1"""

2``numpy.linalg``

3================

4

5 The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient

6 low level implementations of standard linear algebra algorithms. Those

7 libraries may be provided by NumPy itself using C versions of a subset of their

8 reference implementations but, when possible, highly optimized libraries that

9 take advantage of specialized processor functionality are preferred. Examples

10 of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries

11 are multithreaded and processor dependent, environmental variables and external

12 packages such as threadpoolctl may be needed to control the number of threads

13 or specify the processor architecture.

14

15- OpenBLAS: https://www.openblas.net/

16- threadpoolctl: https://github.com/joblib/threadpoolctl

17

18 Please note that the most-used linear algebra functions in NumPy are present in

19 the main ``numpy`` namespace rather than in ``numpy.linalg``. There are:

20``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,

21``einsum_path`` and ``kron``.

22

23 Functions present in numpy.linalg are listed below.

24

25

26 Matrix and vector products

27--------------------------

28

29 multi_dot

30 matrix_power

31

32 Decompositions

33--------------

34

35 cholesky

36 qr

37 svd

38

39 Matrix eigenvalues

40------------------

41

42 eig

43 eigh

44 eigvals

45 eigvalsh

46

47 Norms and other numbers

48-----------------------

49

50 norm

51 cond

52 det

53 matrix_rank

54 slogdet

55

56 Solving equations and inverting matrices

57----------------------------------------

58

59 solve

60 tensorsolve

61 lstsq

62 inv

63 pinv

64 tensorinv

65

66 Exceptions

67----------

68

69 LinAlgError

70

71"""

72# To get sub-modules

73 from . import linalg

74 from .linalg import *

75

76__all__ = linalg.__all__.copy()

77

78 from numpy._pytesttester import PytestTester

79 test = PytestTester(__name__)

80 del PytestTester