Note
Go to the end to download the full example code.
Batch sampling#
GEMSEO v6.2.0 added the option vectorize (default: False) to the DOE algorithms
to enable batch sampling of outputs of interest.
In other words,
GEMSEO can evaluate a multidisciplinary system at several points at the same time,
without the need for multiple processes
(see the DOE option n_processes for more information).
This can be particularly useful when evaluating such a system
in parallel is more expensive than evaluating it serially because
the disciplines are so inexpensive.
In this case, the batch sampling can be sequential.
This example illustrates this features, first for an evaluation problem, then for a scenario.
from __future__ import annotations
from typing import TYPE_CHECKING
from numpy import array
from numpy import newaxis
from pandas.testing import assert_frame_equal
from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.scipy.scipy_doe import SciPyDOE
from gemseo.algos.doe.scipy.settings.mc import MC_Settings
from gemseo.algos.evaluation_problem import EvaluationProblem
from gemseo.core.discipline.discipline import Discipline
from gemseo.core.mdo_functions.mdo_function import MDOFunction
from gemseo.formulations.disciplinary_opt_settings import DisciplinaryOpt_Settings
from gemseo.formulations.mdf_settings import MDF_Settings
from gemseo.problems.mdo.sellar.sellar_1 import Sellar1
from gemseo.problems.mdo.sellar.sellar_2 import Sellar2
from gemseo.problems.mdo.sellar.sellar_design_space import SellarDesignSpace
from gemseo.problems.mdo.sellar.sellar_system import SellarSystem
from gemseo.scenarios.mdo_scenario import MDOScenario
from gemseo.utils.timer import Timer
if TYPE_CHECKING:
from gemseo.datasets.optimization_dataset import OptimizationDataset
from gemseo.typing import RealArray
from gemseo.typing import StrKeyMapping
For an evaluation problem#
First,
we illustrate this feature with an EvaluationProblem
consisting of calculating the area of several rectangles.
To do this, we define the design space
including the bounds of the length and width of such a rectangle:
design_space = DesignSpace()
design_space.add_variable("length", lower_bound=0.0, upper_bound=10.0)
design_space.add_variable("width", lower_bound=0.0, upper_bound=10.0)
Then,
we define a function to compute the area of a rectangle from an array.
The latter can contain either the values of the length and width of a rectangle,
i.e., array([length, width]),
or a collection of n pairs of length and width corresponding to n rectangles,
i.e., array([[length_1, width_1], ..., [length_n, width_n]]).
Note that the function includes dimension checks to support both cases.
class AreaFunction:
"""Compute the area of a rectangle."""
def __init__(self) -> None:
self.last_input_data = array([])
def __call__(self, input_data: RealArray) -> RealArray:
"""Compute the area of the rectangle.
Args:
input_data: The input data.
This 1D array is
shaped as ``(input_dimension,)`` or ``(n_samples, input_dimension)``.
Returns:
The output data.
This 1D array is
shaped as ``(output_dimension,)`` or ``(n_samples, output_dimension)``.
"""
is_1d = input_data.ndim == 1
if is_1d:
input_data = input_data[newaxis, :]
self.last_input_data = input_data
length = input_data[:, 0]
width = input_data[:, 1]
area = length * width
return area[0] if is_1d else area
function = AreaFunction()
We can verify that the function works correctly:
(
function(array([3.0, 6.0])),
function(array([2.0, 4.0])),
function(array([[3.0, 6.0], [2.0, 4.0]])),
)
(np.float64(18.0), np.float64(8.0), array([18., 8.]))
This function can be attached
to an EvaluationProblem using an MDOFunction
problem = EvaluationProblem(design_space)
problem.add_observable(MDOFunction(function, "area"))
Then, this problem can be sampled using a DOE algorithm, e.g., Monte Carlo. The default mode is sequential sampling; we use it to generate reference samples:
SciPyDOE("MC").execute(problem, settings_model=MC_Settings(n_samples=1000))
samples_ref = problem.to_dataset()
INFO - 16:24:58: Evaluation problem:
INFO - 16:24:58: Evaluate the functions: area
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | length | 0 | None | 10 | float |
INFO - 16:24:58: | width | 0 | None | 10 | float |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: Running the algorithm MC:
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and reset the EvaluationProblem to clear the database before refilling it:
problem.reset()
Finally, we can generate the same samples in a batch mode
SciPyDOE("MC").execute(
problem, settings_model=MC_Settings(n_samples=1000, vectorize=True)
)
samples = problem.to_dataset()
INFO - 16:24:58: Evaluation problem:
INFO - 16:24:58: Evaluate the functions: area
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | length | 0 | None | 10 | float |
INFO - 16:24:58: | width | 0 | None | 10 | float |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: Running the algorithm MC:
check that these samples are equal to the reference ones:
assert_frame_equal(samples, samples_ref)
and that the function did receive a 2D array of shape (1000, 2):
assert function.last_input_data.shape == (1000, 2)
For a scenario#
In this second part of the example, we will apply batch sampling to the case of a scenario.
