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

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