Shortcuts on this page
r m x toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1"""
2``numpy.linalg``
3================
5The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
6low level implementations of standard linear algebra algorithms. Those
7libraries may be provided by NumPy itself using C versions of a subset of their
8reference implementations but, when possible, highly optimized libraries that
9take advantage of specialized processor functionality are preferred. Examples
10of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
11are multithreaded and processor dependent, environmental variables and external
12packages such as threadpoolctl may be needed to control the number of threads
13or specify the processor architecture.
15- OpenBLAS: https://www.openblas.net/
16- threadpoolctl: https://github.com/joblib/threadpoolctl
18Please note that the most-used linear algebra functions in NumPy are present in
19the main ``numpy`` namespace rather than in ``numpy.linalg``. There are:
20``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
21``einsum_path`` and ``kron``.
23Functions present in numpy.linalg are listed below.
26Matrix and vector products
27--------------------------
29 multi_dot
30 matrix_power
32Decompositions
33--------------
35 cholesky
36 qr
37 svd
39Matrix eigenvalues
40------------------
42 eig
43 eigh
44 eigvals
45 eigvalsh
47Norms and other numbers
48-----------------------
50 norm
51 cond
52 det
53 matrix_rank
54 slogdet
56Solving equations and inverting matrices
57----------------------------------------
59 solve
60 tensorsolve
61 lstsq
62 inv
63 pinv
64 tensorinv
66Exceptions
67----------
69 LinAlgError
71"""
72# To get sub-modules
73from . import linalg
74from .linalg import *
76__all__ = linalg.__all__.copy()
78from numpy._pytesttester import PytestTester
79test = PytestTester(__name__)
80del PytestTester