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- add MatrixBase::eigenvalues() convenience method

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- add MatrixBase::matrixNorm(); in the non-selfadjoint case, we reduce to the
  selfadjoint case by using the "C*-identity" a.k.a.
	norm of x = sqrt(norm of x * x.adjoint())
This commit is contained in:
Benoit Jacob
2008-06-02 04:42:45 +00:00
parent 92b7e2d6a1
commit 0444e3601a
3 changed files with 53 additions and 3 deletions
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48
Eigen/src/QR/SelfAdjointEigenSolver.h
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@@ -31,6 +31,8 @@
*
* \param MatrixType the type of the matrix of which we are computing the eigen decomposition
*
* \note MatrixType must be an actual Matrix type, it can't be an expression type.
*
* \sa MatrixBase::eigenvalues(), class EigenSolver
*/
template<typename _MatrixType> class SelfAdjointEigenSolver
@@ -58,7 +60,6 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
RealVectorType eigenvalues(void) const { return m_eivalues; }
protected:
MatrixType m_eivec;
RealVectorType m_eivalues;
@@ -169,4 +170,49 @@ void SelfAdjointEigenSolver<MatrixType>::compute(const MatrixType& matrix)
std::cout << "ei values = " << m_eivalues.transpose() << "\n\n";
}
template<typename Derived>
inline Matrix<typename NumTraits<typename ei_traits<Derived>::Scalar>::Real, ei_traits<Derived>::ColsAtCompileTime, 1>
MatrixBase<Derived>::eigenvalues() const
{
ei_assert(Flags&SelfAdjointBit);
return SelfAdjointEigenSolver<typename Derived::Eval>(eval()).eigenvalues();
}
template<typename Derived, bool IsSelfAdjoint>
struct ei_matrixNorm_selector
{
static inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real
matrixNorm(const MatrixBase<Derived>& m)
{
// FIXME if it is really guaranteed that the eigenvalues are already sorted,
// then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
return m.eigenvalues().cwiseAbs().maxCoeff();
}
};
template<typename Derived> struct ei_matrixNorm_selector<Derived, false>
{
static inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real
matrixNorm(const MatrixBase<Derived>& m)
{
// FIXME if it is really guaranteed that the eigenvalues are already sorted,
// then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
return ei_sqrt(
(m*m.adjoint())
.template marked<SelfAdjoint>()
.eigenvalues()
.cwiseAbs()
.maxCoeff()
);
}
};
template<typename Derived>
inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real
MatrixBase<Derived>::matrixNorm() const
{
return ei_matrixNorm_selector<Derived, Flags&SelfAdjointBit>
::matrixNorm(derived());
}
#endif // EIGEN_SELFADJOINTEIGENSOLVER_H