Source code for pyomo.contrib.mindtpy.MindtPy

# -*- coding: utf-8 -*-

#  ___________________________________________________________________________
#  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.
#  ___________________________________________________________________________

"""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

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