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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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