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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SPARSEPRODUCT_H
#define EIGEN_SPARSEPRODUCT_H
template<typename Lhs, typename Rhs>
struct SparseSparseProductReturnType
{
typedef typename internal::traits<Lhs>::Scalar Scalar;
enum {
LhsRowMajor = internal::traits<Lhs>::Flags & RowMajorBit,
RhsRowMajor = internal::traits<Rhs>::Flags & RowMajorBit,
TransposeRhs = (!LhsRowMajor) && RhsRowMajor,
TransposeLhs = LhsRowMajor && (!RhsRowMajor)
};
typedef typename internal::conditional<TransposeLhs,
SparseMatrix<Scalar,0>,
typename internal::nested<Lhs,Rhs::RowsAtCompileTime>::type>::type LhsNested;
typedef typename internal::conditional<TransposeRhs,
SparseMatrix<Scalar,0>,
typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type>::type RhsNested;
typedef SparseSparseProduct<LhsNested, RhsNested> Type;
};
namespace internal {
template<typename LhsNested, typename RhsNested>
struct traits<SparseSparseProduct<LhsNested, RhsNested> >
{
typedef MatrixXpr XprKind;
// clean the nested types:
typedef typename remove_all<LhsNested>::type _LhsNested;
typedef typename remove_all<RhsNested>::type _RhsNested;
typedef typename _LhsNested::Scalar Scalar;
typedef typename promote_index_type<typename traits<_LhsNested>::Index,
typename traits<_RhsNested>::Index>::type Index;
enum {
LhsCoeffReadCost = _LhsNested::CoeffReadCost,
RhsCoeffReadCost = _RhsNested::CoeffReadCost,
LhsFlags = _LhsNested::Flags,
RhsFlags = _RhsNested::Flags,
RowsAtCompileTime = _LhsNested::RowsAtCompileTime,
ColsAtCompileTime = _RhsNested::ColsAtCompileTime,
MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime,
MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime,
InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime),
EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit),
Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits)
| EvalBeforeAssigningBit
| EvalBeforeNestingBit,
CoeffReadCost = Dynamic
};
typedef Sparse StorageKind;
};
} // end namespace internal
template<typename LhsNested, typename RhsNested>
class SparseSparseProduct : internal::no_assignment_operator,
public SparseMatrixBase<SparseSparseProduct<LhsNested, RhsNested> >
{
public:
typedef SparseMatrixBase<SparseSparseProduct> Base;
EIGEN_DENSE_PUBLIC_INTERFACE(SparseSparseProduct)
private:
typedef typename internal::traits<SparseSparseProduct>::_LhsNested _LhsNested;
typedef typename internal::traits<SparseSparseProduct>::_RhsNested _RhsNested;
public:
template<typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs), m_tolerance(0), m_conservative(true)
{
init();
}
template<typename Lhs, typename Rhs>
EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs, RealScalar tolerance)
: m_lhs(lhs), m_rhs(rhs), m_tolerance(tolerance), m_conservative(false)
{
init();
}
SparseSparseProduct pruned(Scalar reference = 0, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision()) const
{
return SparseSparseProduct(m_lhs,m_rhs,internal::abs(reference)*epsilon);
}
template<typename Dest>
void evalTo(Dest& result) const
{
if(m_conservative)
internal::conservative_sparse_sparse_product_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result);
else
internal::sparse_sparse_product_with_pruning_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result,m_tolerance);
}
EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; }
EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; }
protected:
void init()
{
eigen_assert(m_lhs.cols() == m_rhs.rows());
enum {
ProductIsValid = _LhsNested::ColsAtCompileTime==Dynamic
|| _RhsNested::RowsAtCompileTime==Dynamic
|| int(_LhsNested::ColsAtCompileTime)==int(_RhsNested::RowsAtCompileTime),
AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime,
SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested,_RhsNested)
};
// note to the lost user:
// * for a dot product use: v1.dot(v2)
// * for a coeff-wise product use: v1.cwise()*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)
}
LhsNested m_lhs;
RhsNested m_rhs;
RealScalar m_tolerance;
bool m_conservative;
};
// sparse = sparse * sparse
template<typename Derived>
template<typename Lhs, typename Rhs>
inline Derived& SparseMatrixBase<Derived>::operator=(const SparseSparseProduct<Lhs,Rhs>& product)
{
product.evalTo(derived());
return derived();
}
/** \returns an expression of the product of two sparse matrices.
* By default a conservative product preserving the symbolic non zeros is performed.
* The automatic pruning of the small values can be achieved by calling the pruned() function
* in which case a totally different product algorithm is employed:
* \code
* C = (A*B).pruned(); // supress numerical zeros (exact)
* C = (A*B).pruned(ref);
* C = (A*B).pruned(ref,epsilon);
* \endcode
* where \c ref is a meaningful non zero reference value.
* */
template<typename Derived>
template<typename OtherDerived>
inline const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
{
return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
}
#endif // EIGEN_SPARSEPRODUCT_H
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