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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 -------------------------- cross multi_dot matrix_power tensordot matmul Decompositions -------------- cholesky outer qr svd svdvals Matrix eigenvalues ------------------ eig eigh eigvals eigvalsh Norms and other numbers ----------------------- norm matrix_norm vector_norm cond det matrix_rank slogdet trace (Array API compatible) Solving equations and inverting matrices ---------------------------------------- solve tensorsolve lstsq inv pinv tensorinv Other matrix operations ----------------------- diagonal (Array API compatible) matrix_transpose (Array API compatible) Exceptions ---------- LinAlgError """ # To get sub-modules from . import _linalg from ._linalg import * __all__ = _linalg.__all__.copy() # noqa: PLE0605 from numpy._pytesttester import PytestTester test = PytestTester(__name__) del PytestTester