# # Linear algebra (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.

## # Matrix and vector products

method description
dot(a, b[, out]) Dot product of two arrays.
linalg.multi_dot(arrays) Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.
vdot(a, b) Return the dot product of two vectors.
inner(a, b) Inner product of two arrays.
outer(a, b[, out]) Compute the outer product of two vectors.
matmul(x1, x2, /[, out, casting, order, …]) Matrix product of two arrays.
tensordot(a, b[, axes]) Compute tensor dot product along specified axes.
einsum(subscripts, *operands[, out, dtype, …]) Evaluates the Einstein summation convention on the operands.
einsum_path(subscripts, *operands[, optimize]) Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.
linalg.matrix_power(a, n) Raise a square matrix to the (integer) power n.
kron(a, b) Kronecker product of two arrays.

## # Decompositions

method description
linalg.cholesky(a) Cholesky decomposition.
linalg.qr(a[, mode]) Compute the qr factorization of a matrix.
linalg.svd(a[, full_matrices, compute_uv, …]) Singular Value Decomposition.

## # Matrix eigenvalues

method description
linalg.eig(a) Compute the eigenvalues and right eigenvectors of a square array.
linalg.eigh(a[, UPLO]) Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.
linalg.eigvals(a) Compute the eigenvalues of a general matrix.
linalg.eigvalsh(a[, UPLO]) Compute the eigenvalues of a complex Hermitian or real symmetric matrix.

## # Norms and other numbers

method description
linalg.norm(x[, ord, axis, keepdims]) Matrix or vector norm.
linalg.cond(x[, p]) Compute the condition number of a matrix.
linalg.det(a) Compute the determinant of an array.
linalg.matrix_rank(M[, tol, hermitian]) Return matrix rank of array using SVD method
linalg.slogdet(a) Compute the sign and (natural) logarithm of the determinant of an array.
trace(a[, offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array.

## # Solving equations and inverting matrices

method description
linalg.solve(a, b) Solve a linear matrix equation, or system of linear scalar equations.
linalg.tensorsolve(a, b[, axes]) Solve the tensor equation a x = b for x.
linalg.lstsq(a, b[, rcond]) Return the least-squares solution to a linear matrix equation.
linalg.inv(a) Compute the (multiplicative) inverse of a matrix.
linalg.pinv(a[, rcond, hermitian]) Compute the (Moore-Penrose) pseudo-inverse of a matrix.
linalg.tensorinv(a[, ind]) Compute the ‘inverse’ of an N-dimensional array.

## # Exceptions

method description
linalg.LinAlgError Generic Python-exception-derived object raised by linalg functions.

## # Linear algebra on several matrices at once

New in version 1.8.0.

Several of the linear algebra routines listed above are able to compute results for several matrices at once, if they are stacked into the same array.

This is indicated in the documentation via input parameter specifications such as a : (..., M, M) array_like. This means that if for instance given an input array a.shape == (N, M, M), it is interpreted as a “stack” of N matrices, each of size M-by-M. Similar specification applies to return values, for instance the determinant has det : (...) and will in this case return an array of shape det(a).shape == (N,). This generalizes to linear algebra operations on higher-dimensional arrays: the last 1 or 2 dimensions of a multidimensional array are interpreted as vectors or matrices, as appropriate for each operation.

Last Updated: 9/22/2019, 7:44:42 PM