def test_jacobian_SimpleModel1(self):
model = SimpleModel1()
m = model.make_model()
x_init_list = [
[-5.0], [-4.0], [-3.0], [-1.5], [0.5], [1.0], [2.0], [3.5]
]
external_model = ExternalPyomoModel(
[m.x], [m.y], [m.residual_eqn], [m.external_eqn],
)
for x in x_init_list:
external_model.set_input_values(x)
jac = external_model.evaluate_jacobian_equality_constraints()
self.assertAlmostEqual(
jac.toarray()[0][0],
model.evaluate_jacobian(x[0]),
delta=1e-8,
)
def test_jacobian_SimpleModel2x2_1(self):
model = SimpleModel2by2_1()
m = model.make_model()
m.x[0].set_value(1.0)
m.x[1].set_value(2.0)
m.y[0].set_value(3.0)
m.y[1].set_value(4.0)
x0_init_list = [-5.0, -3.0, 0.5, 1.0, 2.5]
x1_init_list = [-4.5, -2.3, 0.0, 1.0, 4.1]
x_init_list = list(itertools.product(x0_init_list, x1_init_list))
external_model = ExternalPyomoModel(
list(m.x.values()),
list(m.y.values()),
list(m.residual_eqn.values()),
list(m.external_eqn.values()),
)
for x in x_init_list:
external_model.set_input_values(x)
jac = external_model.evaluate_jacobian_equality_constraints()
expected_jac = model.evaluate_jacobian(x)
np.testing.assert_allclose(jac.toarray(), expected_jac, rtol=1e-8)
def test_jacobian_SimpleModel2(self):
model = SimpleModel2()
m = model.make_model()
x_init_list = [
[-5.0], [-4.0], [-3.0], [-1.5], [0.5], [1.0], [2.0], [3.5]
]
external_model = ExternalPyomoModel(
[m.x], [m.y], [m.residual_eqn], [m.external_eqn],
)
for x in x_init_list:
external_model.set_input_values(x)
jac = external_model.evaluate_jacobian_equality_constraints()
# evaluate_jacobian_equality_constraints involves an LU
# factorization and repeated back-solve. SciPy returns a
# dense matrix from this operation. I am not sure if I should
# cast it to a sparse matrix. For now it is dense...
self.assertAlmostEqual(
jac.toarray()[0][0],
model.evaluate_jacobian(x[0]),
delta=1e-7,
)
