# 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.
"""MMA optimizer library."""
from __future__ import annotations
from typing import Any
from gemseo.algos.opt.optimization_library import OptimizationAlgorithmDescription
from gemseo.algos.opt.optimization_library import OptimizationLibrary
from gemseo.algos.opt_result import OptimizationResult
from gemseo_mma.opt.core.mma_optimizer import MMAOptimizer
[docs]class MMASvanberg(OptimizationLibrary):
"""Svanberg Method of Moving Asymptotes optimization library."""
descriptions: dict[str, OptimizationAlgorithmDescription]
"""The optimization algorithm description."""
def __init__(self) -> None:
"""Constructor."""
super().__init__()
self.descriptions = {
"MMA": OptimizationAlgorithmDescription(
"MMA",
"MMA",
"MMA",
require_gradient=True,
handle_equality_constraints=False,
handle_inequality_constraints=True,
positive_constraints=False,
)
}
def _get_options(
self,
max_iter: int = 1000,
ftol_abs: float = 1e-14,
xtol_abs: float = 1e-14,
ftol_rel: float = 1e-8,
xtol_rel: float = 1e-8,
eq_tolerance: float = 1e-2,
tol: float = 1e-2,
conv_tol: float | None = None,
max_optimization_step: float = 0.1,
max_asymptote_distance: float = 10.0,
min_asymptote_distance: float = 0.01,
initial_asymptotes_distance: float = 0.5,
asymptotes_distance_amplification_coefficient: float = 1.2,
asymptotes_distance_reduction_coefficient: float = 0.7,
**kwargs: Any,
) -> dict[str, Any]:
r"""Sets the options.
Args:
ftol_abs: The absolute tolerance on the objective function.
xtol_abs: The absolute tolerance on the design parameters.
max_iter: The maximum number of iterations.
ftol_rel: The relative tolerance on the objective function.
xtol_rel: The relative tolerance on the design parameters.
normalize_design_space: If True, normalize the design variables between 0
and 1.
eq_tolerance: The tolerance on the equality constraints.
tol: tolerance of convergence used in MMA to be compared with kkt residual.
conv_tol: If provided control all other convergence tolerances.
max_optimization_step: The maximum optimization step.
max_asymptote_distance: The maximum distance of the asymptotes from the
current design variable value.
min_asymptote_distance: The minimum distance of the asymptotes from the
current design variable value.
initial_asymptotes_distance: The initial asymptotes distance from the
current design variable value.
asymptotes_distance_amplification_coefficient: The amplification factor
for successful iterations.
asymptotes_distance_reduction_coefficient: The decremental factor for
unsuccessful iterations.
**kwargs: The other options.
Returns:
The converted options.
Raises:
ValueError: If an option is invalid.
"""
if conv_tol is not None:
ftol_rel = conv_tol
ftol_abs = conv_tol
xtol_rel = conv_tol
xtol_abs = conv_tol
else:
conv_tol = min(ftol_rel, ftol_abs, xtol_rel, xtol_abs)
return self._process_options(
max_iter=max_iter,
tol=tol,
conv_tol=conv_tol,
max_optimization_step=max_optimization_step,
max_asymptote_distance=max_asymptote_distance,
min_asymptote_distance=min_asymptote_distance,
initial_asymptotes_distance=initial_asymptotes_distance,
asymptotes_distance_amplification_coefficient=asymptotes_distance_amplification_coefficient,
asymptotes_distance_reduction_coefficient=asymptotes_distance_reduction_coefficient,
ftol_rel=ftol_rel,
ftol_abs=ftol_abs,
xtol_rel=xtol_rel,
xtol_abs=xtol_abs,
eq_tolerance=eq_tolerance,
**kwargs,
)
def _run(self, **options: float | int | str) -> OptimizationResult:
"""Runs the algorithm, to be overloaded by subclasses.
Args:
**options: The options dict for the algorithm,
see associated MMA_options.json file.
Returns:
The OptimizationResult object.
"""
optimizer = MMAOptimizer(self.problem)
message, status = optimizer.optimize(**options)
return self.get_optimum_from_database(message, status)
[docs] def get_optimum_from_database(
self, message: str | None = None, status: int | None = None
) -> OptimizationResult:
"""Get optimum from database using last point of database.
Retrieves the optimum from the database and builds an optimization result object
from it.
Args:
message: The solver message.
status: The solver status.
Returns:
The OptimizationResult object.
"""
problem = self.problem
if len(problem.database) == 0:
return OptimizationResult(
optimizer_name=self.algo_name,
message=message,
status=status,
n_obj_call=0,
)
x_0 = problem.database.get_x_vect(1)
# get last point as optimum
x_opt = problem.database.get_x_vect(-1)
is_feas, _ = problem.get_violation_criteria(x_opt)
f_opt = problem.database.get_function_value(
function_name=problem.objective.name, x_vect_or_iteration=x_opt
)
c_opt = {
cont.name: problem.database.get_function_value(
function_name=cont.name, x_vect_or_iteration=x_opt
)
for cont in problem.constraints
}
c_opt_grad = {
cont.name: problem.database.get_gradient_history(function_name=cont.name)[
-1
]
for cont in problem.constraints
}
# f_opt, x_opt, is_feas, c_opt, c_opt_grad = problem.get_optimum()
if f_opt is not None and not problem.minimize_objective:
f_opt = -f_opt
return OptimizationResult(
x_0=x_0,
x_opt=x_opt,
f_opt=f_opt,
optimizer_name=self.algo_name,
message=message,
status=status,
n_obj_call=problem.objective.n_calls,
is_feasible=is_feas,
constraint_values=c_opt,
constraints_grad=c_opt_grad,
)
