def epigraph_reformulation(exp, slack_var_list, constraint_list, use_mcpp, sense):
"""Epigraph reformulation.
Generate the epigraph reformuation for objective expressions.
Parameters
----------
slack_var_list : VarList
Slack vars for epigraph reformulation.
constraint_list : ConstraintList
Epigraph constraint list.
use_mcpp : Bool
Whether to use mcpp to tighten the bound of slack variables.
exp : Expression
The expression to reformualte.
sense : objective sense
The objective sense.
"""
slack_var = slack_var_list.add()
if mcpp_available() and use_mcpp:
mc_obj = McCormick(exp)
slack_var.setub(mc_obj.upper())
slack_var.setlb(mc_obj.lower())
else:
# Use Pyomo's contrib.fbbt package
lb, ub = compute_bounds_on_expr(exp)
if sense == minimize:
slack_var.setlb(lb)
else:
slack_var.setub(ub)
if sense == minimize:
constraint_list.add(expr=slack_var >= exp)
else:
constraint_list.add(expr=slack_var <= exp)
def process_objective(solve_data, config, move_linear_objective=False):
m = solve_data.working_model
util_blk = getattr(m, solve_data.util_block_name)
# Handle missing or multiple objectives
active_objectives = list(m.component_data_objects(
ctype=Objective, active=True, descend_into=True))
solve_data.results.problem.number_of_objectives = len(active_objectives)
if len(active_objectives) == 0:
config.logger.warning(
'Model has no active objectives. Adding dummy objective.')
util_blk.dummy_objective = Objective(expr=1)
main_obj = util_blk.dummy_objective
elif len(active_objectives) > 1:
raise ValueError('Model has multiple active objectives.')
else:
main_obj = active_objectives[0]
solve_data.results.problem.sense = main_obj.sense
solve_data.objective_sense = main_obj.sense
# Move the objective to the constraints if it is nonlinear
if main_obj.expr.polynomial_degree() not in (1, 0) \
or move_linear_objective:
if move_linear_objective:
config.logger.info("Moving objective to constraint set.")
else:
config.logger.info("Objective is nonlinear. Moving it to constraint set.")
util_blk.objective_value = Var(domain=Reals, initialize=0)
if mcpp_available():
mc_obj = McCormick(main_obj.expr)
util_blk.objective_value.setub(mc_obj.upper())
util_blk.objective_value.setlb(mc_obj.lower())
if main_obj.sense == minimize:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value >= main_obj.expr)
solve_data.results.problem.sense = ProblemSense.minimize
else:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value <= main_obj.expr)
solve_data.results.problem.sense = ProblemSense.maximize
# Deactivate the original objective and add this new one.
main_obj.deactivate()
util_blk.objective = Objective(
expr=util_blk.objective_value, sense=main_obj.sense)
# Add the new variable and constraint to the working lists
util_blk.variable_list.append(util_blk.objective_value)
util_blk.constraint_list.append(util_blk.objective_constr)
def process_objective(solve_data, config):
m = solve_data.working_model
util_blk = getattr(m, solve_data.util_block_name)
# Handle missing or multiple objectives
objs = list(m.component_data_objects(
ctype=Objective, active=True, descend_into=True))
num_objs = len(objs)
solve_data.results.problem.number_of_objectives = num_objs
if num_objs == 0:
config.logger.warning(
'Model has no active objectives. Adding dummy objective.')
util_blk.dummy_objective = Objective(expr=1)
main_obj = util_blk.dummy_objective
elif num_objs > 1:
raise ValueError('Model has multiple active objectives.')
else:
main_obj = objs[0]
solve_data.results.problem.sense = main_obj.sense
solve_data.objective_sense = main_obj.sense
# Move the objective to the constraints if it is nonlinear
if main_obj.expr.polynomial_degree() not in (1, 0):
config.logger.info("Objective is nonlinear. Moving it to constraint set.")
