def define_model(**kwds):
model = ConcreteModel()
model.x1 = Var(bounds=(0, 3)) # domain variable
model.x2 = Var(bounds=(0, 3)) # domain variable
model.x3 = Var(bounds=(0, 3)) # domain variable
model.x4 = Var(bounds=(0, 3)) # domain variable
model.Fx1 = Var() # range variable
model.Fx2 = Var() # range variable
model.Fx3 = Var() # range variable
model.Fx4 = Var() # range variable
model.p = Param(initialize=1.0)
model.obj = Objective(expr=model.Fx1 + model.Fx2 + model.Fx3 + model.Fx4 +
model.x1 + model.x2 + model.x3 + model.x4,
sense=kwds.pop('sense', maximize))
model.piecewise1 = Piecewise(model.Fx1,
model.x1,
pw_pts=DOMAIN_PTS,
f_rule=F,
**kwds)
model.piecewise2 = Piecewise(model.Fx2,
model.x2,
pw_pts=DOMAIN_PTS,
f_rule=F,
**kwds)
model.piecewise3 = Piecewise(model.Fx3,
model.x3,
pw_pts=DOMAIN_PTS,
f_rule=F,
**kwds)
model.piecewise4 = Piecewise(model.Fx4,
model.x4,
pw_pts=DOMAIN_PTS,
f_rule=F,
**kwds)
#Fix the answer for testing purposes
model.set_answer_constraint1 = Constraint(
expr=model.x1 == 0.5) # Fx1 should solve to 0
model.set_answer_constraint2 = Constraint(expr=model.x2 == 1.0) #
model.set_answer_constraint3 = Constraint(expr=model.Fx2 == 0.5) #
model.set_answer_constraint4 = Constraint(
expr=model.x3 == 1.5) # Fx3 should solve to 1
model.set_answer_constraint5 = Constraint(
expr=model.x4 == 2.5) # Fx4 should solve to 1.5
return model
def create_instance(scenario_name):
cnt = d[scenario_name]
model = ConcreteModel()
# first stage
model.x = Var(bounds=(0, 10))
# first stage derived
model.y = Expression(expr=model.x + 1)
model.fx = Var()
# second stage
model.z = Var(bounds=(-10, 10))
# second stage derived
model.q = Expression(expr=model.z**2)
model.fz = Var()
model.r = Var()
# stage costs
model.StageCost = Expression([1, 2])
model.StageCost.add(1, model.fx)
model.StageCost.add(2, -model.fz + model.r - cnt)
model.o = Objective(expr=sum_product(model.StageCost))
model.ZERO = Param(initialize=0, mutable=True)
if cnt == 0:
cnt = model.ZERO
model.c_first_stage = Constraint(expr=model.x >= 0)
# test our handling of intermediate variables that
# are created by Piecewise but can not necessarily
# be declared on the scenario tree
model.p_first_stage = Piecewise(model.fx,
model.x,
pw_pts=[0., 2., 5., 7., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[10., 10., 9., 10., 10.],
force_pw=True)
model.c_second_stage = Constraint(expr=model.x + model.r * cnt >= -100)
model.r_second_stage = Constraint(expr=inequality(-cnt, model.r, 0))
# exercise more of the code by making this an indexed
# block
model.p_second_stage = Piecewise([1],
model.fz,
model.z,
pw_pts=[-10, -5., 0., 5., 10.],
pw_constr_type='EQ',
pw_repn='INC',
f_rule=[0., 0., -1., 2. + cnt, 1.],
force_pw=True)
return model
def define_model(**kwds):
model = ConcreteModel()
model.x = Var(INDEX_SET, bounds=(-5, 4)) # domain variable
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET, initialize=1.0)
model.obj = Objective(expr=sum_product(model.Fx),
sense=kwds.pop('sense', maximize))
model.piecewise = Piecewise(INDEX_SET,
model.Fx,
model.x,
pw_pts=DOMAIN_PTS,
f_rule=F,
**kwds)
#Fix the answer for testing purposes
model.set_answer_constraint1 = Constraint(expr=model.x[1] == -5.0)
model.set_answer_constraint2 = Constraint(expr=model.x[2] == -3.0)
model.set_answer_constraint3 = Constraint(expr=model.x[3] == -2.5)
model.set_answer_constraint4 = Constraint(expr=model.x[4] == -1.5)
model.set_answer_constraint5 = Constraint(expr=model.x[5] == 2.0)
model.set_answer_constraint6 = Constraint(expr=model.x[6] == 3.5)
model.set_answer_constraint7 = Constraint(expr=model.x[7] == 4.0)
return model
def define_model(**kwds):
model = ConcreteModel()
model.x1 = Var(bounds=(-5,4)) # domain variable
