Lines Matching refs:Matrix
27 * eigen-decomposition; this is expected to be an instantiation of the Matrix
30 * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars
32 * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
33 * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
34 * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
37 * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the
38 * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is
46 * a given matrix pair. Alternatively, you can use the
91 typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
98 typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
104 /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
106 * This is a square matrix with entries of type #ComplexScalar.
109 typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
143 /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair.
145 * \param[in] A Square matrix whose eigendecomposition is to be computed.
146 * \param[in] B Square matrix whose eigendecomposition is to be computed.
169 * \returns %Matrix whose columns are the (possibly complex) right eigenvectors.
197 * so there are as many eigenvalues as rows in the matrix. The eigenvalues
230 /** \brief Computes generalized eigendecomposition of given matrix.
232 * \param[in] A Square matrix whose eigendecomposition is to be computed.
233 * \param[in] B Square matrix whose eigendecomposition is to be computed.
239 * This function computes the eigenvalues of the real matrix \p matrix.
244 * The matrix is first reduced to real generalized Schur form using the RealQZ
343 Matrix<Scalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( v.segment(st,sz) );
344 Matrix<Scalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
372 Matrix<RealScalar,2,2> S2 = mS.template block<2,2>(i,i) * Matrix<Scalar,2,1>(b,a).asDiagonal();
394 Matrix<ComplexScalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( cv.segment(st,sz) );
395 Matrix
