def test_diet_10n_10i(self):
from Model.model import Model
m = Model()
MIN_REQ = load_obj('diet_10n_min_req')
ingredients, list_of_ingredients = load_obj('diet_10n_10i_ing'), load_obj('diet_10n_10i_l_o_ing')
x = []
for ing in list_of_ingredients:
x.append(m.add_var("real+", name=ing))
x = np.array(x)
m.minimize(sum(get_by_key(ingredients, "price", list_of_ingredients) * x))
for cst in MIN_REQ:
left = get_by_key(ingredients, cst, list_of_ingredients)
m.add_constraint(sum(left * x) >= MIN_REQ[cst])
m.solve(consider_dual=0)
try:
i = 0
for ing in list_of_ingredients:
m.add_lazy_constraint(x[i] <= ingredients[ing]['max'])
i += 1
except InfeasibleError as e:
pass
else:
self.fail("Should raise InfeasibleError but didn't")
def test_woody_min_dual_lazy_constraint(self):
m = Model()
x1 = m.add_var("real+", name="a")
x2 = m.add_var("real+", name="b")
x3 = m.add_var("real+", name="c")
m.minimize(35 * x1 + 60 * x2 + 75 * x3)
m.add_constraint(8 * x1 + 12 * x2 + 16 * x3 >= 120)
m.add_constraint(15 * x2 + 20 * x3 >= 60)
m.add_constraint(3 * x1 + 6 * x2 + 9 * x3 >= 48)
m.solve(consider_dual=2)
m.add_lazy_constraint(x1 <= 5)
m.add_lazy_constraint(x3 >= 1)
computed_solution = m.get_solution_object()
real_sol = [5, 0, 5, 550]
for x_idx in range(len(real_sol)):
self.assertAlmostEqual(computed_solution[x_idx], real_sol[x_idx])
print("all added")
t0 = time()
m.solve(consider_dual=0)
print("Solved first in %f" % (time() - t0))
m.print_solution(slack=False)
sol_obj = m.get_solution_object()
solved = False
while not solved:
solved = True
i = 0
for ing in list_of_ingredients:
if sol_obj[i] > ingredients[ing]['max']:
solved = False
m.add_lazy_constraint(x[i] <= ingredients[ing]['max'])
exit(1)
sol_obj = m.get_solution_object()
break
i += 1
print("Solved total in %f" % (time() - t0))
print("End tableau")
print(m.t.tableau)
print("Steps: ", m.steps)
m.print_solution(slack=False)