This is documentation for an old release of NumPy (version 1.3.). Read this page Search for this page in the documentation of the latest stable release (version > 1.17).
Linear algebra (numpy.linalg)
Matrix and vector products
dot () |
|
vdot (a, b) |
Return the dot product of two vectors. |
inner (a, b) |
Inner product of two arrays. |
outer (a, b) |
Returns the outer product of two vectors. |
tensordot (a, b[, axes]) |
Returns the tensor dot product for (ndim >= 1) arrays along an axes. |
linalg.matrix_power (M, n) |
Raise a square matrix to the (integer) power n. |
kron (a, b) |
Kronecker product of two arrays. |
Decompositions
linalg.cholesky (a) |
Cholesky decomposition. |
linalg.qr (a[, mode]) |
Compute QR decomposition of a matrix. |
linalg.svd (a[, full_matrices, compute_uv]) |
Singular Value Decomposition. |
Matrix eigenvalues
linalg.eig (a) |
Compute eigenvalues and right eigenvectors of an array. |
linalg.eigh (a[, UPLO]) |
Eigenvalues and eigenvectors of a Hermitian or real symmetric matrix. |
linalg.eigvals (a) |
Compute the eigenvalues of a general matrix. |
linalg.eigvalsh (a[, UPLO]) |
Compute the eigenvalues of a Hermitean or real symmetric matrix. |
Norms and other numbers
linalg.norm (x[, ord]) |
Matrix or vector norm. |
linalg.cond (x[, p]) |
Compute the condition number of a matrix. |
linalg.det (a) |
Compute the determinant of an array. |
trace (a[, offset, axis1, axis2, ...]) |
Return the sum along diagonals of the array. |
Solving equations and inverting matrices
linalg.solve (a, b) |
Solve the equation a x = b for x. |
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 an equation. |
linalg.inv (a) |
Compute the inverse of a matrix. |
linalg.pinv (a[, rcond]) |
Compute the (Moore-Penrose) pseudo-inverse of a matrix. |
linalg.tensorinv (a[, ind]) |
Find the ‘inverse’ of a N-d array |