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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 { // consta…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
templatetypename _MatrixType,int _UpLo
templatebool 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 Diagonalconst 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 LAPACKs xGELSS: // RealScalar tolerance numext::maxi(vecD.array().abs().maxCoeff() * NumTraitsRealScalar::epsilon(),RealScalar(1) / NumTraitsRealScalar::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, Lapacks xSYTRS routines use 0 for the tolerance. // Using numeric_limits::min() gives us more robustness to denormals. RealScalar tolerance (std::numeric_limitsRealScalar::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 conjugateIfConjugate().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
templatetypename Derived
templatetypename Visitor
EIGEN_DEVICE_FUNC
void DenseBaseDerived::visit(Visitor visitor) const
{if(size()0)return;typedef typename internal::visitor_evaluatorDerived ThisEvaluator;ThisEvaluator thisEval(derived());enum {unroll SizeAtCompileTime ! Dynamic SizeAtCompileTime * int(ThisEvaluator::CoeffReadCost) (SizeAtCompileTime-1) * int(internal::functor_traitsVisitor::Cost) EIGEN_UNROLLING_LIMIT};return internal::visitor_implVisitor, ThisEvaluator, unroll ? int(SizeAtCompileTime) : Dynamic::run(thisEval, visitor);
}
