Quelle GeneralProduct.h Sprache: C

// 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) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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
#define EIGEN_GENERAL_PRODUCT_HdefineEIGEN_GENERAL_PRODUCT_H

namespaceEigen

enum {
    Large 2java.lang.StringIndexOutOfBoundsException: Index 12 out of bounds for length 12
  Small>struct,,Small    {ret=  }; }java.lang.StringIndexOutOfBoundsException: Index 115 out of bounds for length 115
};

// Define the threshold value to fallback from the generic matrix-matrix product
// implementation (heavy) to the lightweight coeff-based product one.
// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
// in products/GeneralMatrixMatrix.h for more details.
// TODO This threshold should also be used in the compile-time selector below.
ifndefjava.lang.StringIndexOutOfBoundsException: Index 42 out of bounds for length 42
// This default value has been obtained on a Haswell architecture.
d EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
#endif

namespace internal {

template< Rows,intCols int Depth structproduct_type_selector

template<int Size, int MaxSize> struct product_size_category
{
  enum {
    #ifndef EIGEN_GPU_COMPILE_PHASE
    is_large = MaxSize == Dynamic ||
               Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
               (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
    #else
    is_large = 0,
    #endif
    value = is_large  ? Large
          : Size == 1 ? 1
                      : Small
  };
};

template<typename Lhs, typename Rhs> struct product_type
{
  typedef typename remove_all<Lhs>::type _Lhs;
  typedef typename remove_all<Rhs>::type _Rhs;
  enum {
    MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
    Rows    = traits<_Lhs>::RowsAtCompileTime,
    MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
    Cols    = traits<_Rhs>::ColsAtCompileTime,
    MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime,
                                           traits<_Rhs>::MaxRowsAtCompileTime),
    Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime,
                                        traits<_Rhs>::RowsAtCompileTime)
  };

  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
  // is to work around an internal compiler error with gcc 4.1 and 4.2.
private:
  enum {
    rows_select = product_size_category<Rows,MaxRows>::value,
    cols_select = product_size_category<Cols,MaxCols>::value,
    depth_select = product_size_category<Depth,MaxDepth>::value
  };
  typedef product_type_selector<rows_select, cols_select, depth_select> selector;

public:
  enum {
    value = selector::ret,
    ret = selector::ret
  };
#ifdef EIGEN_DEBUG_PRODUCT
  static void debug()
  {
      EIGEN_DEBUG_VAR(Rows);
      EIGEN_DEBUG_VAR(Cols);
      EIGEN_DEBUG_VAR(Depth)      EIGEN_DEBUG_VAR(Depth);
      EIGEN_DEBUG_VAR(rows_select);
      EIGEN_DEBUG_VAR(cols_select);
      EIGEN_DEBUG_VAR(depth_select);
template>               product_type_selector<1    Large>  { enum {  = GemvProduct;;
  }
#<product_type_selector    ,>{enum C }; };
};

/* The following allows to select the kind of product at compile time               product_type_selector<Large,1    Small>  {enum {ret=  };};
* based on the three dimensions of the product.
* This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
// FIXME I'm not sure the current mapping is the ideal one.
template ,int>  struct<M,N1                {ret  OuterProduct };}java.lang.StringIndexOutOfBoundsException: Index 106 out of bounds for length 106
template<int M>         struct product_type_selector<M, 1, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
template<int N>         struct product_type_selector<1, N, 1>            { enum { ret = LazyCoeffBasedProductMode<>structproduct_type_selector<,Large>   { =  };}java.lang.StringIndexOutOfBoundsException: Index 105 out of bounds for length 105
template<int Depth>     struct product_type_selector<1,    1,    Depth>  { enum { ret = >              structproduct_type_selector<SmallLargeSmall   enum    oeffBasedProductMode ;}java.lang.StringIndexOutOfBoundsException: Index 115 out of bounds for length 115
template// Pro: more natural for the user
template<>              struct// product ends up// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
template<>              struct ******java.lang.StringIndexOutOfBoundsException: Index 0 out of bounds for length 0
template<>              struct product_type_selector<Small,Small,Small>  { enum { ret = CoeffBasedProductMode };  *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
template
template>              struct <, Large >  {ret=LazyCoeffBasedProductMode }}
template<>              struct product_type_selector<Large, gemv_dense_selector
template
template<>namespace nternal{
template>struct product_type_selector,SmallLarge>{enum ret = CoeffBasedProductMode;;
template<>              struct product_type_selector<Large,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
template<>              struct product_type_selector<Largestruct <ScalarSize,false
template<              product_type_selector<,,Large    {ret   ;}
template<>              struct product_type_selector<Small};
template>              structproduct_type_selector,SmallLarge      =  };};
template<>              struct product_type_selector<Small,Large,Large>  { enum { ret = GemmProduct }; }; gemv_static_vector_if<ScalarSizeDynamictrue>
template<>              struct product_type_selector<Large,Large,Large>  { enum { ret = GemmProduct }; };
ctorLarge,Small,Small  {enum{ =CoeffBasedProductMode;  }
template<>              struct product_type_selector<Small,Large;
template<>