Vectorizing the user disciplines is more complicated than vectorizing functions because the user disciplines will be handled by multidisciplinary processes, including process disciplines, which only accept 1D input and output arrays and 2D Jacobian arrays. A user discipline must therefore always receive dictionaries of 1D arrays and return dictionaries of 1D arrays.
class AreaDiscipline(Discipline):
"""Compute the area of a rectangle."""
def __init__(self) -> None:
super().__init__()
self.io.input_grammar.update_from_names(("length", "width"))
self.io.output_grammar.update_from_names(("area",))
def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
length = input_data["length"]
width = input_data["width"]
return {"area": length * width}
area_discipline = AreaDiscipline()
In the case of this discipline,
the expected forms of the input and output data are
{"length": array([length_1, ..., length_n]), "width": array([width_1, ..., width_n])}
and {"area": array([area_1, ..., area_n])} respectively.
Note that in the case of a d-length vectorial variable "x",
the expected form would be
{"x": array([x_1_1, ..., x_d_1, ..., x_1_n, ..., x_d_n])}.
DisciplinaryOpt formulation#
We use this discipline to illustrate the batch sampling feature
with the DisciplinaryOpt formulation.
Remember that this MDO formulation executes the disciplines
in the order provided by the user.
For a better illustration,
we add a discipline to post-process the output data of AreaDiscipline:
class AreaIncreaser(Discipline):
"""Increase the area by a factor of 2."""
def __init__(self) -> None:
super().__init__()
self.io.input_grammar.update_from_names(("area",))
self.io.output_grammar.update_from_names(("final_area",))
def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
return {"final_area": 2.0 * input_data["area"]}
disciplines = [area_discipline, AreaIncreaser()]
Now, we can create and execute the scenario using batch sampling:
scenario = MDOScenario(
disciplines,
"final_area",
design_space,
formulation_settings_model=DisciplinaryOpt_Settings(),
)
scenario.execute(MC_Settings(n_samples=1000, vectorize=True))
samples = scenario.to_dataset(opt_naming=False)
INFO - 16:24:58: *** Start MDOScenario execution ***
INFO - 16:24:58: MDOScenario
INFO - 16:24:58: Disciplines: AreaDiscipline AreaIncreaser
INFO - 16:24:58: MDO formulation: DisciplinaryOpt
INFO - 16:24:58: Optimization problem:
INFO - 16:24:58: minimize final_area(length, width)
INFO - 16:24:58: with respect to length, width
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: | length | 0 | None | 10 | float |
INFO - 16:24:58: | width | 0 | None | 10 | float |
INFO - 16:24:58: +--------+-------------+-------+-------------+-------+
INFO - 16:24:58: Solving optimization problem with algorithm MC:
INFO - 16:24:58: Optimization result:
INFO - 16:24:58: Optimizer info:
INFO - 16:24:58: Status: None
INFO - 16:24:58: Message: None
INFO - 16:24:58: Solution:
INFO - 16:24:58: Objective: 0.006453354446785439
INFO - 16:24:58: Design space:
INFO - 16:24:58: +--------+-------------+----------------------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +--------+-------------+----------------------+-------------+-------+
INFO - 16:24:58: | length | 0 | 3.359702633489273 | 10 | float |
INFO - 16:24:58: | width | 0 | 0.000960405599956804 | 10 | float |
INFO - 16:24:58: +--------+-------------+----------------------+-------------+-------+
INFO - 16:24:58: *** End MDOScenario execution (time: 0:00:00.012502) ***
We can see that the samples are equal to the reference ones to within a factor of 2:
samples_ref.rename_variable("area", "final_area")
samples_ref.transform_data(lambda area: 2.0 * area, variable_names="final_area")
assert_frame_equal(samples, samples_ref)
and that the AreaDiscipline received and returned 1D arrays of size 1000:
assert area_discipline.io.data["length"].shape == (1000,)
assert area_discipline.io.data["width"].shape == (1000,)
assert area_discipline.io.data["area"].shape == (1000,)
MDF formulation#
To conclude this example, we will illustrate the applicability of batch sampling to the MDF formulation, including the computation of Jacobian data.
Let us consider the Sellar MDO problem whose disciplines have been vectorized
(look at the source code of Sellar1, Sellar2
and SellarSystem,
including the part related to the Jacobian computation with sparse arrays.).
We create a function defining and solving this MDO problem using either sequential or batch sampling:
def solve_sellar(
use_batch_sampling: bool, eval_jac: bool
) -> tuple[OptimizationDataset, float]:
"""Solve the Sellar sampling problem.
