# -*- coding: utf-8 -*-
# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2024
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
import logging
from pyomo.contrib.mindtpy.config_options import _get_MindtPy_FP_config
from pyomo.contrib.mindtpy.algorithm_base_class import _MindtPyAlgorithm
from pyomo.core import ConstraintList
from pyomo.contrib.mindtpy.util import calc_jacobians
from pyomo.opt import SolverFactory
from pyomo.contrib.mindtpy.cut_generation import add_oa_cuts
[docs]
@SolverFactory.register(
'mindtpy.fp', doc='MindtPy: Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo'
)
class MindtPy_FP_Solver(_MindtPyAlgorithm):
"""
Decomposition solver for Mixed-Integer Nonlinear Programming (MINLP) problems.
The MindtPy (Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo) solver
applies a variety of decomposition-based approaches to solve Mixed-Integer
Nonlinear Programming (MINLP) problems.
This class includes:
- Feasibility pump (FP)
"""
CONFIG = _get_MindtPy_FP_config()
[docs]
def check_config(self):
# feasibility pump alone will lead to iteration_limit = 0, important!
self.config.iteration_limit = 0
self.config.move_objective = True
super().check_config()
[docs]
def initialize_mip_problem(self):
"""Deactivate the nonlinear constraints to create the MIP problem."""
super().initialize_mip_problem()
self.jacobians = calc_jacobians(
self.mip.MindtPy_utils.nonlinear_constraint_list,
self.config.differentiate_mode,
) # preload jacobians
self.mip.MindtPy_utils.cuts.oa_cuts = ConstraintList(
doc='Outer approximation cuts'
)
def add_cuts(
self,
dual_values,
linearize_active=True,
linearize_violated=True,
cb_opt=None,
nlp=None,
):
add_oa_cuts(
self.mip,
dual_values,
self.jacobians,
self.objective_sense,
self.mip_constraint_polynomial_degree,
self.mip_iter,
self.config,
self.timing,
cb_opt,
linearize_active,
linearize_violated,
)
[docs]
def MindtPy_iteration_loop(self):
pass