text: // This file is part of Eigen, a lightweight C++ template library
  // for linear algebra.
  //
  // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
  // Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
  // Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
  //
  // This Source Code Form is subject to the terms of the Mozilla
  // Public License v. 2.0. If a copy of the MPL was not distributed
  // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
  
  
  #ifndef EIGEN_PRODUCTEVALUATORS_H
  #define EIGEN_PRODUCTEVALUATORS_H
  
  namespace Eigen {
  
  namespace internal {
  
  /** \internal
    * Evaluator of a product expression.
    * Since products require special treatments to handle all possible cases,
    * we simply defer the evaluation logic to a product_evaluator class
    * which offers more partial specialization possibilities.
    *
    * \sa class product_evaluator
    */
  template<typename Lhs, typename Rhs, int Options>
  struct evaluator<Product<Lhs, Rhs, Options> >
   : public product_evaluator<Product<Lhs, Rhs, Options> >
  {
    typedef Product<Lhs, Rhs, Options> XprType;
    typedef product_evaluator<XprType> Base;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
  };
  
  // Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
  // TODO we should apply that rule only if that's really helpful
  template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
  struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
                                                 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
                                                 const Product<Lhs, Rhs, DefaultProduct> > >
  {
    static const bool value = true;
  };
  template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
  struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
                                 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
                                 const Product<Lhs, Rhs, DefaultProduct> > >
   : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
  {
    typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
                                 const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
                                 const Product<Lhs, Rhs, DefaultProduct> > XprType;
    typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
      : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
    {}
  };
  
  
  template<typename Lhs, typename Rhs, int DiagIndex>
  struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
   : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> >
  {
    typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
    typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
      : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
          Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
          xpr.index() ))
    {}
  };
  
  
  // Helper class to perform a matrix product with the destination at hand.
  // Depending on the sizes of the factors, there are different evaluation strategies
  // as controlled by internal::product_type.
  template< typename Lhs, typename Rhs,
            typename LhsShape = typename evaluator_traits<Lhs>::Shape,
            typename RhsShape = typename evaluator_traits<Rhs>::Shape,
            int ProductType = internal::product_type<Lhs,Rhs>::value>
  struct generic_product_impl;
  
  template<typename Lhs, typename Rhs>
  struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct> > {
    static const bool value = true;
  };
  
  // This is the default evaluator implementation for products:
  // It creates a temporary and call generic_product_impl
  template<typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
  struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
    : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject>
  {
    typedef Product<Lhs, Rhs, Options> XprType;
    typedef typename XprType::PlainObject PlainObject;
    typedef evaluator<PlainObject> Base;
    enum {
      Flags = Base::Flags | EvalBeforeNestingBit
    };
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    explicit product_evaluator(const XprType& xpr)
      : m_result(xpr.rows(), xpr.cols())
    {
      ::new (static_cast<Base*>(this)) Base(m_result);
  
  // FIXME shall we handle nested_eval here?,
  // if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
  //     typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
  //     typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
  //     typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
  //     typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
  //
  //     const LhsNested lhs(xpr.lhs());
  //     const RhsNested rhs(xpr.rhs());
  //
  //     generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
  
      generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
    }
  
  protected:
    PlainObject m_result;
  };
  
  // The following three shortcuts are enabled only if the scalar types match exactly.
  // TODO: we could enable them for different scalar types when the product is not vectorized.
  
  // Dense = Product
  template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
  struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
    typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
  {
    typedef Product<Lhs,Rhs,Options> SrcXprType;
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
    {
      Index dstRows = src.rows();
      Index dstCols = src.cols();
      if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
        dst.resize(dstRows, dstCols);
      // FIXME shall we handle nested_eval here?
      generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
    }
  };
  
  // Dense += Product
  template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
  struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
    typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
  {
    typedef Product<Lhs,Rhs,Options> SrcXprType;
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
    {
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
      // FIXME shall we handle nested_eval here?
      generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
    }
  };
  
  // Dense -= Product
  template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
  struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
    typename enable_if<(Options==DefaultProduct || Options==AliasFreeProduct)>::type>
  {
    typedef Product<Lhs,Rhs,Options> SrcXprType;
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
    {
      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
      // FIXME shall we handle nested_eval here?
      generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
    }
  };
  
