mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-12-27 07:29:52 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix compilation (cwise and epsilon)

Browse Source
This commit is contained in:
Gael Guennebaud 2010-02-11 09:24:29 +01:00
parent 602778ea26
commit 48f5980669
2 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Eigen/src/Geometry/AlignedBox.h
View File

@ -100,7 +100,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
inline void setNull() { setEmpty(); } inline void setNull() { setEmpty(); }
/** \returns true if the box is empty. */ /** \returns true if the box is empty. */
inline bool isEmpty() const { return (m_min.cwise() > m_max).any(); } inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); }
/** Makes \c *this an empty box. */ /** Makes \c *this an empty box. */
inline void setEmpty() inline void setEmpty()

4
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
View File

@ -121,7 +121,7 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
const int n = F.rows(); const int n = F.rows();
const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff(); const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff(); const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
if (Fincr_norm < epsilon<Scalar>() * F_norm) { if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
RealScalar delta = 0; RealScalar delta = 0;
RealScalar rfactorial = 1; RealScalar rfactorial = 1;
for (int r = 0; r < n; r++) { for (int r = 0; r < n; r++) {
@ -133,7 +133,7 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
delta = std::max(delta, mx / rfactorial); delta = std::max(delta, mx / rfactorial);
} }
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff(); const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm) if (m_mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)
return true; return true;
} }
return false; return false;