numpy.dot(a, b, out=None) Dot product of two arrays. For 2-D arrays it is equivalent to matrix multiplication, and for 1-D

numpy.linalg.eig(a)

numpy.linalg.matrix_rank(M, tol=None)

numpy.linalg.qr(a, mode='reduced')

numpy.einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe') Evaluates the Einstein summation convention

numpy.kron(a, b)

numpy.linalg.svd(a, full_matrices=1, compute_uv=1)

numpy.linalg.inv(a)

numpy.inner(a, b) Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays (without complex conjugation)