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* change the nesting order of adjoint_return_type to

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1 - make it easier to catch conjugate expressions
 2 - make sure there is no unecessary copy (we had NestByValue<Derived> which seems to be very bad)
* update eigensolver wrt recent changes
This commit is contained in:
Gael Guennebaud 2009-07-07 15:56:13 +02:00
parent 79877a9917
commit ea23f36c78
5 changed files with 18 additions and 12 deletions
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4
Eigen/src/Cholesky/LLT.h
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@ -116,7 +116,7 @@ template<typename MatrixType, int _UpLo> class LLT
bool m_isInitialized;
};
template<typename MatrixType/*, int UpLo*/>
template<typename MatrixType>
bool ei_inplace_llt_lo(MatrixType& mat)
{
typedef typename MatrixType::Scalar Scalar;
@ -157,7 +157,7 @@ bool ei_inplace_llt_lo(MatrixType& mat)
return true;
}
template<typename MatrixType/*, int UpLo*/>
template<typename MatrixType>
bool ei_inplace_llt_up(MatrixType& mat)
{
typedef typename MatrixType::Scalar Scalar;

6
Eigen/src/Core/MatrixBase.h
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@ -258,8 +258,10 @@ template<typename Derived> class MatrixBase
/** \internal the return type of MatrixBase::imag() */
typedef CwiseUnaryView<ei_scalar_imag_op<Scalar>, Derived> NonConstImagReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef Eigen::Transpose<NestByValue<typename ei_cleantype<ConjugateReturnType>::type> >
AdjointReturnType;
typedef typename ei_meta_if<NumTraits<Scalar>::IsComplex,
CwiseUnaryOp<ei_scalar_conjugate_op<Scalar>, NestByValue<Eigen::Transpose<Derived> > >,
Transpose<Derived>
>::ret AdjointReturnType;
/** \internal the return type of MatrixBase::eigenvalues() */
typedef Matrix<typename NumTraits<typename ei_traits<Derived>::Scalar>::Real, ei_traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
/** \internal expression tyepe of a column */

8
Eigen/src/Core/Product.h
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@ -697,7 +697,8 @@ struct ei_cache_friendly_product_selector<ProductType,1,LhsOrder,LhsAccess,RhsCo
{};
/** \internal */
/** \internal
* Overloaded to perform an efficient C += A*B */
template<typename Derived>
template<typename Lhs,typename Rhs>
inline Derived&
@ -710,7 +711,8 @@ MatrixBase<Derived>::operator+=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProdu
return derived();
}
/** \internal */
/** \internal
* Overloaded to perform an efficient C -= A*B */
template<typename Derived>
template<typename Lhs,typename Rhs>
inline Derived&
@ -723,6 +725,8 @@ MatrixBase<Derived>::operator-=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProdu
return derived();
}
/** \internal
* Overloaded to perform an efficient C = A*B */
template<typename Derived>
template<typename Lhs, typename Rhs>
inline Derived& MatrixBase<Derived>::lazyAssign(const Product<Lhs,Rhs,CacheFriendlyProduct>& product)

2
Eigen/src/Core/Transpose.h
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@ -181,7 +181,7 @@ template<typename Derived>
inline const typename MatrixBase<Derived>::AdjointReturnType
MatrixBase<Derived>::adjoint() const
{
return conjugate().nestByValue();
return transpose().nestByValue();
}
/***************************************************************************

10
Eigen/src/QR/SelfAdjointEigenSolver.h
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@ -259,11 +259,11 @@ compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors
// compute C = inv(L) A inv(L')
MatrixType matC = matA;
cholB.matrixL().solveTriangularInPlace(matC);
cholB.matrixL().solveInPlace(matC);
// FIXME since we currently do not support A * inv(L'), let's do (inv(L) A')' :
matC = matC.adjoint().eval();
cholB.matrixL().template marked<LowerTriangular>().solveTriangularInPlace(matC);
matC = matC.adjoint().eval();
matC.adjointInPlace();
cholB.matrixL().solveInPlace(matC);
matC.adjointInPlace();
// this version works too:
// matC = matC.transpose();
// cholB.matrixL().conjugate().template marked<LowerTriangular>().solveTriangularInPlace(matC);
@ -277,7 +277,7 @@ compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors
if (computeEigenvectors)
{
// transform back the eigen vectors: evecs = inv(U) * evecs
cholB.matrixL().adjoint().template marked<UpperTriangular>().solveTriangularInPlace(m_eivec);
cholB.matrixU().solveInPlace(m_eivec);
for (int i=0; i<m_eivec.cols(); ++i)
m_eivec.col(i) = m_eivec.col(i).normalized();
}