def infeasible_ip_model_1():
model = flp.Model()
v1 = flp.variable.Binary('v1')
v2 = flp.variable.Binary('v2')
model += v1, v2
model += flp.Constraint(v1 + v2, '<=', 1)
model += flp.Constraint(v1, '>=', 0.1)
model += flp.Constraint(v2, '>=', 0.1)
return model
def infeasible_lp_model_1():
model = flp.Model()
v1 = flp.variable.Continuous('v1')
v2 = flp.variable.Continuous('v2')
model += v1, v2
model += flp.Constraint(v1 + v2, '<=', 1)
model += flp.Constraint(v1, '>=', 2)
model += flp.Constraint(v2, '>=', 2)
return model
def ip_model_1():
model = flp.Model()
v1 = flp.variable.Binary('v1')
v2 = flp.variable.Integer('v2')
model += v1, v2
model += flp.Objective('min', v2)
model += flp.Constraint(v2, '>=', -1.5 * v1 - 1)
model += flp.Constraint(v2, '>=', 2.5 * v1 - 5)
return model
def test_write_to_file(self):
model = flp.Model()
v1 = flp.variable.Continuous('v1', 10, 20)
v2 = flp.variable.Integer('v2', -10)
v3 = flp.variable.Binary('v3')
v4 = flp.variable.Continuous('v4')
model.add_variables(v1, v2, v3, v4)
model += flp.Objective('max', 3 * v1)
model += flp.Constraint(v1, '<=', v3)
model += flp.Constraint(-v1 + v2, '>=', v3 / 3 - 7.5)
full_lp_filename = flp.write_lp_file(model, 'test.lp')
p = Path(full_lp_filename)
self.assertEqual(p.is_file(), True)
p.unlink()
def lp_model_1():
model = flp.Model()
v1 = flp.variable.Continuous('v1', 10, 20)
v2 = flp.variable.Continuous('v2', 10, 20)
model += v1, v2
model += flp.Objective('max', sum([v1 + v2]))
model += flp.Constraint(v2, '<=', -2 * v1 + 50)
return model
def unbounded_lp_model_1():
model = flp.Model()
v1 = flp.variable.Continuous('v1')
v2 = flp.variable.Continuous('v2')
model += v1, v2
model += flp.Objective(
'max',
v1 + 2 * v2) ### TODO: ERROR CHECKING REQUIRED ON INPUT ARGUMENTS
model += flp.Constraint(v1 + v2, '<=', 1)
return model
def test_model_1(self):
model = flp.Model()
v1 = flp.variable.Continuous('v1', 10, 20)
v2 = flp.variable.Integer('v2', -10, 0)
v3 = flp.variable.Binary('v3')
model.add_variables(*[v1, v2, v3])
obj = flp.Objective('max', 3 * v1)
model.add_objective(obj)
c = flp.Constraint(v1 + 3, '<=', v3)
model.add_constraints(c)
self.assertIsInstance(model, flp.Model)
self.assertEqual(3, model.num_vars())
self.assertEqual(1, model.num_cons())
def big_ip_model_1(solver):
# Knapsack
class Item:
def __init__(self, name, value, size):
self.name = str(name)
self.value = value
self.size = size
class Bag:
def __init__(self, name, size):
self.name = str(name)
self.size = size
random.seed(0)
N_ITEMS = 100
N_BAGS = int(N_ITEMS / 4)
items = [
Item(i, random.randint(1, 100), random.randint(1, 50))
for i in range(N_ITEMS)
]
bags = [Bag(i, random.randint(50, 100)) for i in range(N_BAGS)]
t = time.time()
model = flp.Model()
vars = {}
for item in items:
for bag in bags:
v = flp.variable.Binary(f'v{item.name}_{bag.name}')
vars[(item.name, bag.name)] = v
model += v
print('Vars added', time.time() - t)
# obj_expr = sum(item.value * model.variables[f'v{item.name}_{bag.name}'] \
# for item in items for bag in bags)
# obj_expr = flp.Expression.from_var_dict(dict(model.variables[f'v{item.name}_{bag.name}'], item.value) \
# for item in items for bag in bags)
obj_expr = flp.tsum((item.value, model.variables[f'v{item.name}_{bag.name}']) \
for item in items for bag in bags)
model += flp.Objective('max', obj_expr)
print('Objective added', time.time() - t)
# Assign each item at most once
for item in items:
lhs_expr = flp.tsum(
(1, model.variables[f'v{item.name}_{bag.name}']) for bag in bags)
model += flp.Constraint(lhs_expr, '<=', 1)
# Fill bag at most to size
for bag in bags:
lhs_expr = flp.tsum(
(item.size, model.variables[f'v{item.name}_{bag.name}'])
for item in items)
model += flp.Constraint(lhs_expr, '<=', bag.size)
print('Constraints added', time.time() - t)
solver.set_params({'time_limit': 10})
# solver.set_mipstart(soln)
soln, run = solver.solve(model,
keep_log_file=True,
keep_lp_file=True,
keep_sol_file=True)
print('Solved', time.time() - t)
print(flp.util.run_summary(run, soln, model))