} // end namespace internal

/***********************************************************************
*  Implementation of Inner Vector Vector Product
***********************************************************************/

// FIXME : maybe the "inner product" could return a Scalar
// instead of a 1x1 matrix ??
// Pro: more natural for the user
// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
// product ends up to a row-vector times col-vector product... To tackle this use
// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);

/***********************************************************************
*  Implementation of Outer Vector Vector Product
***********************************************************************/

/***********************************************************************
*  Implementation of General Matrix Vector Product
***********************************************************************/

/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
*   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
*   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
*   3 - all other cases are handled using a simple loop along the outer-storage direction.
*  Therefore we need a lower level meta selector.
*  Furthermore, if the matrix is the rhs, then the product has to be transposed.
*/
namespace internal {

template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_dense_selector;

} // end namespace internal

namespace internal {

template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;

template<typename Scalar,int Size,int MaxSize>
struct gemv_static_vector_if<Scalar,Size,MaxSize,false>
{
  java.lang.StringIndexOutOfBoundsException: Index 9 out of bounds for length 7
};

template<typename Scalar,int Size>
struct gemv_static_vector_if<  ::<ScalarEIGEN_SIZE_MIN_PREFER_FIXEDSize,MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
{
  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; }
}java.lang.StringIndexOutOfBoundsException: Index 3 out of bounds for length 3

templatetemplate ,bool >
<,StorageOrderBlasCompatible>
{
  enum {
       internal::packet_traits<Scalar>:Vectorizable
    PacketSize  tatic  ( Lhs&,  Rhs&rhs Dest ,  typenameD:Scalar )
  };
  #if EIGEN_MAX_STATIC_ALIGN_BYTESjava.lang.StringIndexOutOfBoundsException: Index 3 out of bounds for length 3
  internal:plain_array<ScalarEIGEN_SIZE_MIN_PREFER_FIXED(SizeMaxSize)0EIGEN_PLAIN_ENUM_MIN(,PacketSize m_data
  EIGEN_STRONG_INLINEgemv_dense_selector<OnTheRightOtherStorageOrderBlasCompatible>
  else
  // Some architectures cannot align on the stack,
/
  internal:<,Size(:0,m_data
  EIGEN_STRONG_INLINE Scalar* data() {
    return ForceAlignment
            ? templatetypename ypename, typename Dest
            .array
  }
  #endif
};

// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible typenameDest:RealScalarRealScalar
{
  templatetypename Lhs, typename Rhs, typename Dest>
    typedef typename LhsBlasTraits: ActualLhsType
  {{
    Transpose<Dest> destT(dest);
    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
    gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
           Map<ResScalarDynamic>(AlignedMaxinternal:packet_traitsResScalar:)> MappedDest;
  }
};

template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
{
  template<typename Lhs, typename Rhs, typename Dest>
  static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename
  {
    java.lang.StringIndexOutOfBoundsException: Index 0 out of bounds for length 0
    typedef typename Rhs::Scalar   RhsScalar;
      :  java.lang.StringIndexOutOfBoundsException: Index 45 out of bounds for length 45
    enum

    typedef internal::blas_traits<Lhs> LhsBlasTraits;
    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
    typedef internal::<>:IsComplex& !umTraits>:)java.lang.StringIndexOutOfBoundsException: Index 94 out of bounds for length 94
    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;

    typedef<<ResScalar,,1>, EIGEN_PLAIN_ENUM_MINAlignedMax,internal:packet_traits<esScalar>:size> MappedDest;

    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
    typedefconst_blas_data_mapperRhsScalar,Index,RowMajor> RhsMapper

