| Chapter Introduction | |
| Module 6.1: nag_sym_eig - Standard Symmetric Eigenvalue Problems | |
| nag_sym_eig_all | All eigenvalues, and optionally eigenvectors, of a real symmetric or complex Hermitian matrix |
| nag_sym_eig_sel | Selected eigenvalues, and optionally the corresponding eigenvectors, of a real symmetric or complex Hermitian matrix |
| nag_sym_tridiag_reduc | Reduction of a real symmetric or complex Hermitian matrix to real symmetric tridiagonal form |
| nag_sym_tridiag_orth | Form or apply the transformation matrix determined by nag_sym_tridiag_reduc |
| nag_sym_tridiag_eig_all | All eigenvalues, and optionally eigenvectors, of a real symmetric tridiagonal matrix |
| nag_sym_tridiag_eig_val | Selected eigenvalues of a real symmetric tridiagonal matrix |
| nag_sym_tridiag_eig_vec | Selected eigenvectors of a real symmetric tridiagonal matrix |
| Examples | |
| Module 6.2: nag_nsym_eig - Standard Nonsymmetric Eigenvalue Problems | |
| nag_nsym_eig_all | All eigenvalues, and optionally eigenvectors, of a general real or complex matrix |
| nag_schur_fac | Schur factorization of a general real or complex matrix |
| Examples | |
| Module 6.3: nag_svd - Singular Value Decomposition (SVD) | |
| nag_gen_svd | Singular value decomposition of a general real or complex matrix |
| nag_gen_bidiag_reduc | Reduction of a general real or complex matrix to real bidiagonal form |
| nag_bidiag_svd | Singular value decomposition of a real bidiagonal matrix |
| Examples | |
| Module 6.4: nag_lin_lsq - Linear Least-squares problems | |
| nag_lin_lsq_sol | Solves a real or complex linear least-squares problem |
| nag_lin_lsq_sol_svd | Solves a real or complex linear least-squares problem, assuming that a singular value decomposition of the coefficient matrix has already been computed |
| nag_qr_fac | QR factorization of a general real or complex matrix |
| nag_qr_orth | Form or apply the matrix determined by nag_qr_fac |
| nag_lin_lsq_sol_qr | Solves a real or complex linear least-squares problem, assuming that the factorization of the coefficient matrix has already been computed |
| nag_lin_lsq_sol_qr_svd | Solves a real or complex linear least-squares problem using the SVD, assuming that the QR factorization of the coefficient matrix has already been computed |
| Examples | |
| Module 6.5: nag_sym_gen_eig - Symmetric-definite Generalized Eigenvalue Problems | |
| nag_sym_gen_eig_all | All eigenvalues, and optionally eigenvectors, of a real symmetric-definite or complex Hermitian-definite generalized eigenvalue problem |
| nag_sym_gen_eig_sel | Selected eigenvalues, and optionally the corresponding eigenvectors, of a real symmetric-definite or complex Hermitian-definite generalized eigenvalue problem |
| Examples | |
| Module 6.6: nag_nsym_gen_eig - Nonsymmetric Generalized Eigenvalue Problems | |
| nag_nsym_gen_eig_all | All eigenvalues, and optionally eigenvectors, of a real or complex nonsymmetric generalized eigenvalue problem |
| nag_gen_schur_fac | Generalized Schur factorization of a real or complex matrix pencil |
| Examples | |