This is documentation for an old release of NumPy (version 1.6.0). 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(a, b[, out]) |
Dot product of two arrays. |
| vdot(a, b) |
Return the dot product of two vectors. |
| inner(a, b) |
Inner product of two arrays. |
| outer(a, b) |
Compute the outer product of two vectors. |
| tensordot(a, b[, axes]) |
Compute tensor dot product along specified axes for arrays >= 1-D. |
| einsum(subscripts, *operands[, out, dtype, ...]) |
Evaluates the Einstein summation convention on the operands. |
| 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 the qr factorization of a matrix. |
| linalg.svd(a[, full_matrices, compute_uv]) |
Singular Value Decomposition. |
Matrix eigenvalues
| linalg.eig(a) |
Compute the eigenvalues and right eigenvectors of a square array. |
| linalg.eigh(a[, UPLO]) |
Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix. |
| linalg.eigvals(a) |
Compute the eigenvalues of a general matrix. |
| linalg.eigvalsh(a[, UPLO]) |
Compute the eigenvalues of a Hermitian 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. |
| 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
| 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]) |
Compute the (Moore-Penrose) pseudo-inverse of a matrix. |
| linalg.tensorinv(a[, ind]) |
Compute the ‘inverse’ of an N-dimensional array. |
