ToolPythonIntermediate Chebyshev collocation for eigenvalue problems: a neutron on a mirror print("eigh:", np.round(eigh(H, eigvals_only=True)[:3], 2), " exact:", np.round(a[:3], 3))
ConceptPythonAdvanced The graph Laplacian: why its second eigenvector cuts a network in two w, V = eigh(L)ws, Vs = eigh(Lsym)wg, Vg = eigh(L, np.diag(d)) # the generalized problem L x = lambda D xshuffled.append(eigh(nx.normalized_laplacian_matrix(R).toarray())[0][1])w_nx, V_nx = eigh(L_nx.toarray())wW, VW = eigh(nx.laplacian_matrix(G).toarray()) # with Zachary's weights: L = D - W