res);
+    typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
+    ei_sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
+    res.swap(_res);
   }
 };
 
 template<typename Lhs, typename Rhs, typename ResultType>
 struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
 {
-  typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
     // let's transpose the product to get a column x column product
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
     SparseTemporaryType _res(res.cols(), res.rows());
-    ei_sparse_product_selector<Rhs,Lhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>
-      ::run(rhs, lhs, _res);
+    ei_sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
     res = _res.transpose();
   }
 };
@@ -322,7 +335,7 @@ inline Derived& SparseMatrixBase<Derived>::operator=(const SparseProduct<Lhs,Rhs
 template<typename Derived>
 template<typename Lhs, typename Rhs>
 Derived& MatrixBase<Derived>::lazyAssign(const SparseProduct<Lhs,Rhs,SparseTimeDenseProduct>& product)
-{ 
+{
   typedef typename ei_cleantype<Lhs>::type _Lhs;
   typedef typename ei_cleantype<Rhs>::type _Rhs;
   typedef typename _Lhs::InnerIterator LhsInnerIterator;
diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp
index 95b3546c1..d2c3b1c98 100644
--- a/test/sparse_product.cpp
+++ b/test/sparse_product.cpp
@@ -30,14 +30,13 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
   const int cols = ref.cols();
   typedef typename SparseMatrixType::Scalar Scalar;
   enum { Flags = SparseMatrixType::Flags };
-  
+
   double density = std::max(8./(rows*cols), 0.01);
   typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
   typedef Matrix<Scalar,Dynamic,1> DenseVector;
   Scalar eps = 1e-6;
 
   // test matrix-matrix product
-  /*
   {
     DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
     DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
@@ -65,9 +64,10 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
     VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
     VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
     VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
+
+    VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3);
   }
-  */
-  
+
   // test matrix - diagonal product
   {
     DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
@@ -77,46 +77,44 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
     SparseMatrixType m3(rows, rows);
     initSparse<Scalar>(density, refM2, m2);
     initSparse<Scalar>(density, refM3, m3);
-//     std::cerr << "foo\n" << (m2*d1).toDense() << "\n\n" << refM2*d1 << "\n\n";
     VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
     VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
     VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
-//     std::cerr << "foo\n" << (d1*m2.transpose()).toDense() << "\n\n" << d1 * refM2.transpose() << "\n\n";
     VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
   }
-  
+
   // test self adjoint products
-//   {
-//     DenseMatrix b = DenseMatrix::Random(rows, rows);
-//     DenseMatrix x = DenseMatrix::Random(rows, rows);
-//     DenseMatrix refX = DenseMatrix::Random(rows, rows);
-//     DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
-//     DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
-//     DenseMatrix refS = DenseMatrix::Zero(rows, rows);
-//     SparseMatrixType mUp(rows, rows);
-//     SparseMatrixType mLo(rows, rows);
-//     SparseMatrixType mS(rows, rows);
-//     do {
-//       initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
-//     } while (refUp.isZero());
-//     refLo = refUp.transpose().conjugate();
-//     mLo = mUp.transpose().conjugate();
-//     refS = refUp + refLo;
-//     refS.diagonal() *= 0.5;
-//     mS = mUp + mLo;
-//     for (int k=0; k<mS.outerSize(); ++k)
-//       for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
-//         if (it.index() == k)
-//           it.valueRef() *= 0.5;
-//     
-//     VERIFY_IS_APPROX(refS.adjoint(), refS);
-//     VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
-//     VERIFY_IS_APPROX(mS, refS);
-//     VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
-//     VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
-//     VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
-//     VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
-//   }
+  {
+    DenseMatrix b = DenseMatrix::Random(rows, rows);
+    DenseMatrix x = DenseMatrix::Random(rows, rows);
+    DenseMatrix refX = DenseMatrix::Random(rows, rows);
+    DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refS = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType mUp(rows, rows);
+    SparseMatrixType mLo(rows, rows);
+    SparseMatrixType mS(rows, rows);
+    do {
+      initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
+    } while (refUp.isZero());
+    refLo = refUp.transpose().conjugate();
+    mLo = mUp.transpose().conjugate();
+    refS = refUp + refLo;
+    refS.diagonal() *= 0.5;
+    mS = mUp + mLo;
+    for (int k=0; k<mS.outerSize(); ++k)
+      for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
+        if (it.index() == k)
+          it.valueRef() *= 0.5;
+
+    VERIFY_IS_APPROX(refS.adjoint(), refS);
+    VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
+    VERIFY_IS_APPROX(mS, refS);
+    VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
+    VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
+    VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
+    VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
+  }
 
 }
 
@@ -126,7 +124,7 @@ void test_sparse_product()
     CALL_SUBTEST( sparse_product(SparseMatrix<double>(8, 8)) );
     CALL_SUBTEST( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) );
     CALL_SUBTEST( sparse_product(SparseMatrix<double>(33, 33)) );
-    
+
     CALL_SUBTEST( sparse_product(DynamicSparseMatrix<double>(8, 8)) );
   }
 }