# Source code for gemseo.algos.stop_criteria

```
# 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 - API and implementation and/or documentation
# :author: Francois Gallard
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Various termination criteria for drivers
****************************************
"""
from __future__ import annotations
from numpy import all
from numpy import allclose
from numpy import average
[docs]class FunctionIsNan(TerminationCriterion):
"""Stops driver when a function has NaN value or NaN Jacobian."""
[docs]class DesvarIsNan(TerminationCriterion):
"""Stops driver when the design variables are nan."""
[docs]class MaxIterReachedException(TerminationCriterion):
"""Exception raised when the maximum number of iterations is reached by the
driver."""
[docs]class MaxTimeReached(TerminationCriterion):
"""Exception raised when the maximum execution time is reached by the driver."""
[docs]class FtolReached(TerminationCriterion):
"""Exception raised when the f_tol_rel or f_tol_abs criteria is reached by the
driver."""
[docs]class XtolReached(TerminationCriterion):
"""Exception raised when the x_tol_rel or x_tol_abs criteria is reached by the
driver."""
[docs]def is_x_tol_reached(opt_problem, x_tol_rel=1e-6, x_tol_abs=1e-6, n_x=2):
"""Tests if the tolerance on the design variables are reached The coordinate wise
average of the last n_x points are taken Then it is checked that all points are
within the distance of the center with relative and absolute tolerances specified by
the user.
Parameters
----------
opt_problem: OptimizationProblem
the optimization problem containing the iterations
x_tol_rel: float
relative tolerance
x_tol_abs: float
absolute tolerance
n_x: int
number of design vectors to account for
"""
database = opt_problem.database
if len(database) < n_x:
return False
x_values = database.get_last_n_x(n_x)
# Checks that there is at least one feasible point
if not any(opt_problem.is_point_feasible(database[x_val]) for x_val in x_values):
return False
x_average = average(x_values, axis=0)
return all(
[
allclose(x_val, x_average, atol=x_tol_abs, rtol=x_tol_rel)
for x_val in x_values
]
)
[docs]def is_f_tol_reached(opt_problem, f_tol_rel=1e-6, f_tol_abs=1e-6, n_x=2):
"""Tests if the tolerance on the objective function are reached The average function
value of the last n_x points are taken Then it is checked that all points are within
the distance of the center with relative and absolute tolerances specified by the
user.
Parameters
----------
opt_problem: OptimizationProblem
the optimization problem containing the iterations
x_tol_rel: float
relative tolerance
x_tol_abs: float
absolute tolerance
n_x: int
number of design vectors to account for
"""
database = opt_problem.database
if len(database) < n_x:
return False
# Checks that there is at least one feasible point
x_values = database.get_last_n_x(n_x)
if not any(opt_problem.is_point_feasible(database[x_val]) for x_val in x_values):
return False
obj_name = opt_problem.objective.name
f_values = [
f_value
for f_value in [database.get_f_of_x(obj_name, x_val) for x_val in x_values]
if f_value is not None
]
if not f_values:
return False
f_average = average(f_values)
return all(
[
allclose(f_val, f_average, atol=f_tol_abs, rtol=f_tol_rel)
for f_val in f_values
]
)
```