Args:
use_batch_sampling: Whether to use batch sampling.
eval_jac: Whether to sample the Jacobian functions.
Returns:
The samples, the execution time.
"""
disciplines = [Sellar1(), Sellar2(), SellarSystem()]
design_space = SellarDesignSpace(dtype="float")
scenario = MDOScenario(
disciplines, "obj", design_space, formulation_settings_model=MDF_Settings()
)
scenario.add_constraint("c_1")
scenario.add_constraint("c_2")
with Timer() as timer:
scenario.execute(
MC_Settings(n_samples=100, vectorize=use_batch_sampling, eval_jac=eval_jac)
)
return scenario.to_dataset(), timer.elapsed_time
solve it with both sampling modes:
samples_ref, time_ref = solve_sellar(False, False)
samples, time = solve_sellar(True, False)
INFO - 16:24:58: *** Start MDOScenario execution ***
INFO - 16:24:58: MDOScenario
INFO - 16:24:58: Disciplines: Sellar1 Sellar2 SellarSystem
INFO - 16:24:58: MDO formulation: MDF
INFO - 16:24:58: Optimization problem:
INFO - 16:24:58: minimize obj(x_1, x_2, x_shared)
INFO - 16:24:58: with respect to x_1, x_2, x_shared
INFO - 16:24:58: under the equality constraints
INFO - 16:24:58: c_1(x_1, x_2, x_shared) = 0
INFO - 16:24:58: c_2(x_1, x_2, x_shared) = 0
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | x_1 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_2 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_shared[0] | -10 | 4 | 10 | float |
INFO - 16:24:58: | x_shared[1] | 0 | 3 | 10 | float |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: Solving optimization problem with algorithm MC:
INFO - 16:24:58: 1%| | 1/100 [00:00<00:00, 100.56 it/sec, feas=False, obj=191]
INFO - 16:24:58: 2%|▏ | 2/100 [00:00<00:00, 147.78 it/sec, feas=False, obj=79.2]
INFO - 16:24:58: 3%|▎ | 3/100 [00:00<00:00, 176.62 it/sec, feas=False, obj=69.1]
INFO - 16:24:58: 4%|▍ | 4/100 [00:00<00:00, 197.59 it/sec, feas=False, obj=112]
INFO - 16:24:58: 5%|▌ | 5/100 [00:00<00:00, 217.18 it/sec, feas=False, obj=62.9]
INFO - 16:24:58: 6%|▌ | 6/100 [00:00<00:00, 227.97 it/sec, feas=False, obj=89.3]
INFO - 16:24:58: 7%|▋ | 7/100 [00:00<00:00, 241.85 it/sec, feas=False, obj=162]
INFO - 16:24:58: 8%|▊ | 8/100 [00:00<00:00, 247.30 it/sec, feas=False, obj=591]
INFO - 16:24:58: 9%|▉ | 9/100 [00:00<00:00, 251.85 it/sec, feas=False, obj=127]
INFO - 16:24:58: 10%|█ | 10/100 [00:00<00:00, 255.47 it/sec, feas=False, obj=80.9]
INFO - 16:24:58: 11%|█ | 11/100 [00:00<00:00, 261.97 it/sec, feas=False, obj=131]
INFO - 16:24:58: 12%|█▏ | 12/100 [00:00<00:00, 264.33 it/sec, feas=False, obj=119]
INFO - 16:24:58: 13%|█▎ | 13/100 [00:00<00:00, 265.92 it/sec, feas=False, obj=98.4]
INFO - 16:24:58: 14%|█▍ | 14/100 [00:00<00:00, 267.67 it/sec, feas=False, obj=1.09e+3]
INFO - 16:24:58: 15%|█▌ | 15/100 [00:00<00:00, 269.84 it/sec, feas=False, obj=220]
INFO - 16:24:58: 16%|█▌ | 16/100 [00:00<00:00, 271.51 it/sec, feas=False, obj=30.3]
INFO - 16:24:58: 17%|█▋ | 17/100 [00:00<00:00, 275.39 it/sec, feas=False, obj=130]
INFO - 16:24:58: 18%|█▊ | 18/100 [00:00<00:00, 279.01 it/sec, feas=False, obj=274]
INFO - 16:24:58: 19%|█▉ | 19/100 [00:00<00:00, 282.38 it/sec, feas=False, obj=111]
INFO - 16:24:58: 20%|██ | 20/100 [00:00<00:00, 283.38 it/sec, feas=False, obj=194]
INFO - 16:24:58: 21%|██ | 21/100 [00:00<00:00, 284.35 it/sec, feas=False, obj=87.1]
INFO - 16:24:58: 22%|██▏ | 22/100 [00:00<00:00, 284.58 it/sec, feas=False, obj=121]
