docs/python_docs/python/api/np/routines.linalg.rst
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.. _routines.linalg:
.. module:: mxnet.np.linalg
Linear algebra (:mod: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
.. currentmodule:: mxnet.np
.. autosummary:: :toctree: generated/
dot vdot inner outer tensordot einsum linalg.multi_dot matmul linalg.matrix_power kron
.. autosummary:: :toctree: generated/
linalg.svd linalg.cholesky linalg.qr
.. autosummary:: :toctree: generated/
linalg.eig linalg.eigh linalg.eigvals linalg.eigvalsh
.. autosummary:: :toctree: generated/
linalg.norm trace linalg.cond linalg.det linalg.matrix_rank linalg.slogdet
.. autosummary:: :toctree: generated/
linalg.solve linalg.tensorsolve linalg.lstsq linalg.inv linalg.pinv linalg.tensorinv