To remove in the future –– numpy.linalg

``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

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.linalg._decomp_svd.svdvals scipy.linalg._matfuncs.tanhm numpy.linalg.eigvalsh numpy.linalg.cholesky numpy.linalg.eig numpy.linalg.det numpy.linalg.eigh scipy.linalg._matfuncs.coshm numpy.linalg.slogdet scipy.spatial._qhull.HalfspaceIntersection scipy.linalg._matfuncs.sinhm numpy.linalg.eigvals scipy.sparse.linalg._onenormest.onenormest scipy.linalg._matfuncs.tanm dask.array.linalg.norm numpy.linalg.inv numpy.linalg.solve scipy.signal._ltisys.place_poles

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /numpy/linalg/__init__.py#0
type: <class 'module'>
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