INFO - 16:24:58: 23%|██▎ | 23/100 [00:00<00:00, 285.44 it/sec, feas=False, obj=44.7]
INFO - 16:24:58: 24%|██▍ | 24/100 [00:00<00:00, 287.42 it/sec, feas=False, obj=49.7]
INFO - 16:24:58: 25%|██▌ | 25/100 [00:00<00:00, 289.85 it/sec, feas=False, obj=62.2]
INFO - 16:24:58: 26%|██▌ | 26/100 [00:00<00:00, 290.57 it/sec, feas=False, obj=131]
INFO - 16:24:58: 27%|██▋ | 27/100 [00:00<00:00, 291.14 it/sec, feas=False, obj=89.7]
INFO - 16:24:58: 28%|██▊ | 28/100 [00:00<00:00, 293.21 it/sec, feas=False, obj=142]
INFO - 16:24:58: 29%|██▉ | 29/100 [00:00<00:00, 295.17 it/sec, feas=False, obj=85.4]
INFO - 16:24:58: 30%|███ | 30/100 [00:00<00:00, 295.03 it/sec, feas=False, obj=54.1]
INFO - 16:24:58: 31%|███ | 31/100 [00:00<00:00, 294.98 it/sec, feas=False, obj=138]
INFO - 16:24:58: 32%|███▏ | 32/100 [00:00<00:00, 296.56 it/sec, feas=False, obj=107]
INFO - 16:24:58: 33%|███▎ | 33/100 [00:00<00:00, 298.10 it/sec, feas=False, obj=45.6]
INFO - 16:24:58: 34%|███▍ | 34/100 [00:00<00:00, 298.15 it/sec, feas=False, obj=211]
INFO - 16:24:58: 35%|███▌ | 35/100 [00:00<00:00, 298.15 it/sec, feas=False, obj=149]
INFO - 16:24:58: 36%|███▌ | 36/100 [00:00<00:00, 299.81 it/sec, feas=False, obj=89.2]
INFO - 16:24:58: 37%|███▋ | 37/100 [00:00<00:00, 301.03 it/sec, feas=False, obj=125]
INFO - 16:24:58: 38%|███▊ | 38/100 [00:00<00:00, 300.59 it/sec, feas=False, obj=125]
INFO - 16:24:58: 39%|███▉ | 39/100 [00:00<00:00, 301.64 it/sec, feas=False, obj=156]
INFO - 16:24:58: 40%|████ | 40/100 [00:00<00:00, 302.70 it/sec, feas=False, obj=65.3]
INFO - 16:24:58: 41%|████ | 41/100 [00:00<00:00, 302.54 it/sec, feas=False, obj=134]
INFO - 16:24:58: 42%|████▏ | 42/100 [00:00<00:00, 302.51 it/sec, feas=False, obj=63.5]
INFO - 16:24:58: 43%|████▎ | 43/100 [00:00<00:00, 303.90 it/sec, feas=False, obj=137]
INFO - 16:24:58: 44%|████▍ | 44/100 [00:00<00:00, 305.22 it/sec, feas=False, obj=77.2]
INFO - 16:24:58: 45%|████▌ | 45/100 [00:00<00:00, 305.03 it/sec, feas=False, obj=116]
INFO - 16:24:58: 46%|████▌ | 46/100 [00:00<00:00, 306.11 it/sec, feas=False, obj=134]
INFO - 16:24:58: 47%|████▋ | 47/100 [00:00<00:00, 306.09 it/sec, feas=False, obj=110]
INFO - 16:24:58: 48%|████▊ | 48/100 [00:00<00:00, 306.14 it/sec, feas=False, obj=165]
INFO - 16:24:58: 49%|████▉ | 49/100 [00:00<00:00, 307.25 it/sec, feas=False, obj=189]
INFO - 16:24:58: 50%|█████ | 50/100 [00:00<00:00, 308.19 it/sec, feas=False, obj=109]
INFO - 16:24:58: 51%|█████ | 51/100 [00:00<00:00, 308.32 it/sec, feas=False, obj=95.5]
INFO - 16:24:58: 52%|█████▏ | 52/100 [00:00<00:00, 309.05 it/sec, feas=False, obj=146]
INFO - 16:24:58: 53%|█████▎ | 53/100 [00:00<00:00, 309.87 it/sec, feas=False, obj=165]
INFO - 16:24:58: 54%|█████▍ | 54/100 [00:00<00:00, 310.59 it/sec, feas=False, obj=107]
INFO - 16:24:58: 55%|█████▌ | 55/100 [00:00<00:00, 310.44 it/sec, feas=False, obj=25.3]
INFO - 16:24:58: 56%|█████▌ | 56/100 [00:00<00:00, 311.18 it/sec, feas=False, obj=203]
INFO - 16:24:58: 57%|█████▋ | 57/100 [00:00<00:00, 311.12 it/sec, feas=False, obj=305]
INFO - 16:24:58: 58%|█████▊ | 58/100 [00:00<00:00, 311.87 it/sec, feas=False, obj=51.7]
INFO - 16:24:58: 59%|█████▉ | 59/100 [00:00<00:00, 311.57 it/sec, feas=False, obj=212]
INFO - 16:24:58: 60%|██████ | 60/100 [00:00<00:00, 312.11 it/sec, feas=False, obj=199]
