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Merge pull request #3478 from tvignon-artelys/feature/support_for_knitro_python_package
Support for upcoming knitro python package
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‎pyomo/solvers/plugins/solvers/KNITROAMPL.py‎

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# ___________________________________________________________________________
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#
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# Pyomo: Python Optimization Modeling Objects
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# Copyright (c) 2008-2025
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# National Technology and Engineering Solutions of Sandia, LLC
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# Under the terms of Contract DE-NA0003525 with National Technology and
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# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
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# rights in this software.
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# This software is distributed under the 3-clause BSD License.
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# ___________________________________________________________________________
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import logging
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from pathlib import Path
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from pyomo.common import Executable
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from pyomo.common.collections import Bunch
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from pyomo.opt.base.formats import ProblemFormat, ResultsFormat
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from pyomo.opt.base.solvers import SolverFactory, OptSolver
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from pyomo.solvers.plugins.solvers.ASL import ASL
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logger = logging.getLogger('pyomo.solvers')
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@SolverFactory.register(
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'knitroampl', doc='The Knitro solver for NLP/MINLP and their subclasses'
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)
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class KNITROAMPL(ASL):
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"""An interface to the Knitro optimizer that uses the AMPL Solver Library."""
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def __init__(self, **kwds):
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"""Constructor"""
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executable = kwds.pop('executable', None)
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validate = kwds.pop('validate', True)
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kwds["type"] = "knitroampl"
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kwds["options"] = {'solver': "knitroampl"}
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OptSolver.__init__(self, **kwds)
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self._keepfiles = False
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self._results_file = None
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self._timer = ''
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self._user_executable = None
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# broadly useful for reporting, and in cases where
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# a solver plugin may not report execution time.
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self._last_solve_time = None
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self._define_signal_handlers = None
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self._version_timeout = 2
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if executable == 'knitroampl':
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self.set_executable(name=None, validate=validate)
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elif executable is not None:
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self.set_executable(name=executable, validate=validate)
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#
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# Setup valid problem formats, and valid results for each problem format.
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# Also set the default problem and results formats.
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#
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self._valid_problem_formats = [ProblemFormat.nl]
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self._valid_result_formats = {ProblemFormat.nl: [ResultsFormat.sol]}
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self.set_problem_format(ProblemFormat.nl)
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#
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# Note: Undefined capabilities default to 'None'
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#
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self._capabilities = Bunch()
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self._capabilities.linear = True
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self._capabilities.integer = True
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self._capabilities.quadratic_objective = True
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self._capabilities.quadratic_constraint = True
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self._capabilities.sos1 = False
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self._capabilities.sos2 = False
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def _default_executable(self):
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try:
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# If knitro Python package is available, use the executable it contains
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import knitro
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package_knitroampl_path = (
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Path(knitro.__file__).resolve().parent / 'knitroampl' / 'knitroampl'
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)
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executable = Executable(str(package_knitroampl_path))
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except ModuleNotFoundError:
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# Otherwise, search usual path list
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executable = Executable('knitroampl')
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if not executable:
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logger.warning(
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"Could not locate the 'knitroampl' executable, "
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"which is required for solver %s" % self.name
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)
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self.enable = False
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return None
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return executable.path()

‎pyomo/solvers/plugins/solvers/__init__.py‎

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xpress_direct,
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xpress_persistent,
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SAS,
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KNITROAMPL,
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)