util_blk.objective_value = Var(domain=Reals, initialize=0)
if mcpp_available():
mc_obj = McCormick(main_obj.expr)
util_blk.objective_value.setub(mc_obj.upper())
util_blk.objective_value.setlb(mc_obj.lower())
if main_obj.sense == minimize:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value >= main_obj.expr)
solve_data.results.problem.sense = ProblemSense.minimize
else:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value <= main_obj.expr)
solve_data.results.problem.sense = ProblemSense.maximize
# Deactivate the original objective and add this new one.
main_obj.deactivate()
util_blk.objective = Objective(
expr=util_blk.objective_value, sense=main_obj.sense)
# Add the new variable and constraint to the working lists
util_blk.variable_list.append(util_blk.objective_value)
util_blk.constraint_list.append(util_blk.objective_constr)
SolverFactory('gdpopt').solve(
eight_process,
strategy='LOA',
init_strategy='custom_disjuncts',
custom_init_disjuncts=initialize,
mip_solver=mip_solver,
nlp_solver=nlp_solver,
call_after_subproblem_feasible=assert_correct_disjuncts_active)
self.assertTrue(fabs(value(eight_process.profit.expr) - 68) <= 1E-2)
@unittest.skipIf(not GLOA_solvers_available,
"Required subsolvers %s are not available" % (GLOA_solvers, ))
@unittest.skipIf(not mcpp_available(), "MC++ is not available")
class TestGLOA(unittest.TestCase):
"""Tests for global logic-based outer approximation."""
def test_GDP_integer_vars(self):
m = ConcreteModel()
m.x = Var(bounds=(0, 10))
m.y = Var(domain=Integers, bounds=(0, 5))
m.d = Disjunction(
expr=[[m.x >= m.y, m.y >= 3.5], [m.x >= m.y, m.y >= 2.5]])
m.o = Objective(expr=m.x)
SolverFactory('gdpopt').solve(m,
strategy='GLOA',
mip_solver=mip_solver,
nlp_solver=nlp_solver,
minlp_solver=minlp_solver)
self.assertAlmostEqual(value(m.o.expr), 3)
self.assertTrue(soln_disj.indicator_var.value == 1)
SolverFactory('gdpopt').solve(
eight_process, strategy='LOA', init_strategy='custom_disjuncts',
custom_init_disjuncts=initialize,
mip_solver=mip_solver,
nlp_solver=nlp_solver,
call_after_subproblem_feasible=assert_correct_disjuncts_active)
self.assertTrue(fabs(value(eight_process.profit.expr) - 68) <= 1E-2)
@unittest.skipIf(not GLOA_solvers_available,
"Required subsolvers %s are not available"
% (GLOA_solvers,))
@unittest.skipIf(not mcpp_available(), "MC++ is not available")
class TestGLOA(unittest.TestCase):
"""Tests for global logic-based outer approximation."""
def test_GDP_integer_vars(self):
m = ConcreteModel()
m.x = Var(bounds=(0, 10))
m.y = Var(domain=Integers, bounds=(0, 5))
m.d = Disjunction(expr=[[
m.x >= m.y, m.y >= 3.5
],
[
m.x >= m.y, m.y >= 2.5
]])
m.o = Objective(expr=m.x)
SolverFactory('gdpopt').solve(
def process_objective(solve_data, config, move_linear_objective=False, use_mcpp=True, updata_var_con_list=True):
"""Process model objective function.
Check that the model has only 1 valid objective.
If the objective is nonlinear, move it into the constraints.
If no objective function exists, emit a warning and create a dummy objective.
