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)
def test_model2(self):
model = create_model2()
nlp = PyomoNLP(model)
solver = CyIpoptSolver(nlp)
x, info = solver.solve(tee=False)
x_sol = np.array([3.0, 1.99997807])
y_sol = np.array([0.00017543])
self.assertTrue(np.allclose(x, x_sol, rtol=1e-4))
self.assertAlmostEqual(nlp.objective(x), -31.000000057167462, 3)
self.assertTrue(np.allclose(info['mult_g'], y_sol, rtol=1e-4))
def test_model1(self):
model = create_model1()
nlp = PyomoNLP(model)
solver = CyIpoptSolver(nlp)
x, info = solver.solve(tee=False)
x_sol = np.array([3.85958688, 4.67936007, 3.10358931])
y_sol = np.array([-1.0, 53.90357665])
self.assertTrue(np.allclose(x, x_sol, rtol=1e-4))
self.assertAlmostEqual(nlp.objective(x), -428.6362455416348)
self.assertTrue(np.allclose(info['mult_g'], y_sol, rtol=1e-4))
def test_model3(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])
model = create_model3(G, A, b, c)
nlp = PyomoNLP(model)
solver = CyIpoptSolver(nlp)
x, info = solver.solve(tee=False)
x_sol = np.array([2.0, -1.0, 1.0])
y_sol = np.array([-3., 2.])
self.assertTrue(np.allclose(x, x_sol, rtol=1e-4))
self.assertAlmostEqual(nlp.objective(x), -3.5, 3)
self.assertTrue(np.allclose(info['mult_g'], y_sol, rtol=1e-4))
