Lines Matching defs:in
16 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
66 * The default constructor is for cases in which the user intends to
81 * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
84 * \param[in] B Self-adjoint matrix for the generalized eigenvalue problem.
85 * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
87 * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
90 * value can contain floating point value in string form, in which case the
92 * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
93 * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
116 * \param[in] A Self-adjoint matrix whose eigenvalues / eigenvectors will
119 * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
121 * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
124 * value can contain floating point value in string form, in which case the
126 * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
127 * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
152 * \param[in] A Selfadjoint matrix whose eigendecomposition is to be computed.
153 * \param[in] B Selfadjoint matrix for generalized eigenvalues.
154 * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
156 * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
159 * value can contain floating point value in string form, in which case the
161 * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
162 * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
179 * \param[in] A Selfadjoint matrix whose eigendecomposition is to be computed.
180 * \param[in] nbrEigenvalues The number of eigenvalues / eigenvectors to compute.
182 * \param[in] eigs_sigma String containing either "LM", "SM", "LA", or "SA", with
185 * value can contain floating point value in string form, in which case the
187 * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
188 * \param[in] tol What tolerance to find the eigenvalues to. Default is 0, which
215 * matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
237 * so there are as many eigenvalues as rows in the matrix. The eigenvalues
238 * are sorted in increasing order.
381 // In the following special case we're going to invert the problem, since solving
455 // Error codes are returned in here, initial value of 0 indicates a random initial
515 Scalar *in = workd + ipntr[0] - 1;
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);
526 in = workd + ipntr[2] - 1;
535 Matrix<Scalar, Dynamic, 1>::Map(out, n) = A * Matrix<Scalar, Dynamic, 1>::Map(in, n);
541 internal::OP<MatrixSolver, MatrixType, Scalar, BisSPD>::applyOP(OP, A, n, in, out);
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));
556 // The B * in is already computed and stored at in if ido == 1
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));
566 Scalar *in = workd + ipntr[0] - 1;
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);
590 // "A" means "All", use "S" to choose specific eigenvalues (not yet supported in ARPACK))
594 // if howmny == "S", specifies the eigenvalues to compute (not implemented in ARPACK)
749 static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out);
756 static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
760 // First solve L^T out = in
762 Matrix<Scalar, Dynamic, 1>::Map(out, n) = OP.matrixU().solve(Matrix<Scalar, Dynamic, 1>::Map(in, n));
777 // Solve L^T out = in
788 static inline void applyOP(MatrixSolver &OP, const MatrixType &A, int n, Scalar *in, Scalar *out)
790 eigen_assert(false && "Should never be in here...");
795 eigen_assert(false && "Should never be in here...");
