# -*- 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.
# ___________________________________________________________________________
"""Implementation of the MindtPy solver.
22.2.10 changes:
- Add support for partitioning nonlinear-sum objective.
22.1.12 changes:
- Improve the log.
21.12.15 changes:
- Improve the online doc.
21.11.10 changes:
- Add support for solution pool of MIP solvers.
21.8.21 changes:
- Add support for gurobi_persistent solver in (Regularized) LP/NLP-based B&B algorithm.
21.5.19 changes:
- Add Feasibility Pump strategy.
- Add Regularized Outer Approximation method.
- Restructure and simplify the MindyPy code.
20.10.15 changes:
- Add Extended Cutting Plane and Global Outer Approximation strategy.
- Update online doc.
20.6.30 changes:
- Add support for different norms (L1, L2, L-infinity) of the objective function in the feasibility subproblem.
- Add support for different differentiate_mode to calculate Jacobian.
20.6.9 changes:
- Add cycling check in Outer Approximation method.
- Add support for GAMS solvers interface.
- Fix warmstart for both OA and LP/NLP method.
20.5.9 changes:
- Add single-tree implementation.
- Add support for cplex_persistent solver.
- Fix bug in OA cut expression in cut_generation.py.
24.1.11 changes:
- fix gurobi single tree termination check bug
- fix Gurobi single tree cycle handling
- fix bug in feasibility pump method
- add special handling for infeasible relaxed NLP
- update the log format of infeasible fixed NLP subproblems
- create a new copy_var_list_values function
"""
from pyomo.contrib.mindtpy import __version__
from pyomo.opt import SolverFactory
from pyomo.contrib.mindtpy.config_options import _get_MindtPy_config
from pyomo.common.config import document_kwargs_from_configdict
from pyomo.contrib.mindtpy.config_options import _supported_algorithms
[docs]@SolverFactory.register(
'mindtpy', doc='MindtPy: Mixed-Integer Nonlinear Decomposition Toolbox in Pyomo'
)
class MindtPySolver(object):
"""
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.
These approaches include:
- Outer approximation (OA)
- Global outer approximation (GOA)
- Regularized outer approximation (ROA)
- LP/NLP based branch-and-bound (LP/NLP)
- Global LP/NLP based branch-and-bound (GLP/NLP)
- Regularized LP/NLP based branch-and-bound (RLP/NLP)
- Feasibility pump (FP)
"""
CONFIG = _get_MindtPy_config()
[docs] def available(self, exception_flag=True):
"""Check if solver is available."""
return True
def license_is_valid(self):
return True
[docs] def version(self):
"""Return a 3-tuple describing the solver version."""
return __version__
[docs] @document_kwargs_from_configdict(CONFIG)
def solve(self, model, **kwds):
"""Solve the model.
Args:
model (Block): a Pyomo model or block to be solved
"""
# The algorithm should have been specified as an argument to the solve
# method. We will instantiate an ephemeral instance of the correct
# solver and call its solve method.
options = kwds.pop('options', {})
config = self.CONFIG(options, preserve_implicit=True)
# Don't complain about extra things, they aren't for us. We just need to
# get the algorithm and then our job is done.
config.set_value(kwds, skip_implicit=True)
return SolverFactory(_supported_algorithms[config.strategy][0]).solve(
model, **kwds
)
#
# Support 'with' statements.
#
def __enter__(self):
return self
def __exit__(self, t, v, traceback):
pass