INFO - 16:24:58: 61%|██████ | 61/100 [00:00<00:00, 312.62 it/sec, feas=False, obj=93.1]
INFO - 16:24:58: 62%|██████▏ | 62/100 [00:00<00:00, 313.19 it/sec, feas=False, obj=39.9]
INFO - 16:24:58: 63%|██████▎ | 63/100 [00:00<00:00, 313.82 it/sec, feas=False, obj=112]
INFO - 16:24:58: 64%|██████▍ | 64/100 [00:00<00:00, 314.22 it/sec, feas=False, obj=136]
INFO - 16:24:58: 65%|██████▌ | 65/100 [00:00<00:00, 314.64 it/sec, feas=False, obj=40.7]
INFO - 16:24:58: 66%|██████▌ | 66/100 [00:00<00:00, 314.36 it/sec, feas=False, obj=2.66e+3]
INFO - 16:24:58: 67%|██████▋ | 67/100 [00:00<00:00, 315.00 it/sec, feas=False, obj=160]
INFO - 16:24:58: 68%|██████▊ | 68/100 [00:00<00:00, 315.56 it/sec, feas=False, obj=138]
INFO - 16:24:58: 69%|██████▉ | 69/100 [00:00<00:00, 315.33 it/sec, feas=False, obj=899]
INFO - 16:24:58: 70%|███████ | 70/100 [00:00<00:00, 316.05 it/sec, feas=False, obj=276]
INFO - 16:24:58: 71%|███████ | 71/100 [00:00<00:00, 315.91 it/sec, feas=False, obj=44.1]
INFO - 16:24:58: 72%|███████▏ | 72/100 [00:00<00:00, 315.47 it/sec, feas=False, obj=114]
INFO - 16:24:58: 73%|███████▎ | 73/100 [00:00<00:00, 315.88 it/sec, feas=False, obj=596]
INFO - 16:24:58: 74%|███████▍ | 74/100 [00:00<00:00, 315.55 it/sec, feas=False, obj=120]
INFO - 16:24:58: 75%|███████▌ | 75/100 [00:00<00:00, 315.96 it/sec, feas=False, obj=407]
INFO - 16:24:58: 76%|███████▌ | 76/100 [00:00<00:00, 315.72 it/sec, feas=False, obj=51.4]
INFO - 16:24:58: 77%|███████▋ | 77/100 [00:00<00:00, 315.41 it/sec, feas=False, obj=148]
INFO - 16:24:58: 78%|███████▊ | 78/100 [00:00<00:00, 315.04 it/sec, feas=False, obj=93.9]
INFO - 16:24:58: 79%|███████▉ | 79/100 [00:00<00:00, 314.82 it/sec, feas=False, obj=72.1]
INFO - 16:24:58: 80%|████████ | 80/100 [00:00<00:00, 315.43 it/sec, feas=False, obj=292]
INFO - 16:24:58: 81%|████████ | 81/100 [00:00<00:00, 315.24 it/sec, feas=False, obj=98.6]
INFO - 16:24:58: 82%|████████▏ | 82/100 [00:00<00:00, 315.07 it/sec, feas=False, obj=25]
INFO - 16:24:58: 83%|████████▎ | 83/100 [00:00<00:00, 314.98 it/sec, feas=False, obj=33.1]
INFO - 16:24:58: 84%|████████▍ | 84/100 [00:00<00:00, 314.78 it/sec, feas=False, obj=114]
INFO - 16:24:58: 85%|████████▌ | 85/100 [00:00<00:00, 314.43 it/sec, feas=False, obj=115]
INFO - 16:24:58: 86%|████████▌ | 86/100 [00:00<00:00, 314.88 it/sec, feas=False, obj=118]
INFO - 16:24:58: 87%|████████▋ | 87/100 [00:00<00:00, 314.70 it/sec, feas=False, obj=67.1]
INFO - 16:24:58: 88%|████████▊ | 88/100 [00:00<00:00, 315.07 it/sec, feas=False, obj=105]
INFO - 16:24:58: 89%|████████▉ | 89/100 [00:00<00:00, 315.72 it/sec, feas=False, obj=92.6]
INFO - 16:24:58: 90%|█████████ | 90/100 [00:00<00:00, 315.38 it/sec, feas=False, obj=98.1]
INFO - 16:24:58: 91%|█████████ | 91/100 [00:00<00:00, 315.01 it/sec, feas=False, obj=133]
INFO - 16:24:58: 92%|█████████▏| 92/100 [00:00<00:00, 315.32 it/sec, feas=False, obj=197]
INFO - 16:24:58: 93%|█████████▎| 93/100 [00:00<00:00, 315.58 it/sec, feas=False, obj=82.6]
INFO - 16:24:58: 94%|█████████▍| 94/100 [00:00<00:00, 315.33 it/sec, feas=False, obj=27.2]
INFO - 16:24:58: 95%|█████████▌| 95/100 [00:00<00:00, 314.94 it/sec, feas=False, obj=126]
INFO - 16:24:58: 96%|█████████▌| 96/100 [00:00<00:00, 314.74 it/sec, feas=False, obj=162]
INFO - 16:24:58: 97%|█████████▋| 97/100 [00:00<00:00, 315.06 it/sec, feas=False, obj=45.7]