‎pyomo/solvers/tests/checks/test_KNITROAMPL.py‎

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# ___________________________________________________________________________
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#
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# Pyomo: Python Optimization Modeling Objects
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# Copyright (c) 2008-2025
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# National Technology and Engineering Solutions of Sandia, LLC
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# Under the terms of Contract DE-NA0003525 with National Technology and
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# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
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# rights in this software.
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# This software is distributed under the 3-clause BSD License.
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# ___________________________________________________________________________
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from pyomo.common import unittest
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from pyomo.environ import (
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ConcreteModel,
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Var,
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Objective,
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Constraint,
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Suffix,
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NonNegativeIntegers,
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NonNegativeReals,
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value,
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)
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from pyomo.opt import SolverFactory, TerminationCondition
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knitroampl_available = SolverFactory('knitroampl').available(False)
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class TestKNITROAMPLInterface(unittest.TestCase):
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_infeasible_lp(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var(within=NonNegativeReals)
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model.C1 = Constraint(expr=model.X == 1)
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model.C2 = Constraint(expr=model.X == 2)
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model.Obj = Objective(expr=model.X)
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results = opt.solve(model)
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self.assertEqual(
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results.solver.termination_condition, TerminationCondition.infeasible
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)
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_unbounded_lp(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var()
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model.Obj = Objective(expr=model.X)
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results = opt.solve(model)
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self.assertIn(
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results.solver.termination_condition,
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(
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TerminationCondition.unbounded,
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TerminationCondition.infeasibleOrUnbounded,
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),
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)
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_optimal_lp(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var(within=NonNegativeReals)
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model.C1 = Constraint(expr=model.X >= 2.5)
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model.Obj = Objective(expr=model.X)
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results = opt.solve(model, load_solutions=True)
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self.assertEqual(
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results.solver.termination_condition, TerminationCondition.optimal
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)
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self.assertAlmostEqual(value(model.X), 2.5)
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_get_duals_lp(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var(within=NonNegativeReals)
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model.Y = Var(within=NonNegativeReals)
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model.C1 = Constraint(expr=2 * model.X + model.Y >= 8)
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model.C2 = Constraint(expr=model.X + 3 * model.Y >= 6)
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model.Obj = Objective(expr=model.X + model.Y)
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results = opt.solve(model, suffixes=['dual'], load_solutions=False)
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model.dual = Suffix(direction=Suffix.IMPORT)
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model.solutions.load_from(results)
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self.assertAlmostEqual(model.dual[model.C1], 0.4)
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self.assertAlmostEqual(model.dual[model.C2], 0.2)
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_infeasible_mip(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var(within=NonNegativeIntegers)
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model.C1 = Constraint(expr=model.X == 1)
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model.C2 = Constraint(expr=model.X == 2)
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model.Obj = Objective(expr=model.X)
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results = opt.solve(model)
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self.assertEqual(
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results.solver.termination_condition, TerminationCondition.infeasible
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)
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@unittest.skipIf(
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not knitroampl_available, "The 'knitroampl' command is not available"
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)
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def test_optimal_mip(self):
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with SolverFactory('knitroampl') as opt:
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model = ConcreteModel()
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model.X = Var(within=NonNegativeIntegers)
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model.C1 = Constraint(expr=model.X >= 2.5)
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model.Obj = Objective(expr=model.X)
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results = opt.solve(model, load_solutions=True)
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self.assertEqual(
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results.solver.termination_condition, TerminationCondition.optimal
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)
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self.assertAlmostEqual(value(model.X), 3)
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if __name__ == "__main__":
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unittest.main()

‎pyomo/solvers/tests/mip/test4.soln‎

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MILP solution:
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Objective value = 2
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x2 = 1

‎pyomo/solvers/tests/testcases.py‎

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#
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# KNITROAMPL
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#
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# NO EXPECTED FAILURES
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#
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for prob in ('LP_trivial_constraints', 'LP_trivial_constraints_kernel'):
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ExpectedFailures['knitroampl', 'nl', prob] = (
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lambda v: True,
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'Knitro does not consider tight trivial constraints to have zero dual value',
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)
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for prob in ('MILP_unbounded', 'MILP_unbounded_kernel'):
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ExpectedFailures['knitroampl', 'nl', prob] = (
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lambda v: v[:2] <= (14, 2),
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'Unbounded MILP detection not operational in Knitro, fixed in 15.0',
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)
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def generate_scenarios(arg=None):

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