  
  // Dense ?= scalar * Product
  // TODO we should apply that rule if that's really helpful
  // for instance, this is not good for inner products
  template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
  struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
                                             const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
  {
    typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
                          const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
                          const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
    {
      call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
    }
  };
  
  //----------------------------------------
  // Catch "Dense ?= xpr + Product<>" expression to save one temporary
  // FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
  
  template<typename OtherXpr, typename Lhs, typename Rhs>
  struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
                                                 const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
    static const bool value = true;
  };
  
  template<typename OtherXpr, typename Lhs, typename Rhs>
  struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
                                                 const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
    static const bool value = true;
  };
  
  template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
  struct assignment_from_xpr_op_product
  {
    template<typename SrcXprType, typename InitialFunc>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
    {
      call_assignment_no_alias(dst, src.lhs(), Func1());
      call_assignment_no_alias(dst, src.rhs(), Func2());
    }
  };
  
  #define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
    template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
    struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
                                              const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
      : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
    {}
  
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_sum_op,add_assign_op);
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
  
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_difference_op,sub_assign_op);
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
  EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
  
  //----------------------------------------
  
  template<typename Lhs, typename Rhs>
  struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
  {
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
  };
  
  
  /***********************************************************************
  *  Implementation of outer dense * dense vector product
  ***********************************************************************/
  
  // Column major result
  template<typename Dst, typename Lhs, typename Rhs, typename Func>
  void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
  {
    evaluator<Rhs> rhsEval(rhs);
    ei_declare_local_nested_eval(Lhs,lhs,Rhs::SizeAtCompileTime,actual_lhs);
    // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
    // FIXME not very good if rhs is real and lhs complex while alpha is real too
    const Index cols = dst.cols();
    for (Index j=0; j<cols; ++j)
      func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
  }
  
  // Row major result
  template<typename Dst, typename Lhs, typename Rhs, typename Func>
  void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
  {
    evaluator<Lhs> lhsEval(lhs);
    ei_declare_local_nested_eval(Rhs,rhs,Lhs::SizeAtCompileTime,actual_rhs);
    // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
    // FIXME not very good if lhs is real and rhs complex while alpha is real too
    const Index rows = dst.rows();
    for (Index i=0; i<rows; ++i)
      func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
  }
  
  template<typename Lhs, typename Rhs>
  struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
  {
    template<typename T> struct is_row_major : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {};
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
    struct set  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived()  = src; } };
    struct add  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
    struct sub  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
    struct adds {
      Scalar m_scale;
      explicit adds(const Scalar& s) : m_scale(s) {}
      template<typename Dst, typename Src> void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const {
        dst.const_cast_derived() += m_scale * src;
      }
    };
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
    }
  
  };
  
  
  // This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
  template<typename Lhs, typename Rhs, typename Derived>
  struct generic_product_impl_base
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
  
  };
  
  template<typename Lhs, typename Rhs>
  struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
    : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct> >
  {
    typedef typename nested_eval<Lhs,1>::type LhsNested;
    typedef typename nested_eval<Rhs,1>::type RhsNested;
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
    enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
    typedef typename internal::remove_all<typename internal::conditional<int(Side)==OnTheRight,LhsNested,RhsNested>::type>::type MatrixType;
  
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      // Fallback to inner product if both the lhs and rhs is a runtime vector.
      if (lhs.rows() == 1 && rhs.cols() == 1) {
        dst.coeffRef(0,0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0));
        return;
      }
      LhsNested actual_lhs(lhs);
      RhsNested actual_rhs(rhs);
      internal::gemv_dense_selector<Side,
                              (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
                              bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)
                             >::run(actual_lhs, actual_rhs, dst, alpha);
    }
  };
  
  template<typename Lhs, typename Rhs>
  struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      // Same as: dst.noalias() = lhs.lazyProduct(rhs);
      // but easier on the compiler side
      call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      // dst.noalias() += lhs.lazyProduct(rhs);
      call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
    }
  
    template<typename Dst>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
    {
      // dst.noalias() -= lhs.lazyProduct(rhs);
      call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
    }
  
    // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h
    // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance:
    //   dst {,+,-}= (s1*A)*(B*s2)
    // will be rewritten as:
    //   dst {,+,-}= (s1*s2) * (A.lazyProduct(B))
    // There are at least four benefits of doing so:
    //  1 - huge performance gain for heap-allocated matrix types as it save costly allocations.
    //  2 - it is faster than simply by-passing the heap allocation through stack allocation.
    //  3 - it makes this fallback consistent with the heavy GEMM routine.
    //  4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
    //      (see https://stackoverflow.com/questions/54738495)
    // For small fixed sizes matrices, howver, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
    // and the behavior depends also a lot on the compiler... This is why this re-writting strategy is currently
    // enabled only when falling back from the main GEMM.
    template<typename Dst, typename Func>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func &func)
    {
      enum {
        HasScalarFactor = blas_traits<Lhs>::HasScalarFactor || blas_traits<Rhs>::HasScalarFactor,
        ConjLhs = blas_traits<Lhs>::NeedToConjugate,
        ConjRhs = blas_traits<Rhs>::NeedToConjugate
      };
      // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto
      //        this is important for real*complex_mat
      Scalar actualAlpha = combine_scalar_factors<Scalar>(lhs, rhs);
  
      eval_dynamic_impl(dst,
                        blas_traits<Lhs>::extract(lhs).template conjugateIf<ConjLhs>(),
                        blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(),
                        func,
                        actualAlpha,
                        typename conditional<HasScalarFactor,true_type,false_type>::type());
    }
  
  protected:
  
    template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar&  s /* == 1 */, false_type)
    {
      EIGEN_UNUSED_VARIABLE(s);
      eigen_internal_assert(s==Scalar(1));
      call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
    }
  
    template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s, true_type)
    {
      call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func);
    }
  };
  
  // This specialization enforces the use of a coefficient-based evaluation strategy
  template<typename Lhs, typename Rhs>
  struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,LazyCoeffBasedProductMode>
    : generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> {};
  
  // Case 2: Evaluate coeff by coeff
  //
  // This is mostly taken from CoeffBasedProduct.h
  // The main difference is that we add an extra argument to the etor_product_*_impl::run() function
  // for the inner dimension of the product, because evaluator object do not know their size.
  
  template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
  struct etor_product_coeff_impl;
  
  template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl;
  
  template<typename Lhs, typename Rhs, int ProductTag>
  struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
      : evaluator_base<Product<Lhs, Rhs, LazyProduct> >
  {
    typedef Product<Lhs, Rhs, LazyProduct> XprType;
    typedef typename XprType::Scalar Scalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    explicit product_evaluator(const XprType& xpr)
      : m_lhs(xpr.lhs()),
        m_rhs(xpr.rhs()),
        m_lhsImpl(m_lhs),     // FIXME the creation of the evaluator objects should result in a no-op, but check that!
        m_rhsImpl(m_rhs),     //       Moreover, they are only useful for the packet path, so we could completely disable them when not needed,
                              //       or perhaps declare them on the fly on the packet method... We have experiment to check what's best.
        m_innerDim(xpr.lhs().cols())
    {
      EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
      EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
  #if 0
      std::cerr << "LhsOuterStrideBytes=  " << LhsOuterStrideBytes << "\n";
      std::cerr << "RhsOuterStrideBytes=  " << RhsOuterStrideBytes << "\n";
      std::cerr << "LhsAlignment=         " << LhsAlignment << "\n";
      std::cerr << "RhsAlignment=         " << RhsAlignment << "\n";
      std::cerr << "CanVectorizeLhs=      " << CanVectorizeLhs << "\n";
      std::cerr << "CanVectorizeRhs=      " << CanVectorizeRhs << "\n";
      std::cerr << "CanVectorizeInner=    " << CanVectorizeInner << "\n";
      std::cerr << "EvalToRowMajor=       " << EvalToRowMajor << "\n";
      std::cerr << "Alignment=            " << Alignment << "\n";
      std::cerr << "Flags=                " << Flags << "\n";
  #endif
    }
  
    // Everything below here is taken from CoeffBasedProduct.h
  
    typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
    typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
  
    typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned;
    typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned;
  
    typedef evaluator<LhsNestedCleaned> LhsEtorType;
    typedef evaluator<RhsNestedCleaned> RhsEtorType;
  
    enum {
      RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
      ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
      InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
      MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
      MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
    };
  
    typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
    typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
  
    enum {
  
      LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
      RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
      CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
                    : InnerSize == Dynamic ? HugeCost
                      : InnerSize * (NumTraits<Scalar>::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost))
                      + (InnerSize - 1) * NumTraits<Scalar>::AddCost,
  
      Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
  
      LhsFlags = LhsEtorType::Flags,
      RhsFlags = RhsEtorType::Flags,
  
      LhsRowMajor = LhsFlags & RowMajorBit,
      RhsRowMajor = RhsFlags & RowMajorBit,
  
      LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
      RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
  
      // Here, we don't care about alignment larger than the usable packet size.
      LhsAlignment = EIGEN_PLAIN_ENUM_MIN(LhsEtorType::Alignment,LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
      RhsAlignment = EIGEN_PLAIN_ENUM_MIN(RhsEtorType::Alignment,RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
  
      SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
  
      CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
      CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
  
      EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
                      : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
                      : (bool(RhsRowMajor) && !CanVectorizeLhs),
  
      Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit)
            | (EvalToRowMajor ? RowMajorBit : 0)
            // TODO enable vectorization for mixed types
            | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0)
            | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
  
      LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
      RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
  
      Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,LhsAlignment))!=0 ? 0 : LhsAlignment)
                : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % EIGEN_PLAIN_ENUM_MAX(1,RhsAlignment))!=0 ? 0 : RhsAlignment)
                : 0,
  
      /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
       * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
       * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
       * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
       */
      CanVectorizeInner =    SameType
                          && LhsRowMajor
                          && (!RhsRowMajor)
                          && (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit)
                          && (int(InnerSize) % packet_traits<Scalar>::size == 0)
    };
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
    {
      return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
    }
  
    /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
     * which is why we don't set the LinearAccessBit.
     * TODO: this seems possible when the result is a vector
     */
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    const CoeffReturnType coeff(Index index) const
    {
      const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
      const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
      return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
    }
  
    template<int LoadMode, typename PacketType>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    const PacketType packet(Index row, Index col) const
    {
      PacketType res;
      typedef etor_product_packet_impl<bool(int(Flags)&RowMajorBit) ? RowMajor : ColMajor,
                                       Unroll ? int(InnerSize) : Dynamic,
                                       LhsEtorType, RhsEtorType, PacketType, LoadMode> PacketImpl;
      PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
      return res;
    }
  
    template<int LoadMode, typename PacketType>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    const PacketType packet(Index index) const
    {
      const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
      const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
      return packet<LoadMode,PacketType>(row,col);
    }
  
  protected:
    typename internal::add_const_on_value_type<LhsNested>::type m_lhs;
    typename internal::add_const_on_value_type<RhsNested>::type m_rhs;
  
    LhsEtorType m_lhsImpl;
    RhsEtorType m_rhsImpl;
  
    // TODO: Get rid of m_innerDim if known at compile time
    Index m_innerDim;
  };
  
  template<typename Lhs, typename Rhs>
  struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
    : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape>
  {
    typedef Product<Lhs, Rhs, DefaultProduct> XprType;
    typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
    typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
    enum {
      Flags = Base::Flags | EvalBeforeNestingBit
    };
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    explicit product_evaluator(const XprType& xpr)
      : Base(BaseProduct(xpr.lhs(),xpr.rhs()))
    {}
  };
  
  /****************************************
  *** Coeff based product, Packet path  ***
  ****************************************/
  
  template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
    {
      etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
      res =  pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
    }
  };
  
  template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
    {
      etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
      res =  pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
    {
      res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
    {
      res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
    {
      res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
    {
      res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
    {
      res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
      for(Index i = 0; i < innerDim; ++i)
        res =  pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
    }
  };
  
  template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
  struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
  {
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
    {
      res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
      for(Index i = 0; i < innerDim; ++i)
        res =  pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
    }
  };
  