INFO - 16:24:58: 98%|█████████▊| 98/100 [00:00<00:00, 314.89 it/sec, feas=False, obj=21.2]
INFO - 16:24:58: 99%|█████████▉| 99/100 [00:00<00:00, 314.78 it/sec, feas=False, obj=342]
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:24:58: 100%|██████████| 100/100 [00:00<00:00, 312.61 it/sec, feas=False, obj=125]
INFO - 16:24:58: Optimization result:
INFO - 16:24:58: Optimizer info:
INFO - 16:24:58: Status: None
INFO - 16:24:58: Message: None
INFO - 16:24:58: Solution:
WARNING - 16:24:58: The solution is not feasible.
INFO - 16:24:58: Objective: 30.3484674529711
INFO - 16:24:58: Standardized constraints:
INFO - 16:24:58: c_1 = -12.474091450044018
INFO - 16:24:58: c_2 = -10.003409829769815
INFO - 16:24:58: Design space:
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: | x_1 | 0 | 2.740483886137183 | 10 | float |
INFO - 16:24:58: | x_2 | 0 | 0.07091828603166261 | 10 | float |
INFO - 16:24:58: | x_shared[0] | -10 | 2.914417911498957 | 10 | float |
INFO - 16:24:58: | x_shared[1] | 0 | 7.199093835086931 | 10 | float |
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: *** End MDOScenario execution (time: 0:00:00.322789) ***
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:24:58: *** Start MDOScenario execution ***
INFO - 16:24:58: MDOScenario
INFO - 16:24:58: Disciplines: Sellar1 Sellar2 SellarSystem
INFO - 16:24:58: MDO formulation: MDF
INFO - 16:24:58: Optimization problem:
INFO - 16:24:58: minimize obj(x_1, x_2, x_shared)
INFO - 16:24:58: with respect to x_1, x_2, x_shared
INFO - 16:24:58: under the equality constraints
INFO - 16:24:58: c_1(x_1, x_2, x_shared) = 0
INFO - 16:24:58: c_2(x_1, x_2, x_shared) = 0
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | x_1 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_2 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_shared[0] | -10 | 4 | 10 | float |
INFO - 16:24:58: | x_shared[1] | 0 | 3 | 10 | float |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: Solving optimization problem with algorithm MC:
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:24:58: Optimization result:
INFO - 16:24:58: Optimizer info:
INFO - 16:24:58: Status: None
INFO - 16:24:58: Message: None
INFO - 16:24:58: Solution:
WARNING - 16:24:58: The solution is not feasible.
INFO - 16:24:58: Objective: 30.348468138942017
INFO - 16:24:58: Standardized constraints:
INFO - 16:24:58: c_1 = [-12.47409214]
INFO - 16:24:58: c_2 = [-10.00340912]
INFO - 16:24:58: Design space:
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: | x_1 | 0 | 2.740483886137183 | 10 | float |
INFO - 16:24:58: | x_2 | 0 | 0.07091828603166261 | 10 | float |
INFO - 16:24:58: | x_shared[0] | -10 | 2.914417911498957 | 10 | float |
INFO - 16:24:58: | x_shared[1] | 0 | 7.199093835086931 | 10 | float |
INFO - 16:24:58: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:58: *** End MDOScenario execution (time: 0:00:00.023397) ***
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
WARNING - 16:24:58: Optimization found no feasible point; the least infeasible point is selected.
check that the samples are equal:
assert_frame_equal(samples, samples_ref, rtol=1e-3)
and see a significant execution time reduction:
f"{round((time_ref - time) / time_ref * 100)} %"
'93 %'
The same type of results are obtained by evaluating the gradients.