model.x2 = Var(bounds=(-5,4)) # domain variable
model.x3 = Var(bounds=(-5,4)) # domain variable
model.x4 = Var(bounds=(-5,4)) # domain variable
model.x5 = Var(bounds=(-5,4)) # domain variable
model.x6 = Var(bounds=(-5,4)) # domain variable
model.x7 = Var(bounds=(-5,4)) # domain variable
model.Fx1 = Var() # range variable
model.Fx2 = Var() # range variable
model.Fx3 = Var() # range variable
model.Fx4 = Var() # range variable
model.Fx5 = Var() # range variable
model.Fx6 = Var() # range variable
model.Fx7 = Var() # range variable
model.obj = Objective(expr=model.Fx1+model.Fx2+model.Fx3+model.Fx4+model.Fx5+model.Fx6+model.Fx7, sense=kwds.pop('sense',maximize))
model.piecewise1 = Piecewise(model.Fx1,model.x1,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise2 = Piecewise(model.Fx2,model.x2,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise3 = Piecewise(model.Fx3,model.x3,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise4 = Piecewise(model.Fx4,model.x4,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise5 = Piecewise(model.Fx5,model.x5,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise6 = Piecewise(model.Fx6,model.x6,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
model.piecewise7 = Piecewise(model.Fx7,model.x7,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
#Fix the answer for testing purposes
model.set_answer_constraint1 = Constraint(expr= model.x1 == -5.0)
model.set_answer_constraint2 = Constraint(expr= model.x2 == -3.0)
model.set_answer_constraint3 = Constraint(expr= model.x3 == -2.5)
model.set_answer_constraint4 = Constraint(expr= model.x4 == -1.5)
model.set_answer_constraint5 = Constraint(expr= model.x5 == 2.0)
model.set_answer_constraint6 = Constraint(expr= model.x6 == 3.5)
model.set_answer_constraint7 = Constraint(expr= model.x7 == 4.0)
return model
def _generate_model(self):
self.model = ConcreteModel()
model = self.model
model._name = self.description
model.x = Var()
model.y = Var()
model.obj = Objective(expr=model.y)
model.p = Piecewise(model.y, model.x,
pw_pts=[-1,0,1],
f_rule=[1,0.5,1],
pw_repn='SOS2',
pw_constr_type='LB',
unbounded_domain_var=True)
def define_model(**kwds):
model = ConcreteModel()
model.x = Var(INDEX_SET1, INDEX_SET2, bounds=(0,6)) # domain variable
model.Fx = Var(INDEX_SET1, INDEX_SET2) # range variable
model.p = Param(INDEX_SET1, INDEX_SET2, initialize=1.0)
model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET1,INDEX_SET2,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
f_rule=F, **kwds)
#Fix the answer for testing purposes
model.set_answer_constraint1 = Constraint(INDEX_SET2,rule= lambda model,t2,t3: model.x['1',t2,t3] == 0.0)
model.set_answer_constraint2 = Constraint(INDEX_SET2,rule= lambda model,t2,t3: model.x['2',t2,t3] == 3.0)
model.set_answer_constraint3 = Constraint(INDEX_SET2,rule= lambda model,t2,t3: model.x['3',t2,t3] == 5.5)
model.set_answer_constraint4 = Constraint(INDEX_SET2,rule= lambda model,t2,t3: model.x['40',t2,t3] == 6.0)
return model
def define_model(**kwds):
model = ConcreteModel()
model.x = Var(INDEX) # domain variable
model.Fx = Var(INDEX) # range variable
model.obj = Objective(expr=sum_product(model.Fx)+sum_product(model.x), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
f_rule=F,
unbounded_domain_var=True,
**kwds)
#Fix the answer for testing purposes
model.set_answer_constraint1 = Constraint(expr= model.x[1] == 0.5) # Fx1 should solve to 0
model.set_answer_constraint2 = Constraint(expr= model.x[2] == 1.0) #
model.set_answer_constraint3 = Constraint(expr= model.Fx[2] == 0.5) #
model.set_answer_constraint4 = Constraint(expr= model.x[3] == 1.5) # Fx3 should solve to 1
model.set_answer_constraint5 = Constraint(expr= model.x[4] == 2.5) # Fx4 should solve to 1.5
return model