  
  /***************************************************************************
  * Triangular products
  ***************************************************************************/
  template<int Mode, bool LhsIsTriangular,
           typename Lhs, bool LhsIsVector,
           typename Rhs, bool RhsIsVector>
  struct triangular_product_impl;
  
  template<typename Lhs, typename Rhs, int ProductTag>
  struct generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag>
    : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag> >
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dest>
    static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      triangular_product_impl<Lhs::Mode,true,typename Lhs::MatrixType,false,Rhs, Rhs::ColsAtCompileTime==1>
          ::run(dst, lhs.nestedExpression(), rhs, alpha);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag>
  struct generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag>
  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag> >
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dest>
    static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      triangular_product_impl<Rhs::Mode,false,Lhs,Lhs::RowsAtCompileTime==1, typename Rhs::MatrixType, false>::run(dst, lhs, rhs.nestedExpression(), alpha);
    }
  };
  
  
  /***************************************************************************
  * SelfAdjoint products
  ***************************************************************************/
  template <typename Lhs, int LhsMode, bool LhsIsVector,
            typename Rhs, int RhsMode, bool RhsIsVector>
  struct selfadjoint_product_impl;
  
  template<typename Lhs, typename Rhs, int ProductTag>
  struct generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag>
    : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag> >
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dest>
    static EIGEN_DEVICE_FUNC
    void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      selfadjoint_product_impl<typename Lhs::MatrixType,Lhs::Mode,false,Rhs,0,Rhs::IsVectorAtCompileTime>::run(dst, lhs.nestedExpression(), rhs, alpha);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag>
  struct generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag>
  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag> >
  {
    typedef typename Product<Lhs,Rhs>::Scalar Scalar;
  
    template<typename Dest>
    static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
    {
      selfadjoint_product_impl<Lhs,0,Lhs::IsVectorAtCompileTime,typename Rhs::MatrixType,Rhs::Mode,false>::run(dst, lhs, rhs.nestedExpression(), alpha);
    }
  };
  
  
  /***************************************************************************
  * Diagonal products
  ***************************************************************************/
  
  template<typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
  struct diagonal_product_evaluator_base
    : evaluator_base<Derived>
  {
     typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
  public:
    enum {
      CoeffReadCost = NumTraits<Scalar>::MulCost + evaluator<MatrixType>::CoeffReadCost + evaluator<DiagonalType>::CoeffReadCost,
  
      MatrixFlags = evaluator<MatrixType>::Flags,
      DiagFlags = evaluator<DiagonalType>::Flags,
  
      _StorageOrder = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
                    : (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor
                    : MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
      _SameStorageOrder = _StorageOrder == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
  
      _ScalarAccessOnDiag =  !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
                             ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
      _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
      // FIXME currently we need same types, but in the future the next rule should be the one
      //_Vectorizable = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagFlags)&PacketAccessBit))),
      _Vectorizable =   bool(int(MatrixFlags)&PacketAccessBit)
                    &&  _SameTypes
                    && (_SameStorageOrder || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
                    && (_ScalarAccessOnDiag || (bool(int(DiagFlags)&PacketAccessBit))),
      _LinearAccessMask = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
      Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixFlags)) | (_Vectorizable ? PacketAccessBit : 0),
      Alignment = evaluator<MatrixType>::Alignment,
  
      AsScalarProduct =     (DiagonalType::SizeAtCompileTime==1)
                        ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
                        ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
    };
  
    EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
      : m_diagImpl(diag), m_matImpl(mat)
    {
      EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
    {
      if(AsScalarProduct)
        return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
      else
        return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
    }
  
  protected:
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const
    {
      return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
                            internal::pset1<PacketType>(m_diagImpl.coeff(id)));
    }
  
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const
    {
      enum {
        InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
        DiagonalPacketLoadMode = EIGEN_PLAIN_ENUM_MIN(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
      };
      return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
                            m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
    }
  
    evaluator<DiagonalType> m_diagImpl;
    evaluator<MatrixType>   m_matImpl;
  };
  
  // diagonal * dense
  template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
  struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
    : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft>
  {
    typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft> Base;
    using Base::m_diagImpl;
    using Base::m_matImpl;
    using Base::coeff;
    typedef typename Base::Scalar Scalar;
  
    typedef Product<Lhs, Rhs, ProductKind> XprType;
    typedef typename XprType::PlainObject PlainObject;
    typedef typename Lhs::DiagonalVectorType DiagonalType;
  
  
    enum { StorageOrder = Base::_StorageOrder };
  
    EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
      : Base(xpr.rhs(), xpr.lhs().diagonal())
    {
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
    {
      return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
    }
  
