def test_composite_nlp(self):
G = np.array([[6, 2, 1], [2, 5, 2], [1, 2, 4]])
A = np.array([[1, 0, 1], [0, 1, 1]])
b = np.array([3, 0])
c = np.array([-8, -3, -3])
scenarios = dict()
coupling_vars = dict()
n_scenarios = 2
np.random.seed(seed=985739465)
bs = [b, b + 0.001]
for i in range(n_scenarios):
instance = create_model3(G, A, bs[i], c)
nlp = PyomoNLP(instance)
scenario_name = "s{}".format(i)
scenarios[scenario_name] = nlp
coupling_vars[scenario_name] = [nlp.variable_idx(instance.x[0])]
nlp = TwoStageStochasticNLP(scenarios, coupling_vars)
solver = CyIpoptSolver(nlp)
x, info = solver.solve(tee=False)
x_sol = np.array([
2.00003846, -0.99996154, 0.99996154, 2.00003846, -0.99996154,
1.00096154, 2.00003846
])
self.assertTrue(np.allclose(x, x_sol, rtol=1e-4))
self.assertAlmostEqual(nlp.objective(x), -6.99899, 3)
models = []
scenarios = dict()
coupling_vars = dict()
n_scenarios = 5
np.random.seed(seed=985739465)
bs = [b + np.random.normal(scale=2.0, size=1) for i in range(n_scenarios)]
for i in range(n_scenarios):
instance = create_basic_dense_qp(G, A, bs[i], c)
nlp = PyomoNLP(instance)
models.append(instance)
scenario_name = "s{}".format(i)
scenarios[scenario_name] = nlp
coupling_vars[scenario_name] = [nlp.variable_idx(instance.x[0])]
nlp = TwoStageStochasticNLP(scenarios, coupling_vars)
x = nlp.x_init()
y = nlp.y_init()
jac_c = nlp.jacobian_c(x)
plt.spy(jac_c)
plt.title('Jacobian of the constraints\n')
plt.show()
hess_lag = nlp.hessian_lag(x, y)
plt.spy(hess_lag.tocoo())
plt.title('Hessian of the Lagrangian function\n')
plt.show()
coupling_vars = dict()
n_scenarios = 5
np.random.seed(seed=985739465)
bs = [b+np.random.normal(scale=2.0, size=1) for i in range(n_scenarios)]
for i in range(n_scenarios):
instance = create_basic_dense_qp(G,
A,
bs[i],
c)
nlp = PyomoNLP(instance)
models.append(instance)
scenario_name = "s{}".format(i)
scenarios[scenario_name] = nlp
coupling_vars[scenario_name] = [nlp.variable_idx(instance.x[0])]
nlp = TwoStageStochasticNLP(scenarios, coupling_vars)
x = nlp.x_init()
y = nlp.y_init()
jac_c = nlp.jacobian_c(x)
plt.spy(jac_c)
plt.title('Jacobian of the constraints\n')
plt.show()
hess_lag = nlp.hessian_lag(x, y)
plt.spy(hess_lag.tocoo())
plt.title('Hessian of the Lagrangian function\n')
plt.show()
