diff --git a/Eigen/src/Core/Redux.h b/Eigen/src/Core/Redux.h
index b2c775d90..1b6a4177a 100644
--- a/Eigen/src/Core/Redux.h
+++ b/Eigen/src/Core/Redux.h
@@ -174,7 +174,7 @@ struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
   typedef typename Derived::Scalar Scalar;
   typedef typename Derived::Index Index;
   EIGEN_DEVICE_FUNC
-  static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
+  static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
   {
     eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
     Scalar res;
@@ -200,10 +200,10 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
   typedef typename packet_traits<Scalar>::type PacketScalar;
   typedef typename Derived::Index Index;
 
-  static Scalar run(const Derived& mat, const Func& func)
+  static Scalar run(const Derived &mat, const Func& func)
   {
     const Index size = mat.size();
-    eigen_assert(size && "you are using an empty matrix");
+    
     const Index packetSize = packet_traits<Scalar>::size;
     const Index alignedStart = internal::first_aligned(mat);
     enum {
@@ -258,7 +258,7 @@ struct redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
   typedef typename packet_traits<Scalar>::type PacketScalar;
   typedef typename Derived::Index Index;
 
-  static Scalar run(const Derived& mat, const Func& func)
+  static Scalar run(const Derived &mat, const Func& func)
   {
     eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
     const Index innerSize = mat.innerSize();
@@ -300,9 +300,8 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
     Size = Derived::SizeAtCompileTime,
     VectorizedSize = (Size / PacketSize) * PacketSize
   };
-  static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func)
+  static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func)
   {
-    eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix");
     Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func));
     if (VectorizedSize != Size)
       res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func));
@@ -310,6 +309,64 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
   }
 };
 
+#ifdef EIGEN_ENABLE_EVALUATORS
+// evaluator adaptor
+template<typename XprType>
+class redux_evaluator
+{
+public:
+  redux_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) {}
+  
+  typedef typename XprType::Index Index;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketScalar PacketScalar;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+  
+  enum {
+    MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
+    // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
+    Flags = XprType::Flags & ~DirectAccessBit,
+    IsRowMajor = XprType::IsRowMajor,
+    SizeAtCompileTime = XprType::SizeAtCompileTime,
+    InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime,
+    CoeffReadCost = XprType::CoeffReadCost
+  };
+  
+  Index rows() const { return m_xpr.rows(); }
+  Index cols() const { return m_xpr.cols(); }
+  Index size() const { return m_xpr.size(); }
+  Index innerSize() const { return m_xpr.innerSize(); }
+  Index outerSize() const { return m_xpr.outerSize(); }
+
+  CoeffReturnType coeff(Index row, Index col) const
+  { return m_evaluator.coeff(row, col); }
+
+  CoeffReturnType coeff(Index index) const
+  { return m_evaluator.coeff(index); }
+
+  template<int LoadMode>
+  PacketReturnType packet(Index row, Index col) const
+  { return m_evaluator.template packet<LoadMode>(row, col); }
+
+  template<int LoadMode>
+  PacketReturnType packet(Index index) const
+  { return m_evaluator.template packet<LoadMode>(index); }
+  
+  CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
+  { return m_evaluator.coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
+  
+  template<int LoadMode>
+  PacketReturnType packetByOuterInner(Index outer, Index inner) const
+  { return m_evaluator.template packet<LoadMode>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
+  
+protected:
+  typename internal::evaluator<XprType>::nestedType m_evaluator;
+  const XprType &m_xpr;
+};
+#endif
+
 } // end namespace internal
 
 /***************************************************************************
@@ -320,7 +377,7 @@ struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling>
 /** \returns the result of a full redux operation on the whole matrix or vector using \a func
   *
   * The template parameter \a BinaryOp is the type of the functor \a func which must be
-  * an associative operator. Both current STL and TR1 functor styles are handled.
+  * an associative operator. Both current C++98 and C++11 functor styles are handled.
   *
   * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
   */
@@ -329,9 +386,19 @@ template<typename Func>
 EIGEN_STRONG_INLINE typename internal::result_of<Func(typename internal::traits<Derived>::Scalar)>::type
 DenseBase<Derived>::redux(const Func& func) const
 {
+  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
+#ifdef EIGEN_TEST_EVALUATORS
+  
+  typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
+  ThisEvaluator thisEval(derived());
+  return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func);
+  
+#else
   typedef typename internal::remove_all<typename Derived::Nested>::type ThisNested;
+  
   return internal::redux_impl<Func, ThisNested>
             ::run(derived(), func);
+#endif
 }
 
 /** \returns the minimum of all coefficients of \c *this.