  #ifndef EIGEN_GPUCC
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
    {
      // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
      // See also similar calls below.
      return this->template packet_impl<LoadMode,PacketType>(row,col, row,
                                   typename internal::conditional<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>::type());
    }
  
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet(Index idx) const
    {
      return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
    }
  #endif
  };
  
  // dense * diagonal
  template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
  struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
    : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight>
  {
    typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight> Base;
    using Base::m_diagImpl;
    using Base::m_matImpl;
    using Base::coeff;
    typedef typename Base::Scalar Scalar;
  
    typedef Product<Lhs, Rhs, ProductKind> XprType;
    typedef typename XprType::PlainObject PlainObject;
  
    enum { StorageOrder = Base::_StorageOrder };
  
    EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
      : Base(xpr.lhs(), xpr.rhs().diagonal())
    {
    }
  
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
    {
      return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
    }
  
  #ifndef EIGEN_GPUCC
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
    {
      return this->template packet_impl<LoadMode,PacketType>(row,col, col,
                                   typename internal::conditional<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>::type());
    }
  
    template<int LoadMode,typename PacketType>
    EIGEN_STRONG_INLINE PacketType packet(Index idx) const
    {
      return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
    }
  #endif
  };
  
  /***************************************************************************
  * Products with permutation matrices
  ***************************************************************************/
  
  /** \internal
    * \class permutation_matrix_product
    * Internal helper class implementing the product between a permutation matrix and a matrix.
    * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
    */
  template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
  struct permutation_matrix_product;
  
  template<typename ExpressionType, int Side, bool Transposed>
  struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
  {
      typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
      typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
  
      template<typename Dest, typename PermutationType>
      static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
      {
        MatrixType mat(xpr);
        const Index n = Side==OnTheLeft ? mat.rows() : mat.cols();
        // FIXME we need an is_same for expression that is not sensitive to constness. For instance
        // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
        //if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
        if(is_same_dense(dst, mat))
        {
          // apply the permutation inplace
          Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(perm.size());
          mask.fill(false);
          Index r = 0;
          while(r < perm.size())
          {
            // search for the next seed
            while(r<perm.size() && mask[r]) r++;
            if(r>=perm.size())
              break;
            // we got one, let's follow it until we are back to the seed
            Index k0 = r++;
            Index kPrev = k0;
            mask.coeffRef(k0) = true;
            for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k))
            {
                    Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
              .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
                         (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
  
              mask.coeffRef(k) = true;
              kPrev = k;
            }
          }
        }
        else
        {
          for(Index i = 0; i < n; ++i)
          {
            Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
                 (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
  
            =
  
            Block<const MatrixTypeCleaned,Side==OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixTypeCleaned::ColsAtCompileTime>
                 (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
          }
        }
      }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
    {
      permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
    {
      permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs)
    {
      permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs)
    {
      permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
    }
  };
  
  
  /***************************************************************************
  * Products with transpositions matrices
  ***************************************************************************/
  
  // FIXME could we unify Transpositions and Permutation into a single "shape"??
  
  /** \internal
    * \class transposition_matrix_product
    * Internal helper class implementing the product between a permutation matrix and a matrix.
    */
  template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
  struct transposition_matrix_product
  {
    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
    typedef typename remove_all<MatrixType>::type MatrixTypeCleaned;
  
    template<typename Dest, typename TranspositionType>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
    {
      MatrixType mat(xpr);
      typedef typename TranspositionType::StorageIndex StorageIndex;
      const Index size = tr.size();
      StorageIndex j = 0;
  
      if(!is_same_dense(dst,mat))
        dst = mat;
  
      for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
        if(Index(j=tr.coeff(k))!=k)
        {
          if(Side==OnTheLeft)        dst.row(k).swap(dst.row(j));
          else if(Side==OnTheRight)  dst.col(k).swap(dst.col(j));
        }
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
    {
      transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
    {
      transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
    }
  };
  
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs)
    {
      transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
    }
  };
  
  template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
  struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag>
  {
    template<typename Dest>
    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs)
    {
      transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
    }
  };
  
  } // end namespace internal
  
  } // end namespace Eigen
  
  #endif // EIGEN_PRODUCT_EVALUATORS_H
  