def setup_main(solve_data, config, fp, regularization_problem):
"""Set up main problem/main regularization problem for OA, ECP, Feasibility Pump and ROA methods.
Args:
solve_data (MindtPySolveData): data container that holds solve-instance data.
config (ConfigBlock): the specific configurations for MindtPy.
fp (bool): whether it is in the loop of feasibility pump.
regularization_problem (bool): whether it is solving a regularization problem.
"""
MindtPy = solve_data.mip.MindtPy_utils
for c in MindtPy.constraint_list:
if c.body.polynomial_degree() not in {1, 0}:
c.deactivate()
MindtPy.cuts.activate()
sign_adjust = 1 if solve_data.objective_sense == minimize else -1
MindtPy.del_component('mip_obj')
if regularization_problem and config.single_tree:
MindtPy.del_component('loa_proj_mip_obj')
MindtPy.cuts.del_component('obj_reg_estimate')
if config.add_regularization is not None and config.add_no_good_cuts:
if regularization_problem:
MindtPy.cuts.no_good_cuts.activate()
else:
MindtPy.cuts.no_good_cuts.deactivate()
if fp:
MindtPy.del_component('fp_mip_obj')
if config.fp_main_norm == 'L1':
MindtPy.fp_mip_obj = generate_norm1_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif config.fp_main_norm == 'L2':
MindtPy.fp_mip_obj = generate_norm2sq_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif config.fp_main_norm == 'L_infinity':
MindtPy.fp_mip_obj = generate_norm_inf_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif regularization_problem:
if MindtPy.objective_list[0].expr.polynomial_degree() in {1, 0}:
MindtPy.objective_constr.activate()
if config.add_regularization == 'level_L1':
MindtPy.loa_proj_mip_obj = generate_norm1_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization == 'level_L2':
MindtPy.loa_proj_mip_obj = generate_norm2sq_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization == 'level_L_infinity':
MindtPy.loa_proj_mip_obj = generate_norm_inf_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization in {
'grad_lag', 'hess_lag', 'hess_only_lag', 'sqp_lag'
}:
MindtPy.loa_proj_mip_obj = generate_lag_objective_function(
solve_data.mip,
solve_data.best_solution_found,
config,
solve_data,
discrete_only=False)
if solve_data.objective_sense == minimize:
MindtPy.cuts.obj_reg_estimate = Constraint(
expr=MindtPy.objective_value <= (1 - config.level_coef) *
solve_data.UB + config.level_coef * solve_data.LB)
else:
MindtPy.cuts.obj_reg_estimate = Constraint(
expr=MindtPy.objective_value >= (1 - config.level_coef) *
solve_data.LB + config.level_coef * solve_data.UB)
else:
if config.add_slack:
MindtPy.del_component('aug_penalty_expr')
MindtPy.aug_penalty_expr = Expression(
expr=sign_adjust * config.OA_penalty_factor *
sum(v for v in MindtPy.cuts.slack_vars[...]))
main_objective = MindtPy.objective_list[-1]
MindtPy.mip_obj = Objective(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0),
sense=solve_data.objective_sense)
if config.use_dual_bound:
# Delete previously added dual bound constraint
MindtPy.cuts.del_component('dual_bound')
if solve_data.objective_sense == minimize:
MindtPy.cuts.dual_bound = Constraint(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0) >=
solve_data.LB,
doc=
'Objective function expression should improve on the best found dual bound'
)
else:
MindtPy.cuts.dual_bound = Constraint(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0) <=
solve_data.UB,
doc=
'Objective function expression should improve on the best found dual bound'
)
def setup_main(solve_data, config, fp, regularization_problem):
"""Set up main problem/main regularization problem for OA, ECP, Feasibility Pump and ROA methods.
Parameters
----------
solve_data : MindtPySolveData
Data container that holds solve-instance data.
config : ConfigBlock
The specific configurations for MindtPy.
fp : bool
Whether it is in the loop of feasibility pump.
regularization_problem : bool
Whether it is solving a regularization problem.
