# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Damien Guenot
# OTHER AUTHORS - MACROSCOPIC CHANGES
# Francois Gallard : refactoring for v1, May 2016
"""The library of SciPy local optimization algorithms."""
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar
from typing import Final
from numpy import isfinite
from numpy import real
from scipy.optimize import minimize
from gemseo.algos.design_space_utils import get_value_and_bounds
from gemseo.algos.opt.base_optimization_library import BaseOptimizationLibrary
from gemseo.algos.opt.base_optimization_library import OptimizationAlgorithmDescription
from gemseo.algos.opt.base_optimizer_settings import BaseOptimizerSettings
from gemseo.algos.opt.scipy_local.settings.base_scipy_local_settings import (
BaseScipyLocalSettings,
)
from gemseo.algos.opt.scipy_local.settings.cobyqa import COBYQA_Settings
from gemseo.algos.opt.scipy_local.settings.lbfgsb import L_BFGS_B_Settings
from gemseo.algos.opt.scipy_local.settings.nelder_mead import NELDER_MEAD_Settings
from gemseo.algos.opt.scipy_local.settings.slsqp import SLSQP_Settings
from gemseo.algos.opt.scipy_local.settings.tnc import TNC_Settings
from gemseo.utils.compatibility.scipy import SCIPY_GREATER_THAN_1_14
from gemseo.utils.constants import C_LONG_MAX
if TYPE_CHECKING:
from gemseo.algos.optimization_problem import OptimizationProblem
[docs]
@dataclass
class SciPyAlgorithmDescription(OptimizationAlgorithmDescription):
"""The description of the SciPy local optimization library."""
library_name: str = "SciPy Local"
"""The library name."""
Settings: type[BaseScipyLocalSettings] = BaseScipyLocalSettings
"""The option validation model for SciPy local optimization library."""
[docs]
class ScipyOpt(BaseOptimizationLibrary):
"""The library of SciPy optimization algorithms."""
__DOC: Final[str] = "https://docs.scipy.org/doc/scipy/reference/"
ALGORITHM_INFOS: ClassVar[dict[str, SciPyAlgorithmDescription]] = {
"SLSQP": SciPyAlgorithmDescription(
algorithm_name="SLSQP",
description=(
"Sequential Least-Squares Quadratic Programming (SLSQP) "
"implemented in the SciPy library"
),
handle_equality_constraints=True,
handle_inequality_constraints=True,
internal_algorithm_name="SLSQP",
require_gradient=True,
positive_constraints=True,
website=f"{__DOC}optimize.minimize-slsqp.html",
Settings=SLSQP_Settings,
),
"L-BFGS-B": SciPyAlgorithmDescription(
algorithm_name="L-BFGS-B",
description=(
"Limited-memory BFGS algorithm implemented in the SciPy library"
),
internal_algorithm_name="L-BFGS-B",
require_gradient=True,
website=f"{__DOC}optimize.minimize-lbfgsb.html",
Settings=L_BFGS_B_Settings,
),
"TNC": SciPyAlgorithmDescription(
algorithm_name="TNC",
description=(
"Truncated Newton (TNC) algorithm implemented in SciPy library"
),
internal_algorithm_name="TNC",
require_gradient=True,
website=f"{__DOC}optimize.minimize-tnc.html",
Settings=TNC_Settings,
),
"NELDER-MEAD": SciPyAlgorithmDescription(
algorithm_name="NELDER-MEAD",
description="Nelder-Mead algorithm implemented in the SciPy library",
internal_algorithm_name="Nelder-Mead",
website=f"{__DOC}optimize.minimize-neldermead.html",
Settings=NELDER_MEAD_Settings,
),
}
if SCIPY_GREATER_THAN_1_14:
ALGORITHM_INFOS["COBYQA"] = SciPyAlgorithmDescription(
algorithm_name="COBYQA",
description=(
"Derivative-free trust-region SQP method "
"based on quadratic models for constrained optimization."
),
internal_algorithm_name="COBYQA",
handle_equality_constraints=True,
handle_inequality_constraints=True,
positive_constraints=True,
website=f"{__DOC}optimize.minimize-cobyqa.html",
Settings=COBYQA_Settings,
)
def _run(self, problem: OptimizationProblem, **settings: Any) -> tuple[str, Any]:
# Get the normalized bounds:
x_0, l_b, u_b = get_value_and_bounds(problem.design_space, self._normalize_ds)
# Replace infinite values with None:
l_b = [val if isfinite(val) else None for val in l_b]
u_b = [val if isfinite(val) else None for val in u_b]
bounds = list(zip(l_b, u_b))
# Get constraint in SciPy format
scipy_constraints = [
{
"type": constraint.f_type,
"fun": constraint.evaluate,
"jac": constraint.jac,
}
for constraint in self._get_right_sign_constraints(problem)
]
# Filter settings to get only the scipy.optimize.minimize ones
options_ = self._filter_settings(settings, BaseOptimizerSettings)
# Deactivate stopping criteria which are handled by GEMSEO
tolerance = 0.0
if self._algo_name != "TNC":
options_["maxiter"] = C_LONG_MAX
opt_result = minimize(
fun=lambda x: real(problem.objective.evaluate(x)),
x0=x_0,
method=self.ALGORITHM_INFOS[self._algo_name].internal_algorithm_name,
jac=problem.objective.jac,
bounds=bounds,
constraints=scipy_constraints,
options=options_,
tol=tolerance,
)
return opt_result.message, opt_result.status