Parameters
----------
solve_data (GDPoptSolveData): solver environment data class
config (ConfigBlock): solver configuration options
move_linear_objective (bool): if True, move even linear
objective functions to the constraints
"""
m = solve_data.working_model
util_blk = getattr(m, solve_data.util_block_name)
# Handle missing or multiple objectives
active_objectives = list(m.component_data_objects(
ctype=Objective, active=True, descend_into=True))
solve_data.results.problem.number_of_objectives = len(active_objectives)
if len(active_objectives) == 0:
config.logger.warning(
'Model has no active objectives. Adding dummy objective.')
util_blk.dummy_objective = Objective(expr=1)
main_obj = util_blk.dummy_objective
elif len(active_objectives) > 1:
raise ValueError('Model has multiple active objectives.')
else:
main_obj = active_objectives[0]
solve_data.results.problem.sense = ProblemSense.minimize if main_obj.sense == 1 else ProblemSense.maximize
solve_data.objective_sense = main_obj.sense
# Move the objective to the constraints if it is nonlinear
if main_obj.expr.polynomial_degree() not in (1, 0) \
or move_linear_objective:
if move_linear_objective:
config.logger.info("Moving objective to constraint set.")
else:
config.logger.info(
"Objective is nonlinear. Moving it to constraint set.")
util_blk.objective_value = Var(domain=Reals, initialize=0)
if mcpp_available() and use_mcpp:
mc_obj = McCormick(main_obj.expr)
util_blk.objective_value.setub(mc_obj.upper())
util_blk.objective_value.setlb(mc_obj.lower())
else:
# Use Pyomo's contrib.fbbt package
lb, ub = compute_bounds_on_expr(main_obj.expr)
if solve_data.results.problem.sense == ProblemSense.minimize:
util_blk.objective_value.setlb(lb)
else:
util_blk.objective_value.setub(ub)
if main_obj.sense == minimize:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value >= main_obj.expr)
else:
util_blk.objective_constr = Constraint(
expr=util_blk.objective_value <= main_obj.expr)
# Deactivate the original objective and add this new one.
main_obj.deactivate()
util_blk.objective = Objective(
expr=util_blk.objective_value, sense=main_obj.sense)
# Add the new variable and constraint to the working lists
if main_obj.expr.polynomial_degree() not in (1, 0) or (move_linear_objective and updata_var_con_list):
util_blk.variable_list.append(util_blk.objective_value)
util_blk.continuous_variable_list.append(util_blk.objective_value)
util_blk.constraint_list.append(util_blk.objective_constr)
util_blk.objective_list.append(util_blk.objective)
if util_blk.objective_constr.body.polynomial_degree() in (0, 1):
util_blk.linear_constraint_list.append(util_blk.objective_constr)
else:
util_blk.nonlinear_constraint_list.append(
util_blk.objective_constr)
else:
subsolvers_available = True
model_list = [
EightProcessFlowsheet(convex=False),
Nonconvex1(),
Nonconvex2(),
Nonconvex3(),
Nonconvex4(),
]
@unittest.skipIf(not subsolvers_available,
'Required subsolvers %s are not available' %
(required_solvers, ))
@unittest.skipIf(not pyomo_mcpp.mcpp_available(), 'MC++ is not available')
class TestMindtPy(unittest.TestCase):
"""Tests for the MindtPy solver plugin."""
def test_GOA(self):
"""Test the global outer approximation decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
for model in model_list:
results = opt.solve(model,
strategy='GOA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
single_tree=True)
self.assertIn(results.solver.termination_condition, [
TerminationCondition.optimal, TerminationCondition.feasible
])
else:
subsolvers_available = True
model_list = [EightProcessFlowsheet(convex=False),
Nonconvex1(),
Nonconvex2(),
Nonconvex3(),
Nonconvex4(),
]
@unittest.skipIf(not subsolvers_available,
'Required subsolvers %s are not available'
% (required_solvers,))
@unittest.skipIf(not pyomo_mcpp.mcpp_available(),
'MC++ is not available')
class TestMindtPy(unittest.TestCase):
"""Tests for the MindtPy solver plugin."""
def test_GOA(self):
"""Test the global outer approximation decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
for model in model_list:
results = opt.solve(model, strategy='GOA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
single_tree=True)
self.assertIn(results.solver.termination_condition, [
TerminationCondition.optimal, TerminationCondition.feasible])
