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- """
- ``numpy.linalg``
- ================
- The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
- low level implementations of standard linear algebra algorithms. Those
- libraries may be provided by NumPy itself using C versions of a subset of their
- reference implementations but, when possible, highly optimized libraries that
- take advantage of specialized processor functionality are preferred. Examples
- of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
- are multithreaded and processor dependent, environmental variables and external
- packages such as threadpoolctl may be needed to control the number of threads
- or specify the processor architecture.
- - OpenBLAS: https://www.openblas.net/
- - threadpoolctl: https://github.com/joblib/threadpoolctl
- Please note that the most-used linear algebra functions in NumPy are present in
- the main ``numpy`` namespace rather than in ``numpy.linalg``. There are:
- ``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
- ``einsum_path`` and ``kron``.
- Functions present in numpy.linalg are listed below.
- Matrix and vector products
- --------------------------
- multi_dot
- matrix_power
- Decompositions
- --------------
- cholesky
- qr
- svd
- Matrix eigenvalues
- ------------------
- eig
- eigh
- eigvals
- eigvalsh
- Norms and other numbers
- -----------------------
- norm
- cond
- det
- matrix_rank
- slogdet
- Solving equations and inverting matrices
- ----------------------------------------
- solve
- tensorsolve
- lstsq
- inv
- pinv
- tensorinv
- Exceptions
- ----------
- LinAlgError
- """
- # To get sub-modules
- from . import linalg
- from .linalg import *
- __all__ = linalg.__all__.copy()
- from numpy._pytesttester import PytestTester
- test = PytestTester(__name__)
- del PytestTester
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