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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
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);
}