samples_ref, time_ref = solve_sellar(False, True)
samples, time = solve_sellar(True, True)
assert_frame_equal(samples, samples_ref, rtol=1e-3)
INFO - 16:24:58: *** Start MDOScenario execution ***
INFO - 16:24:58: MDOScenario
INFO - 16:24:58: Disciplines: Sellar1 Sellar2 SellarSystem
INFO - 16:24:58: MDO formulation: MDF
INFO - 16:24:58: Optimization problem:
INFO - 16:24:58: minimize obj(x_1, x_2, x_shared)
INFO - 16:24:58: with respect to x_1, x_2, x_shared
INFO - 16:24:58: under the equality constraints
INFO - 16:24:58: c_1(x_1, x_2, x_shared) = 0
INFO - 16:24:58: c_2(x_1, x_2, x_shared) = 0
INFO - 16:24:58: over the design space:
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: | x_1 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_2 | 0 | 1 | 10 | float |
INFO - 16:24:58: | x_shared[0] | -10 | 4 | 10 | float |
INFO - 16:24:58: | x_shared[1] | 0 | 3 | 10 | float |
INFO - 16:24:58: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:58: Solving optimization problem with algorithm MC:
INFO - 16:24:59: 1%| | 1/100 [00:00<00:01, 57.78 it/sec, feas=False, obj=191]
INFO - 16:24:59: 2%|▏ | 2/100 [00:00<00:01, 73.21 it/sec, feas=False, obj=79.2]
INFO - 16:24:59: 3%|▎ | 3/100 [00:00<00:01, 81.82 it/sec, feas=False, obj=69.1]
INFO - 16:24:59: 4%|▍ | 4/100 [00:00<00:01, 86.92 it/sec, feas=False, obj=112]
INFO - 16:24:59: 5%|▌ | 5/100 [00:00<00:01, 90.74 it/sec, feas=False, obj=62.9]
INFO - 16:24:59: 6%|▌ | 6/100 [00:00<00:01, 93.13 it/sec, feas=False, obj=89.3]
INFO - 16:24:59: 7%|▋ | 7/100 [00:00<00:00, 95.61 it/sec, feas=False, obj=162]
INFO - 16:24:59: 8%|▊ | 8/100 [00:00<00:00, 96.82 it/sec, feas=False, obj=591]
INFO - 16:24:59: 9%|▉ | 9/100 [00:00<00:00, 97.92 it/sec, feas=False, obj=127]
INFO - 16:24:59: 10%|█ | 10/100 [00:00<00:00, 98.83 it/sec, feas=False, obj=80.9]
INFO - 16:24:59: 11%|█ | 11/100 [00:00<00:00, 100.02 it/sec, feas=False, obj=131]
INFO - 16:24:59: 12%|█▏ | 12/100 [00:00<00:00, 100.51 it/sec, feas=False, obj=119]
INFO - 16:24:59: 13%|█▎ | 13/100 [00:00<00:00, 100.93 it/sec, feas=False, obj=98.4]
INFO - 16:24:59: 14%|█▍ | 14/100 [00:00<00:00, 101.32 it/sec, feas=False, obj=1.09e+3]
INFO - 16:24:59: 15%|█▌ | 15/100 [00:00<00:00, 101.63 it/sec, feas=False, obj=220]
INFO - 16:24:59: 16%|█▌ | 16/100 [00:00<00:00, 101.83 it/sec, feas=False, obj=30.3]
INFO - 16:24:59: 17%|█▋ | 17/100 [00:00<00:00, 102.46 it/sec, feas=False, obj=130]
INFO - 16:24:59: 18%|█▊ | 18/100 [00:00<00:00, 103.05 it/sec, feas=False, obj=274]
INFO - 16:24:59: 19%|█▉ | 19/100 [00:00<00:00, 103.51 it/sec, feas=False, obj=111]
INFO - 16:24:59: 20%|██ | 20/100 [00:00<00:00, 103.70 it/sec, feas=False, obj=194]
INFO - 16:24:59: 21%|██ | 21/100 [00:00<00:00, 103.89 it/sec, feas=False, obj=87.1]
INFO - 16:24:59: 22%|██▏ | 22/100 [00:00<00:00, 104.07 it/sec, feas=False, obj=121]
INFO - 16:24:59: 23%|██▎ | 23/100 [00:00<00:00, 104.27 it/sec, feas=False, obj=44.7]
INFO - 16:24:59: 24%|██▍ | 24/100 [00:00<00:00, 104.68 it/sec, feas=False, obj=49.7]
INFO - 16:24:59: 25%|██▌ | 25/100 [00:00<00:00, 104.90 it/sec, feas=False, obj=62.2]
INFO - 16:24:59: 26%|██▌ | 26/100 [00:00<00:00, 105.00 it/sec, feas=False, obj=131]
