devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

port unsupported modules to new API

Browse Source
This commit is contained in:
Gael Guennebaud
2010-01-05 15:38:20 +01:00
parent cab85218db
commit 39209edd71
9 changed files with 189 additions and 189 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
View File

@@ -25,7 +25,7 @@
#ifndef EIGEN_MATRIX_FUNCTION_ATOMIC
#define EIGEN_MATRIX_FUNCTION_ATOMIC
/** \ingroup MatrixFunctions_Module
/** \ingroup MatrixFunctions_Module
* \class MatrixFunctionAtomic
* \brief Helper class for computing matrix functions of atomic matrices.
*
@@ -110,30 +110,30 @@ void MatrixFunctionAtomic<MatrixType>::computeMu()
const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
VectorType e = VectorType::Ones(m_Arows);
N.template triangularView<UpperTriangular>().solveInPlace(e);
m_mu = e.cwise().abs().maxCoeff();
m_mu = e.cwiseAbs().maxCoeff();
}
/** \brief Determine whether Taylor series has converged */
template <typename MatrixType>
bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F,
bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F,
const MatrixType& Fincr, const MatrixType& P)
{
const int n = F.rows();
const RealScalar F_norm = F.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwise().abs().rowwise().sum().maxCoeff();
const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
if (Fincr_norm < epsilon<Scalar>() * F_norm) {
RealScalar delta = 0;
RealScalar rfactorial = 1;
for (int r = 0; r < n; r++) {
RealScalar mx = 0;
for (int i = 0; i < n; i++)
for (int i = 0; i < n; i++)
mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
if (r != 0)
rfactorial *= r;
delta = std::max(delta, mx / rfactorial);
}
const RealScalar P_norm = P.cwise().abs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < epsilon<Scalar>() * F_norm)
return true;
}
return false;