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目录

一  一种枚举类型的新型使用方式

二 Eigen库中的LDLT分解

三 Eigen中的访问者模式

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一  一种枚举类型的新型使用方式

///D:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.30133\include\xiosbase							enum _Iostate { // constants for stream states							_Statmask = 0x17							};							static constexpr _Iostate goodbit = static_cast<_Iostate>(0x0);							static constexpr _Iostate eofbit  = static_cast<_Iostate>(0x1);							static constexpr _Iostate failbit = static_cast<_Iostate>(0x2);							static constexpr _Iostate badbit  = static_cast<_Iostate>(0x4);							_NODISCARD bool __CLR_OR_THIS_CALL good() const {							return rdstate() == ios_base::goodbit;							}							_NODISCARD bool __CLR_OR_THIS_CALL eof() const {							return rdstate() & ios_base::eofbit;							}							_NODISCARD bool __CLR_OR_THIS_CALL fail() const {							return rdstate() & (ios_base::badbit | ios_base::failbit);							}							_NODISCARD bool __CLR_OR_THIS_CALL bad() const {							return rdstate() & ios_base::badbit;							}							_NODISCARD iostate __CLR_OR_THIS_CALL exceptions() const {							return _Except;							}							void __CLR_OR_THIS_CALL exceptions(iostate _Newexcept) { // set exception mask to argument							_Except = _Newexcept & _Statmask;							clear(rdstate());							}							

二 Eigen库中的LDLT分解

													
Eigen-3.4.0\Eigen\src\Cholesky\LDLT.h													
template<typename _MatrixType,int _UpLo>													
template<bool Conjugate, typename RhsType, typename DstType>													
void LDLT<_MatrixType,_UpLo>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const													
{													// dst = P b													dst = m_transpositions * rhs;													// dst = L^-1 (P b)													// dst = L^-*T (P b)													matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);													// dst = D^-* (L^-1 P b)													// dst = D^-1 (L^-*T P b)													// more precisely, use pseudo-inverse of D (see bug 241)													using std::abs;													const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());													// In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())													// and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:													// RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());													// However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest													// diagonal element is not well justified and leads to numerical issues in some cases.													// Moreover, Lapack's xSYTRS routines use 0 for the tolerance.													// Using numeric_limits::min() gives us more robustness to denormals.													RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();													for (Index i = 0; i < vecD.size(); ++i)													{													if(abs(vecD(i)) > tolerance)													dst.row(i) /= vecD(i);													else													dst.row(i).setZero();													}													// dst = L^-* (D^-* L^-1 P b)													// dst = L^-T (D^-1 L^-*T P b)													matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);													// dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b													// dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b													dst = m_transpositions.transpose() * dst;													
}													
#endif													

三 Eigen中的访问者模式

Eigen-3.4.0\Eigen\src\Core\Visitor.h
template<typename Derived>
template<typename Visitor>
EIGEN_DEVICE_FUNC
void DenseBase<Derived>::visit(Visitor& visitor) const
{if(size()==0)return;typedef typename internal::visitor_evaluator<Derived> ThisEvaluator;ThisEvaluator thisEval(derived());enum {unroll =  SizeAtCompileTime != Dynamic&& SizeAtCompileTime * int(ThisEvaluator::CoeffReadCost) + (SizeAtCompileTime-1) * int(internal::functor_traits<Visitor>::Cost) <= EIGEN_UNROLLING_LIMIT};return internal::visitor_impl<Visitor, ThisEvaluator, unroll ? int(SizeAtCompileTime) : Dynamic>::run(thisEval, visitor);
}