In the examples so far, you met a couple of methods for computational tasks in linear algebra, for example, solve. More methods are available after the command import scipy.linalg as sl is executed. The most common of them are listed in Table 4.6:
| Methods | Description |
| sl.det | Determinant of a matrix |
| sl.eig | Eigenvalues and eigenvectors of a matrix |
| sl.inv | Matrix inverse |
| sl.pinv | Matrix pseudoinverse |
| sl.norm | Matrix or vector norm |
| sl.svd | Singular value decomposition |
| sl.lu | LUÂ decomposition |
| sl.qr | QRÂ decomposition |
| sl.cholesky | Cholesky decomposition |
| sl.solve | Solution of a general or symmetric linear system: Ax = b |
| sl.solve.banded | The same for banded matrices |
| sl.lstsq | Least squares solution |
Table 4.6: Linear algebra functions of the module scipy.linalg
Execute import scipy.linalg as sl first.
