Lines Matching refs:eigenvalues
27 return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
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
73 /** \brief Computes the eigenvalues of a matrix
74 * \returns Column vector containing the eigenvalues.
77 * This function computes the eigenvalues with the help of the
78 * SelfAdjointEigenSolver class. The eigenvalues are repeated according to
79 * their algebraic multiplicity, so there are as many eigenvalues as rows in
85 * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
89 SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
93 return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
110 * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
111 * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
118 * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
126 // FIXME if it is really guaranteed that the eigenvalues are already sorted,
131 .eigenvalues()
143 * The current implementation uses the eigenvalues of the matrix, as computed
144 * by eigenvalues(), to compute the operator norm of the matrix.
149 * \sa eigenvalues(), MatrixBase::operatorNorm()
155 return eigenvalues().cwiseAbs().maxCoeff();
