Lines Matching full:eigenvalues

27     return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();  in run()
39     return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
45 /** \brief Computes the eigenvalues of a matrix 
46   * \returns Column vector containing the eigenvalues.
49   * This function computes the eigenvalues with the help of the EigenSolver
53   * The eigenvalues are repeated according to their algebraic multiplicity,
54   * so there are as many eigenvalues as rows in the matrix.
62   * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
63   *     SelfAdjointView::eigenvalues()
67 MatrixBase<Derived>::eigenvalues() const
72 /** \brief Computes the eigenvalues of a matrix
73   * \returns Column vector containing the eigenvalues.
76   * This function computes the eigenvalues with the help of the
77   * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
78   * their algebraic multiplicity, so there are as many eigenvalues as rows in
84   * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
88 SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
91   return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
108   * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
109   * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
116   * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
124   // FIXME if it is really guaranteed that the eigenvalues are already sorted,
129 		 .eigenvalues()
141   * The current implementation uses the eigenvalues of the matrix, as computed
142   * by eigenvalues(), to compute the operator norm of the matrix.
147   * \sa eigenvalues(), MatrixBase::operatorNorm()
153   return eigenvalues().cwiseAbs().maxCoeff();