def test_get_dfds_dcds2(self):
'''
It tests the function get_sensitivity with rooney & biegler's model.
'''
variable_name = ['asymptote', 'rate_constant']
theta = {
'asymptote': 19.142575284617866,
'rate_constant': 0.53109137696521
}
cov = np.array([[6.30579403, -0.4395341], [-0.4395341, 0.04193591]])
model_uncertain = ConcreteModel()
model_uncertain.asymptote = Var(initialize=15)
model_uncertain.rate_constant = Var(initialize=0.5)
model_uncertain.obj = Objective(
expr=model_uncertain.asymptote *
(1 - exp(-model_uncertain.rate_constant * 10)),
sense=minimize)
theta = {
'asymptote': 19.142575284617866,
'rate_constant': 0.53109137696521
}
for v in variable_name:
getattr(model_uncertain, v).setlb(theta[v])
getattr(model_uncertain, v).setub(theta[v])
gradient_f, gradient_c, col, row, line_dic = get_dfds_dcds(
model_uncertain, variable_name)
np.testing.assert_almost_equal(gradient_f, [0.99506259, 0.945148])
np.testing.assert_almost_equal(gradient_c, np.array([]))
assert col == ['asymptote', 'rate_constant']
assert row == ['obj']
def test_get_dfds_dcds(self):
'''
It tests the function get_sensitivity with a simple nonlinear programming example.
min f: p1*x1+ p2*(x2^2) + p1*p2
s.t c1: x1 = p1
c2: x2 = p2
c3: 10 <= p1 <= 10
c4: 5 <= p2 <= 5
'''
variable_name = ['p1', 'p2']
m = ConcreteModel()
m.x1 = Var(initialize=0)
m.x2 = Var(initialize=0)
m.p1 = Var(initialize=0)
m.p2 = Var(initialize=0)
m.obj = Objective(expr=m.x1 * m.p1 + m.x2 * m.x2 * m.p2 + m.p1 * m.p2,
sense=minimize)
m.c1 = Constraint(expr=m.x1 == m.p1)
m.c2 = Constraint(expr=m.x2 == m.p2)
theta = {'p1': 10.0, 'p2': 5.0}
for v in variable_name:
getattr(m, v).setlb(theta[v])
getattr(m, v).setub(theta[v])
gradient_f, gradient_c, col, row, line_dic = get_dfds_dcds(
m, variable_name)
np.testing.assert_almost_equal(gradient_f, [10., 50., 15., 35.])
np.testing.assert_almost_equal(gradient_c.toarray(),
[[1., 0., -1., 0.], [0., 1., 0., -1.]])
assert col == ['x1', 'x2', 'p1', 'p2']
assert row == ['c1', 'c2', 'obj']