"""
MindtPy = solve_data.mip.MindtPy_utils
for c in MindtPy.constraint_list:
if c.body.polynomial_degree(
) not in solve_data.mip_constraint_polynomial_degree:
c.deactivate()
MindtPy.cuts.activate()
sign_adjust = 1 if solve_data.objective_sense == minimize else -1
MindtPy.del_component('mip_obj')
if regularization_problem and config.single_tree:
MindtPy.del_component('loa_proj_mip_obj')
MindtPy.cuts.del_component('obj_reg_estimate')
if config.add_regularization is not None and config.add_no_good_cuts:
if regularization_problem:
MindtPy.cuts.no_good_cuts.activate()
else:
MindtPy.cuts.no_good_cuts.deactivate()
if fp:
MindtPy.del_component('fp_mip_obj')
if config.fp_main_norm == 'L1':
MindtPy.fp_mip_obj = generate_norm1_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif config.fp_main_norm == 'L2':
MindtPy.fp_mip_obj = generate_norm2sq_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif config.fp_main_norm == 'L_infinity':
MindtPy.fp_mip_obj = generate_norm_inf_objective_function(
solve_data.mip,
solve_data.working_model,
discrete_only=config.fp_discrete_only)
elif regularization_problem:
# The epigraph constraint is very "flat" for branching rules.
# In ROA, if the objective function is linear(or quadratic when quadratic_strategy = 1 or 2), the original objective function is used in the MIP problem.
# In the MIP projection problem, we need to reactivate the epigraph constraint(objective_constr).
if MindtPy.objective_list[0].expr.polynomial_degree(
) in solve_data.mip_objective_polynomial_degree:
MindtPy.objective_constr.activate()
if config.add_regularization == 'level_L1':
MindtPy.loa_proj_mip_obj = generate_norm1_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization == 'level_L2':
MindtPy.loa_proj_mip_obj = generate_norm2sq_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization == 'level_L_infinity':
MindtPy.loa_proj_mip_obj = generate_norm_inf_objective_function(
solve_data.mip,
solve_data.best_solution_found,
discrete_only=False)
elif config.add_regularization in {
'grad_lag', 'hess_lag', 'hess_only_lag', 'sqp_lag'
}:
MindtPy.loa_proj_mip_obj = generate_lag_objective_function(
solve_data.mip,
solve_data.best_solution_found,
config,
solve_data,
discrete_only=False)
if solve_data.objective_sense == minimize:
MindtPy.cuts.obj_reg_estimate = Constraint(
expr=sum(MindtPy.objective_value[:]) <=
(1 - config.level_coef) * solve_data.primal_bound +
config.level_coef * solve_data.dual_bound)
else:
MindtPy.cuts.obj_reg_estimate = Constraint(
expr=sum(MindtPy.objective_value[:]) >=
(1 - config.level_coef) * solve_data.primal_bound +
config.level_coef * solve_data.dual_bound)
else:
if config.add_slack:
MindtPy.del_component('aug_penalty_expr')
MindtPy.aug_penalty_expr = Expression(
expr=sign_adjust * config.OA_penalty_factor *
sum(v for v in MindtPy.cuts.slack_vars[...]))
main_objective = MindtPy.objective_list[-1]
MindtPy.mip_obj = Objective(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0),
sense=solve_data.objective_sense)
if config.use_dual_bound:
# Delete previously added dual bound constraint
MindtPy.cuts.del_component('dual_bound')
if solve_data.objective_sense == minimize:
MindtPy.cuts.dual_bound = Constraint(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0) >=
solve_data.dual_bound,
doc=
'Objective function expression should improve on the best found dual bound'
)
else:
MindtPy.cuts.dual_bound = Constraint(
expr=main_objective.expr +
(MindtPy.aug_penalty_expr if config.add_slack else 0) <=
solve_data.dual_bound,
doc=
'Objective function expression should improve on the best found dual bound'
)