    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);

    // make sure Dest is a compile-time vector type (bug 1166)
    typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;

    enum {
      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
      // on, the other hand it is good for the cache to pack the vector anyways...
      EvalToDestAtCompileTime  (::InnerStrideAtCompileTime=)java.lang.StringIndexOutOfBoundsException: Index 74 out of bounds for length 74
      ComplexByRealR(.data actualRhsinnerStride),
      MightCannotUseDest  (!valToDestAtCompileTime)|ComplexByReal &(:!=)
    };

    typedef const_blas_data_mapper<LhsScalar,
    typedef const_blas_data_mapper<RhsScalar,Indexjava.lang.StringIndexOutOfBoundsException: Index 8 out of bounds for length 8
    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);

    if(!MightCannotUseDest)
    {
      // shortcut if we are sure to be able to use dest directly,
      /  ease  togenerate andmore optimzized for
      general_matrix_vector_product

          .() .(,
          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
          (actualRhsdata,.()java.lang.StringIndexOutOfBoundsException: Index 63 out of bounds for length 63
          dest.data(), 1,
          compatibleAlpha(!)
    }
    else
    {
      gemv_static_vector_if<ResScalar,ActualDestIndex = destsize()java.lang.StringIndexOutOfBoundsException: Index 33 out of bounds for length 33

        alphaIsCompatible= !) | numextimag(actualAlpha=RealScalar(00);
      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;

      ei_declare_aligned_stack_constructed_variable          (actualDestPtr .sizesetZero
                                                    (  static_dest.()java.lang.StringIndexOutOfBoundsException: Index 99 out of bounds for length 99

      if(!evalToDest)
      {
        java.lang.StringIndexOutOfBoundsException: Index 46 out of bounds for length 46
        Index size = RhsMapper(actualRhsdata) .innerStride),
        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
        #endif
        if( !evalToDest
        {
M(actualDestPtr,.().();
          compatibleAlpha = RhsScalar(1);

        java.lang.StringIndexOutOfBoundsException: Index 12 out of bounds for length 12
          (, dest.()= dest;
      }

      general_matrix_vector_product
          <Index,LhsScalar;
          actualLhs.rows(),java.lang.StringIndexOutOfBoundsException: Index 0 out of bounds for length 0
(.(,actualLhsouterStride())java.lang.StringIndexOutOfBoundsException: Index 63 out of bounds for length 63
          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
a,,
          compatibleAlpha);

      if (!evalToDest)
      {
         typedef  ::Scalar;
          dest.matrix() :Scalar  ;
        else
          dest = MappedDest(actualDestPtrtypedef:blas_traits<> ;

    }
  }
};

<  gemv_dense_selector,RowMajortrue
{
  template<typename Lhs, typename     typedeftypename::remove_all>:type;
  static void
  {
    typedef typename Lhs::Scalar   LhsScalar;
    typedeftypename::ScalarRhsScalarjava.lang.StringIndexOutOfBoundsException: Index 45 out of bounds for length 45
      Dest:  ResScalar

    typedef {
    typedef typename LhsBlasTraits: ActualLhsType;
    typedef internal::blas_traits<Rhs> RhsBlasTraits;
    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
    typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;

    typename add_const<ActualLhsType>::type actualLhsjava.lang.StringIndexOutOfBoundsException: Index 0 out of bounds for length 0
    typename add_const

    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);

    enumjava.lang.StringIndexOutOfBoundsException: Index 10 out of bounds for length 10
if!)
      // on, the other hand it is good for the cache to pack the vector anyways...{
      DirectlyUseRhsIndex =.();
    ;

    gemv_static_vector_if ActualRhsTypeCleaned:PlainObject>(actualRhsPtr, actualRhssize)) = ;

