Source code for gemseo.algos.opt.augmented_lagrangian.order_0
# 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.
#
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# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""Augmented Lagrangian of order 0."""
from __future__ import annotations
from numpy import heaviside
from numpy import ndarray
from gemseo.algos.opt.augmented_lagrangian.penalty_heuristic import (
AugmentedLagrangianPenaltyHeuristic,
)
from gemseo.algos.opt.optimization_library import OptimizationAlgorithmDescription
[docs]
class AugmentedLagrangianOrder0(AugmentedLagrangianPenaltyHeuristic):
"""An augmented Lagrangian algorithm of order 0.
The Lagrange multipliers are updated thanks to the constraint values solely (no
gradient used).
"""
def __init__(self) -> None: # noqa:D107
super().__init__()
self.descriptions = {
"Augmented_Lagrangian_order_0": OptimizationAlgorithmDescription(
algorithm_name="Augmented_Lagrangian_order_0",
description=(
"Augmented Lagrangian algorithm for gradient-less functions."
),
internal_algorithm_name="Augmented_Lagrangian",
handle_equality_constraints=True,
handle_inequality_constraints=True,
require_gradient=False,
)
}
def _update_lagrange_multipliers(
self, eq_lag: dict[str, ndarray], ineq_lag: dict[str, ndarray], x_opt: ndarray
) -> None: # noqa:D107
for constraint in self.problem.constraints:
if constraint.name in ineq_lag:
mu_1 = (
ineq_lag[constraint.name]
+ self._rho * self._function_outputs[constraint.name]
)
ineq_lag[constraint.name] = (mu_1) * heaviside(mu_1.real, 0.0)
elif constraint.name in eq_lag:
eq_lag[constraint.name] = (
eq_lag[constraint.name]
+ self._rho * self._function_outputs[constraint.name]
)