def test_gams_expanded_arcs(self):
m = ConcreteModel()
m.x = Var()
m.y = Var()
m.CON1 = Port()
m.CON1.add(m.x, 'v')
m.CON2 = Port()
m.CON2.add(m.y, 'v')
m.c = Arc(source=m.CON1, destination=m.CON2)
TransformationFactory("network.expand_arcs").apply_to(m)
m.o = Objective(expr=m.x)
outs = StringIO()
io_options = dict(symbolic_solver_labels=True)
m.write(outs, format="gams", io_options=io_options)
# no error if we're here, but check for some identifying string
self.assertIn("x - y", outs.getvalue())
def test_gams_expanded_connectors(self):
m = ConcreteModel()
m.x = Var()
m.y = Var()
m.CON1 = Connector()
m.CON1.add(m.x, 'v')
m.CON2 = Connector()
m.CON2.add(m.y, 'v')
m.c = Constraint(expr=m.CON1 + m.CON2 >= 10)
TransformationFactory("core.expand_connectors").apply_to(m)
m.o = Objective(expr=m.x)
os = StringIO()
io_options = dict(symbolic_solver_labels=True)
m.write(os, format="gams", io_options=io_options)
# no error if we're here, but check for some identifying string
self.assertIn("x + y", os.getvalue())
def test_gams_expanded_connectors(self):
m = ConcreteModel()
m.x = Var()
m.y = Var()
m.CON1 = Connector()
m.CON1.add(m.x, 'v')
m.CON2 = Connector()
m.CON2.add(m.y, 'v')
m.c = Constraint(expr=m.CON1 + m.CON2 >= 10)
TransformationFactory("core.expand_connectors").apply_to(m)
m.o = Objective(expr=m.x)
os = StringIO()
io_options = dict(symbolic_solver_labels=True)
m.write(os, format="gams", io_options=io_options)
# no error if we're here, but check for some identifying string
self.assertIn("x + y", os.getvalue())
# The ASL differentiation routines seem to have a
# bug that causes the lagrangian hessian to become
# dense unless this constant term in moved to the
# numerator.
#
# This test model relies on the gjh_asl_json executable. It
# will not solve if sent to a real optimizer.
#
from pyomo.environ import ConcreteModel, Var, Objective, Constraint
model = ConcreteModel()
model.x = Var(bounds=(-1.0, 1.0), initialize=1.0)
model.y = Var(bounds=(-1.0, 1.0), initialize=2.0)
model.v = Var(bounds=(-1.0, 1.0), initialize=3.0)
model.p = Var(initialize=2.0)
model.p.fixed = True
model.OBJ = Objective(expr=model.x)
model.CON1 = Constraint(
rule=lambda model: (2.0, 1.0 / model.p * model.v * (model.x - model.y)))
model.CON2 = Constraint(expr=model.v * 1.0 / model.p *
(model.x - model.y) == 2.0)
model.CON3 = Constraint(expr=model.v * (model.x - model.y) / model.p == 2.0)
model.CON4 = Constraint(expr=model.v *
(model.x / model.p - model.y / model.p) == 2.0)
model.CON5 = Constraint(expr=model.v * (model.x - model.y) *
(1.0 / model.p) == 2.0)
model.CON6 = Constraint(expr=model.v * (model.x - model.y) -
2.0 * model.p == 0)
