Lines Matching refs:Matrix
79 /** \brief Constructor; computes generalized eigenvalues of given matrix with respect to another matrix.
81 * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
84 * \param[in] B Self-adjoint matrix for the generalized eigenvalue problem.
86 * Must be less than the size of the input matrix, or an error is returned.
97 * to compute the eigenvalues of the matrix \p A with respect to \p B. The eigenvectors are computed if
114 /** \brief Constructor; computes eigenvalues of given matrix.
116 * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
120 * Must be less than the size of the input matrix, or an error is returned.
131 * to compute the eigenvalues of the matrix \p A. The eigenvectors are computed if
150 /** \brief Computes generalized eigenvalues / eigenvectors of given matrix using the external ARPACK library.
152 * \param[in] A Selfadjoint matrix whose eigendecomposition is to be computed.
153 * \param[in] B Selfadjoint matrix for generalized eigenvalues.
155 * Must be less than the size of the input matrix, or an error is returned.
177 /** \brief Computes eigenvalues / eigenvectors of given matrix using the external ARPACK library.
179 * \param[in] A Selfadjoint matrix whose eigendecomposition is to be computed.
181 * Must be less than the size of the input matrix, or an error is returned.
204 /** \brief Returns the eigenvectors of given matrix.
206 * \returns A const reference to the matrix whose columns are the eigenvectors.
210 * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
214 * matrix \f$ A \f$, then the matrix returned by this function is the
215 * matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
216 * For the generalized eigenproblem, the matrix returned is the solution \f$ A V = D B V \f$
223 const Matrix<Scalar, Dynamic, Dynamic>& eigenvectors() const
230 /** \brief Returns the eigenvalues of given matrix.
237 * so there are as many eigenvalues as rows in the matrix. The eigenvalues
245 const Matrix<Scalar, Dynamic, 1>& eigenvalues() const
251 /** \brief Computes the positive-definite square root of the matrix.
253 * \returns the positive-definite square root of the matrix
255 * \pre The eigenvalues and eigenvectors of a positive-definite matrix
258 * The square root of a positive-definite matrix \f$ A \f$ is the
259 * positive-definite matrix whose square equals \f$ A \f$. This function
269 Matrix<Scalar, Dynamic, Dynamic> operatorSqrt() const
276 /** \brief Computes the inverse square root of the matrix.
278 * \returns the inverse positive-definite square root of the matrix
280 * \pre The eigenvalues and eigenvectors of a positive-definite matrix
294 Matrix<Scalar, Dynamic, Dynamic> operatorInverseSqrt() const
318 Matrix<Scalar, Dynamic, Dynamic> m_eivec;
319 Matrix<Scalar, Dynamic, 1> m_eivalues;
435 // The working n x ncv matrix, also store the final eigenvectors (if computed)
505 std::cout << "Error factoring matrix" << std::endl;
522 Matrix<Scalar, Dynamic, 1>::Map(out2, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
524 Matrix<Scalar, Dynamic, 1>::Map(out2, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
535 Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
547 Matrix<Scalar, Dynamic, 1>::Map(in, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
551 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
559 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
561 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.solve(B * Matrix<Scalar, Dynamic, 1>::Map(in, n));
570 Matrix<Scalar, Dynamic, 1>::Map(out, n) = Matrix<Scalar, Dynamic, 1>::Map(in, n);
572 Matrix<Scalar, Dynamic, 1>::Map(out, n) = B * Matrix<Scalar, Dynamic, 1>::Map(in, n);
762 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
763 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationPinv() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
767 Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(out, n);
771 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.permutationP() * Matrix<Scalar, Dynamic, 1>::Map(out, n);
772 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixL().solve(Matrix<Scalar, Dynamic, 1>::Map(out, n));
779 Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.matrixU().solve(Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k));
780 Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k) = OP.permutationPinv() * Matrix<Scalar, Dynamic, Dynamic>::Map(vecs, n, k);
