# Source code for pyomo.contrib.gdpopt.GDPopt

```
# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2022
# 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.
# ___________________________________________________________________________
"""Main driver module for GDPopt solver.
22.5.13 changes:
- rewrite of all algorithms
- deprecate 'strategy' in favor of 'algorithm'
- deprecate 'init_strategy' in favor of 'init_algorithm'
20.2.28 changes:
- bugfixes on tests
20.1.22 changes:
- improved subsolver time limit support for GAMS interface
- add maxTimeLimit exit condition for GDPopt-LBB
- add token Big M for reactivated constraints in GDPopt-LBB
- activate fbbt for branch-and-bound nodes
20.1.15 changes:
- internal cleanup of codebase
- merge GDPbb capabilities (logic-based branch and bound)
- refactoring of GDPbb code
- update logging information to include subsolver options
- improve SuppressInfeasibleWarning
- simplify mip preprocessing
- remove not-fully-implemented 'backtracking' from LOA
19.10.11 changes:
- bugfix on SolverStatus error message
19.5.13 changes:
- add handling to integer cuts for disjunct pruning during FBBT
19.4.23 changes:
- add support for linear subproblems
- use automatic differentiation for large constraints
- bugfixes on time limit support
- treat fixed variables as constants in GLOA cut generation
19.3.25 changes:
- add rudimentary time limit support
- start keeping basic changelog
"""
from pyomo.common.config import document_kwargs_from_configdict, ConfigDict
from pyomo.contrib.gdpopt import __version__
from pyomo.contrib.gdpopt.config_options import (
_add_common_configs,
_supported_algorithms,
_get_algorithm_config,
)
from pyomo.opt.base import SolverFactory
def _handle_strategy_deprecation(config):
# This method won't be needed when the strategy arg is removed, but for now,
# we need to copy it over as algorithm. The config system already gave the
# deprecation warning.
if config.algorithm is None and config.strategy is not None:
config.algorithm = config.strategy
[docs]@SolverFactory.register(
'gdpopt',
doc='The GDPopt decomposition-based '
'Generalized Disjunctive Programming (GDP) solver',
)
class GDPoptSolver(object):
"""Decomposition solver for Generalized Disjunctive Programming (GDP)
problems.
The GDPopt (Generalized Disjunctive Programming optimizer) solver applies a
variety of decomposition-based approaches to solve Generalized Disjunctive
Programming (GDP) problems. GDP models can include nonlinear, continuous
variables and constraints, as well as logical conditions.
These approaches include:
- Logic-based outer approximation (LOA)
- Logic-based branch-and-bound (LBB)
- Partial surrogate cuts [pending]
- Generalized Bender decomposition [pending]
This solver implementation was developed by Carnegie Mellon University in
the research group of Ignacio Grossmann.
For nonconvex problems, LOA may not report rigorous lower/upper bounds.
Questions: Please make a post at StackOverflow and/or contact Qi Chen
<https://github.com/qtothec> or David Bernal <https://github.com/bernalde>.
Several key GDPopt components were prototyped by BS and MS students:
- Logic-based branch and bound: Sunjeev Kale
- MC++ interface: Johnny Bates
- LOA set-covering initialization: Eloy Fernandez
- Logic-to-linear transformation: Romeo Valentin
"""
CONFIG = ConfigDict("GDPopt")
_add_common_configs(CONFIG)
[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)
alg_config = _get_algorithm_config()(options, preserve_implicit=True)
alg_config.set_value(kwds, skip_implicit=True)
_handle_strategy_deprecation(alg_config)
algorithm = alg_config.algorithm
if algorithm is None:
raise ValueError(
"No algorithm was specified to the solve method. "
"Please specify an algorithm or use an "
"algorithm-specific solver."
)
# get rid of 'algorithm' and 'strategy' if they exist so that the solver
# can validate.
kwds.pop('algorithm', None)
kwds.pop('strategy', None)
# The algorithm has already been validated, so this will work.
return SolverFactory(_supported_algorithms[algorithm][0]).solve(model, **kwds)
# Support use as a context manager under current solver API
def __enter__(self):
return self
def __exit__(self, t, v, traceback):
pass
[docs] def available(self, exception_flag=True):
"""Solver is always available. Though subsolvers may not be, they will
raise an error when the time comes.
"""
return True
def license_is_valid(self):
return True
_metasolver = False
```