def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.f = pmo.variable()
model.x = pmo.variable(lb=1,ub=3)
model.fi = pmo.parameter_dict()
model.fi[1] = pmo.parameter(value=1.0)
model.fi[2] = pmo.parameter(value=2.0)
model.fi[3] = pmo.parameter(value=0.0)
model.xi = pmo.parameter_dict()
model.xi[1] = pmo.parameter(value=1.0)
model.xi[2] = pmo.parameter(value=2.0)
model.xi[3] = pmo.parameter(value=3.0)
model.p = pmo.variable(domain=NonNegativeReals)
model.n = pmo.variable(domain=NonNegativeReals)
model.lmbda = pmo.create_variable_dict(range(1,4))
model.obj = pmo.objective(model.p+model.n)
model.c1 = pmo.constraint_dict()
model.c1[1] = pmo.constraint((0.0, model.lmbda[1], 1.0))
model.c1[2] = pmo.constraint((0.0, model.lmbda[2], 1.0))
model.c1[3] = pmo.constraint(0.0 <= model.lmbda[3])
model.c2 = pmo.sos2(model.lmbda.values())
model.c3 = pmo.constraint(sum(model.lmbda.values()) == 1)
model.c4 = pmo.constraint(model.f==sum(model.fi[i]*model.lmbda[i]
for i in model.lmbda))
model.c5 = pmo.constraint(model.x==sum(model.xi[i]*model.lmbda[i]
for i in model.lmbda))
model.x.fix(2.75)
# Make an empty SOS constraint
model.c6 = pmo.sos2([])
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.neg1 = pmo.parameter(value=-1.0)
model.pos1 = pmo.parameter(value=1.0)
model.s = list(range(1, 13))
model.x = pmo.create_variable_dict(keys=model.s)
model.x[1].lb = model.neg1
model.x[1].ub = model.pos1
model.x[2].lb = model.neg1
model.x[2].ub = model.pos1
model.obj = pmo.objective(sum(model.x[i] * ((-1)**(i))
for i in model.s),
sense=pmo.maximize)
model.c = pmo.constraint_dict()
model.c[3] = pmo.constraint(model.x[3] >= -1.)
model.c[4] = pmo.constraint(model.x[4] <= 1.)
model.c[5] = pmo.constraint(model.x[5] == -1.)
model.c[6] = pmo.constraint(model.x[6] == -1.)
model.c[7] = pmo.constraint(model.x[7] == 1.)
model.c[8] = pmo.constraint(model.x[8] == 1.)
model.c[9] = pmo.constraint((model.neg1, model.x[9], model.neg1))
model.c[10] = pmo.constraint((-1., model.x[10], -1.))
model.c[11] = pmo.constraint((1., model.x[11], 1.))
model.c[12] = pmo.constraint((1., model.x[12], 1.))
model.c_inactive = pmo.constraint_dict()
# to make the variable used in the constraint match the name
model.c_inactive[3] = pmo.constraint(model.x[3] >= -2.)
model.c_inactive[4] = pmo.constraint(model.x[4] <= 2.)
def _generate_model(self):
self.model = pmo.block()
model = self.model
model._name = self.description
model.s = [1,2]
model.x_unused = pmo.variable(domain=pmo.Integers)
model.x_unused.stale = False
model.x_unused_initialy_stale = pmo.variable(domain=pmo.Integers)
model.x_unused_initialy_stale.stale = True
model.X_unused = pmo.create_variable_dict(keys=model.s,
domain=pmo.Integers)
model.X_unused_initialy_stale = \
pmo.create_variable_dict(keys=model.s,
domain=pmo.Integers)
for i in model.s:
model.X_unused[i].stale = False
model.X_unused_initialy_stale[i].stale = True
model.x = pmo.variable(domain=pmo.IntegerInterval(bounds=(None,None)))
model.x.stale = False
model.x_initialy_stale = pmo.variable(domain=pmo.Integers)
model.x_initialy_stale.stale = True
model.X = pmo.create_variable_dict(keys=model.s,
domain=pmo.Integers)
model.X_initialy_stale = pmo.create_variable_dict(keys=model.s,
domain=pmo.Integers)
for i in model.s:
model.X[i].stale = False
model.X_initialy_stale[i].stale = True
model.obj = pmo.objective(model.x + \
model.x_initialy_stale + \
sum(model.X.values()) + \
sum(model.X_initialy_stale.values()))
model.c = pmo.constraint_dict()
model.c[1] = pmo.constraint(model.x >= 1)
model.c[2] = pmo.constraint(model.x_initialy_stale >= 1)
model.c[3] = pmo.constraint(model.X[1] >= 0)
model.c[4] = pmo.constraint(model.X[2] >= 1)
model.c[5] = pmo.constraint(model.X_initialy_stale[1] >= 0)
model.c[6] = pmo.constraint(model.X_initialy_stale[2] >= 1)
# Test that stale flags do not get updated
# on inactive blocks (where "inactive blocks" mean blocks
# that do NOT follow a path of all active parent blocks
# up to the top-level model)
flat_model = model.clone()
model.b = pmo.block()
model.B = pmo.block_dict()
model.b.b = flat_model.clone()
model.B[1] = pmo.tiny_block()
model.B[1].b = flat_model.clone()
model.B[2] = pmo.tiny_block()
model.B[2].b = flat_model.clone()
model.b.deactivate()
model.B.deactivate()
model.b.b.activate()
model.B[1].b.activate()
model.B[2].b.deactivate()
assert model.b.active is False
assert model.B[1].active is False
assert model.B[1].active is False
assert model.b.b.active is True
assert model.B[1].b.active is True
assert model.B[2].b.active is False
