def main(): model = create_model() nlp = PyomoNLP(model) # initial guesses x = nlp.init_primals() lam = nlp.init_duals() nlp.set_primals(x) nlp.set_duals(lam) # NLP function evaluations f = nlp.evaluate_objective() print("Objective Function\n", f) df = nlp.evaluate_grad_objective() print("Gradient of Objective Function:\n", df) c = nlp.evaluate_constraints() print("Constraint Values:\n", c) c_eq = nlp.evaluate_eq_constraints() print("Equality Constraint Values:\n", c_eq) c_ineq = nlp.evaluate_ineq_constraints() print("Inequality Constraint Values:\n", c_ineq) jac = nlp.evaluate_jacobian() print("Jacobian of Constraints:\n", jac.toarray()) jac_eq = nlp.evaluate_jacobian_eq() print("Jacobian of Equality Constraints:\n", jac_eq.toarray()) jac_ineq = nlp.evaluate_jacobian_ineq() print("Jacobian of Inequality Constraints:\n", jac_ineq.toarray()) hess_lag = nlp.evaluate_hessian_lag() print("Hessian of Lagrangian\n", hess_lag.toarray())
def main(show_plot=True): if show_plot: import matplotlib.pylab as plt instance = create_problem(0.0, 10.0) # Discretize model using Orthogonal Collocation discretizer = pyo.TransformationFactory('dae.collocation') discretizer.apply_to(instance, nfe=100, ncp=3, scheme='LAGRANGE-RADAU') discretizer.reduce_collocation_points(instance, var=instance.u, ncp=1, contset=instance.t) # Interface pyomo model with nlp nlp = PyomoNLP(instance) x = nlp.create_new_vector('primals') x.fill(1.0) nlp.set_primals(x) lam = nlp.create_new_vector('duals') lam.fill(1.0) nlp.set_duals(lam) # Evaluate jacobian jac = nlp.evaluate_jacobian() if show_plot: plt.spy(jac) plt.title('Jacobian of the constraints\n') plt.show() # Evaluate hessian of the lagrangian hess_lag = nlp.evaluate_hessian_lag() if show_plot: plt.spy(hess_lag) plt.title('Hessian of the Lagrangian function\n') plt.show() # Build KKT matrix kkt = BlockMatrix(2, 2) kkt.set_block(0, 0, hess_lag) kkt.set_block(1, 0, jac) kkt.set_block(0, 1, jac.transpose()) if show_plot: plt.spy(kkt.tocoo()) plt.title('KKT system\n') plt.show()
def test_compare_evaluations(self): A1 = 5 A2 = 10 c1 = 3 c2 = 4 N = 6 dt = 1 m = create_pyomo_model(A1, A2, c1, c2, N, dt) solver = pyo.SolverFactory('ipopt') solver.options['linear_solver'] = 'mumps' status = solver.solve(m, tee=False) m_nlp = PyomoNLP(m) mex = create_pyomo_external_grey_box_model(A1, A2, c1, c2, N, dt) # mex_nlp = PyomoGreyBoxNLP(mex) mex_nlp = PyomoNLPWithGreyBoxBlocks(mex) # get the variable and constraint order and create the maps # reliable order independent comparisons m_x_order = m_nlp.primals_names() m_c_order = m_nlp.constraint_names() mex_x_order = mex_nlp.primals_names() mex_c_order = mex_nlp.constraint_names() x1list = [ 'h1[0]', 'h1[1]', 'h1[2]', 'h1[3]', 'h1[4]', 'h1[5]', 'h2[0]', 'h2[1]', 'h2[2]', 'h2[3]', 'h2[4]', 'h2[5]', 'F1[1]', 'F1[2]', 'F1[3]', 'F1[4]', 'F1[5]', 'F2[1]', 'F2[2]', 'F2[3]', 'F2[4]', 'F2[5]', 'F12[0]', 'F12[1]', 'F12[2]', 'F12[3]', 'F12[4]', 'F12[5]', 'Fo[0]', 'Fo[1]', 'Fo[2]', 'Fo[3]', 'Fo[4]', 'Fo[5]' ] x2list = [ 'egb.inputs[h1_0]', 'egb.inputs[h1_1]', 'egb.inputs[h1_2]', 'egb.inputs[h1_3]', 'egb.inputs[h1_4]', 'egb.inputs[h1_5]', 'egb.inputs[h2_0]', 'egb.inputs[h2_1]', 'egb.inputs[h2_2]', 'egb.inputs[h2_3]', 'egb.inputs[h2_4]', 'egb.inputs[h2_5]', 'egb.inputs[F1_1]', 'egb.inputs[F1_2]', 'egb.inputs[F1_3]', 'egb.inputs[F1_4]', 'egb.inputs[F1_5]', 'egb.inputs[F2_1]', 'egb.inputs[F2_2]', 'egb.inputs[F2_3]', 'egb.inputs[F2_4]', 'egb.inputs[F2_5]', 'egb.outputs[F12_0]', 'egb.outputs[F12_1]', 'egb.outputs[F12_2]', 'egb.outputs[F12_3]', 'egb.outputs[F12_4]', 'egb.outputs[F12_5]', 'egb.outputs[Fo_0]', 'egb.outputs[Fo_1]', 'egb.outputs[Fo_2]', 'egb.outputs[Fo_3]', 'egb.outputs[Fo_4]', 'egb.outputs[Fo_5]' ] x1_x2_map = dict(zip(x1list, x2list)) x1idx_x2idx_map = { i: mex_x_order.index(x1_x2_map[m_x_order[i]]) for i in range(len(m_x_order)) } c1list = [ 'h1bal[1]', 'h1bal[2]', 'h1bal[3]', 'h1bal[4]', 'h1bal[5]', 'h2bal[1]', 'h2bal[2]', 'h2bal[3]', 'h2bal[4]', 'h2bal[5]', 'F12con[0]', 'F12con[1]', 'F12con[2]', 'F12con[3]', 'F12con[4]', 'F12con[5]', 'Focon[0]', 'Focon[1]', 'Focon[2]', 'Focon[3]', 'Focon[4]', 'Focon[5]', 'min_inflow[1]', 'min_inflow[2]', 'min_inflow[3]', 'min_inflow[4]', 'min_inflow[5]', 'max_outflow[0]', 'max_outflow[1]', 'max_outflow[2]', 'max_outflow[3]', 'max_outflow[4]', 'max_outflow[5]', 'h10', 'h20' ] c2list = [ 'egb.h1bal_1', 'egb.h1bal_2', 'egb.h1bal_3', 'egb.h1bal_4', 'egb.h1bal_5', 'egb.h2bal_1', 'egb.h2bal_2', 'egb.h2bal_3', 'egb.h2bal_4', 'egb.h2bal_5', 'egb.output_constraints[F12_0]', 'egb.output_constraints[F12_1]', 'egb.output_constraints[F12_2]', 'egb.output_constraints[F12_3]', 'egb.output_constraints[F12_4]', 'egb.output_constraints[F12_5]', 'egb.output_constraints[Fo_0]', 'egb.output_constraints[Fo_1]', 'egb.output_constraints[Fo_2]', 'egb.output_constraints[Fo_3]', 'egb.output_constraints[Fo_4]', 'egb.output_constraints[Fo_5]', 'min_inflow[1]', 'min_inflow[2]', 'min_inflow[3]', 'min_inflow[4]', 'min_inflow[5]', 'max_outflow[0]', 'max_outflow[1]', 'max_outflow[2]', 'max_outflow[3]', 'max_outflow[4]', 'max_outflow[5]', 'h10', 'h20' ] c1_c2_map = dict(zip(c1list, c2list)) c1idx_c2idx_map = { i: mex_c_order.index(c1_c2_map[m_c_order[i]]) for i in range(len(m_c_order)) } # get the primals from m and put them in the correct order for mex m_x = m_nlp.get_primals() mex_x = np.zeros(len(m_x)) for i in range(len(m_x)): mex_x[x1idx_x2idx_map[i]] = m_x[i] # get the duals from m and put them in the correct order for mex m_lam = m_nlp.get_duals() mex_lam = np.zeros(len(m_lam)) for i in range(len(m_x)): mex_lam[c1idx_c2idx_map[i]] = m_lam[i] mex_nlp.set_primals(mex_x) mex_nlp.set_duals(mex_lam) m_obj = m_nlp.evaluate_objective() mex_obj = mex_nlp.evaluate_objective() self.assertAlmostEqual(m_obj, mex_obj, places=4) m_gobj = m_nlp.evaluate_grad_objective() mex_gobj = mex_nlp.evaluate_grad_objective() check_vectors_specific_order(self, m_gobj, m_x_order, mex_gobj, mex_x_order, x1_x2_map) m_c = m_nlp.evaluate_constraints() mex_c = mex_nlp.evaluate_constraints() check_vectors_specific_order(self, m_c, m_c_order, mex_c, mex_c_order, c1_c2_map) m_j = m_nlp.evaluate_jacobian() mex_j = mex_nlp.evaluate_jacobian().todense() check_sparse_matrix_specific_order(self, m_j, m_c_order, m_x_order, mex_j, mex_c_order, mex_x_order, c1_c2_map, x1_x2_map) m_h = m_nlp.evaluate_hessian_lag() mex_h = mex_nlp.evaluate_hessian_lag() check_sparse_matrix_specific_order(self, m_h, m_x_order, m_x_order, mex_h, mex_x_order, mex_x_order, x1_x2_map, x1_x2_map) mex_h = 0 * mex_h mex_nlp.evaluate_hessian_lag(out=mex_h) check_sparse_matrix_specific_order(self, m_h, m_x_order, m_x_order, mex_h, mex_x_order, mex_x_order, x1_x2_map, x1_x2_map)
################################################################# m = create_model(4.5, 1.0) opt = pyo.SolverFactory('ipopt') results = opt.solve(m, tee=True) ################################################################# nlp = PyomoNLP(m) x = nlp.init_primals() y = compute_init_lam(nlp, x=x) nlp.set_primals(x) nlp.set_duals(y) J = nlp.evaluate_jacobian() H = nlp.evaluate_hessian_lag() M = BlockSymMatrix(2) M[0, 0] = H M[1, 0] = J Np = BlockMatrix(2, 1) Np[0, 0] = nlp.extract_submatrix_hessian_lag( pyomo_variables_rows=nlp.get_pyomo_variables(), pyomo_variables_cols=[m.eta1, m.eta2]) Np[1, 0] = nlp.extract_submatrix_jacobian( pyomo_variables=[m.eta1, m.eta2], pyomo_constraints=nlp.get_pyomo_constraints()) ds = spsolve(M.tocsc(), Np.tocsc()) print(nlp.variable_names())