Source code for gemseo_mma.opt.lib_mma

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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, ineq_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, normalize_design_space: bool = True, **kwargs: Any, ) -> dict[str, Any]: """ 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. ineq_tolerance: The tolerance on the inequality 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. """ # noqa: D205, D212, D415 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, ineq_toleranceeq_tolerance=ineq_tolerance, normalize_design_space=normalize_design_space, **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(, x_vect_or_iteration=x_opt ) c_opt = { problem.database.get_function_value(, x_vect_or_iteration=x_opt ) for cont in problem.constraints } c_opt_grad = { problem.database.get_gradient_history([ -1 ] for cont in problem.constraints } 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, )