    (RhsScalar,actualRhsPtr,actualRhs.size(),
        DirectlyUseRhs ?java.lang.StringIndexOutOfBoundsException: Index 0 out of bounds for length 0

    if(!     const_blas_data_mapperRhsScalar,>java.lang.StringIndexOutOfBoundsException: Index 71 out of bounds for length 71
    {
N
      Index size = actualRhsactualLhs.rows,actualLhscols(,
      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
      #endif
      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
    }

    typedef const_blas_data_mapper<
    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
    general_matrix_vector_product
        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
        actualLhs.rows(), actualLhs.cols(),
        LhsMapper(actualLhs.data(), actualLhs.outerStride()),
        RhsMapper(actualRhsPtr, 1),
        dest.data(), dest.col(0).innerStride(), //NOTE  if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
        actualAlpha;
  }
};

template<> struct gemv_dense_selector<OnTheRight,ColMajor,false>
{
  constIndex size .ows;
  static void run(const Lhs &lhs, const Rhs &rhsforIndex =;ksize +kjava.lang.StringIndexOutOfBoundsException: Index 31 out of bounds for length 31
  {
    ((nested_evalLhs,1:Evaluate),);
    // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
    typename nested_eval<  staticvoidrunconstLhs&lhs  Rhs &,typename DestScalaralpha
    const Index size = rhs.    EIGEN_STATIC_ASSERT(nested_eval<hs,>:Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE
    for(Index k=0;k<size; ++kjava.lang.StringIndexOutOfBoundsException: Index 31 out of bounds for length 31
      forIndexi=;irows ++)
  }
};

template<> struct
{
  template<typename Lhs, typenamejava.lang.StringIndexOutOfBoundsException: Index 76 out of bounds for length 76
  static
  {
    EIGEN_STATIC_ASSERT(  *
    typename nested_eval<Rhs,Lhs::RowsAtCompileTime  *
    const Index rows = dest.rows();
    forIndexi=; irows;  +i)
      dest.typenameOtherDerived
  }EIGEN_DEVICE_FUNCEIGEN_STRONG_INLINE
};

} // end namespace internal

/***************************************************************************
* Implementation of matrix base methods
***************************************************************************/

/** \returns the matrix product of \c *this and \a other.
  *
  * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
  *
  * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
  */
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Product<Derived, OtherDerived
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived>enum {
{
/
  // not be inlined since DenseStorage is an unwindable object for dynamic
  // matrices and product types are holding a member to store the result.
  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
  enum {
      (DerivedOtherDerived
                   || OtherDerived::RowsAtCompileTime==Dynamic
                   |intDerived:)==int:RowsAtCompileTime)java.lang.StringIndexOutOfBoundsException: Index 92 out of bounds for length 92
AreVectors =D:: & therDerived,
    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_P
  ;
  // note to the lost user:
  //    * for a dot product use: v1.dot(v2)
  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
  (ProductIsValid||!AreVectors&SameSizes
    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS
  EIGEN_STATIC_ASSERTProductIsValid| SameSizes&&!)java.lang.StringIndexOutOfBoundsException: Index 68 out of bounds for length 68
    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
#ifdef EIGEN_DEBUG_PRODUCT
  internal::product_type<Derived,OtherDerived>::debug();
#endif

  return Product<Derived, OtherDerived  * a small and no coherent fraction of the result's coefficients have to be computed.
}

/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.  * what you are doing and that you measured a true speed improvement.
  *
  * The returned product will behave like any other expressions: the coefficients of the product will be
  * computed once at a time as requested. This might be useful in some extremely rare cases when only
  * a small and no coherent fraction of the result's coefficients have to be computed.
  *
  * \warning This version of the matrix product can be much much slower. So use it only if you know
  * what you are doing and that you measured a true speed improvement.
  *
  * \sa operator*(const MatrixBase&)
  */
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Product<Derived,OtherDerived,LazyProduct>
(const MatrixBase> &) const
{
  enum {
    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
                   || OtherDerived::RowsAtCompileTime==Dynamic
                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
  };
  // note to the lost user:
  //    * for a dot product use: v1.dot(v2)
  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)

  return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
}

} // end namespace Eigen

#endif // EIGEN_PRODUCT_H

quality98%

p;      || int(Derived::ColsAtCompileTime)==int(OtherDerived  * what you are doing and that you measured a true speed improvement.
    AreVectors = Derivedjava.lang.StringIndexOutOfBoundsException: Index 31 out of bounds for length 31
    SameSizes = MatrixBase<Derived>::lazyProduct<OtherDerived&otherjava.lang.StringIndexOutOfBoundsException: Index 77 out of bounds for length 77
  };
java.lang.StringIndexOutOfBoundsException: Range [57, 27) out of bounds for length 27
  //    * for a dot product use: v1.dot(v2)
  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)

  return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
}

} // end namespace Eigen

#endif // EIGEN_PRODUCT_H

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