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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 .linalg import * 

 

from numpy._pytesttester import PytestTester 

test = PytestTester(__name__) 

del PytestTester