INFO - 16:24:59: 27%|██▋ | 27/100 [00:00<00:00, 105.10 it/sec, feas=False, obj=89.7]
INFO - 16:24:59: 28%|██▊ | 28/100 [00:00<00:00, 105.38 it/sec, feas=False, obj=142]
INFO - 16:24:59: 29%|██▉ | 29/100 [00:00<00:00, 105.56 it/sec, feas=False, obj=85.4]
INFO - 16:24:59: 30%|███ | 30/100 [00:00<00:00, 105.59 it/sec, feas=False, obj=54.1]
INFO - 16:24:59: 31%|███ | 31/100 [00:00<00:00, 105.55 it/sec, feas=False, obj=138]
INFO - 16:24:59: 32%|███▏ | 32/100 [00:00<00:00, 105.75 it/sec, feas=False, obj=107]
INFO - 16:24:59: 33%|███▎ | 33/100 [00:00<00:00, 106.01 it/sec, feas=False, obj=45.6]
INFO - 16:24:59: 34%|███▍ | 34/100 [00:00<00:00, 106.05 it/sec, feas=False, obj=211]
INFO - 16:24:59: 35%|███▌ | 35/100 [00:00<00:00, 106.10 it/sec, feas=False, obj=149]
INFO - 16:24:59: 36%|███▌ | 36/100 [00:00<00:00, 106.31 it/sec, feas=False, obj=89.2]
INFO - 16:24:59: 37%|███▋ | 37/100 [00:00<00:00, 106.55 it/sec, feas=False, obj=125]
INFO - 16:24:59: 38%|███▊ | 38/100 [00:00<00:00, 106.55 it/sec, feas=False, obj=125]
INFO - 16:24:59: 39%|███▉ | 39/100 [00:00<00:00, 106.69 it/sec, feas=False, obj=156]
INFO - 16:24:59: 40%|████ | 40/100 [00:00<00:00, 106.84 it/sec, feas=False, obj=65.3]
INFO - 16:24:59: 41%|████ | 41/100 [00:00<00:00, 106.85 it/sec, feas=False, obj=134]
INFO - 16:24:59: 42%|████▏ | 42/100 [00:00<00:00, 106.87 it/sec, feas=False, obj=63.5]
INFO - 16:24:59: 43%|████▎ | 43/100 [00:00<00:00, 107.02 it/sec, feas=False, obj=137]
INFO - 16:24:59: 44%|████▍ | 44/100 [00:00<00:00, 107.10 it/sec, feas=False, obj=77.2]
INFO - 16:24:59: 45%|████▌ | 45/100 [00:00<00:00, 107.09 it/sec, feas=False, obj=116]
INFO - 16:24:59: 46%|████▌ | 46/100 [00:00<00:00, 107.23 it/sec, feas=False, obj=134]
INFO - 16:24:59: 47%|████▋ | 47/100 [00:00<00:00, 107.11 it/sec, feas=False, obj=110]
INFO - 16:24:59: 48%|████▊ | 48/100 [00:00<00:00, 107.09 it/sec, feas=False, obj=165]
INFO - 16:24:59: 49%|████▉ | 49/100 [00:00<00:00, 107.21 it/sec, feas=False, obj=189]
INFO - 16:24:59: 50%|█████ | 50/100 [00:00<00:00, 107.25 it/sec, feas=False, obj=109]
INFO - 16:24:59: 51%|█████ | 51/100 [00:00<00:00, 107.27 it/sec, feas=False, obj=95.5]
INFO - 16:24:59: 52%|█████▏ | 52/100 [00:00<00:00, 107.39 it/sec, feas=False, obj=146]
INFO - 16:24:59: 53%|█████▎ | 53/100 [00:00<00:00, 107.53 it/sec, feas=False, obj=165]
INFO - 16:24:59: 54%|█████▍ | 54/100 [00:00<00:00, 107.66 it/sec, feas=False, obj=107]
INFO - 16:24:59: 55%|█████▌ | 55/100 [00:00<00:00, 107.66 it/sec, feas=False, obj=25.3]
INFO - 16:24:59: 56%|█████▌ | 56/100 [00:00<00:00, 107.76 it/sec, feas=False, obj=203]
INFO - 16:24:59: 57%|█████▋ | 57/100 [00:00<00:00, 107.73 it/sec, feas=False, obj=305]
INFO - 16:24:59: 58%|█████▊ | 58/100 [00:00<00:00, 107.84 it/sec, feas=False, obj=51.7]
INFO - 16:24:59: 59%|█████▉ | 59/100 [00:00<00:00, 107.83 it/sec, feas=False, obj=212]
INFO - 16:24:59: 60%|██████ | 60/100 [00:00<00:00, 107.95 it/sec, feas=False, obj=199]
INFO - 16:24:59: 61%|██████ | 61/100 [00:00<00:00, 108.05 it/sec, feas=False, obj=93.1]
INFO - 16:24:59: 62%|██████▏ | 62/100 [00:00<00:00, 108.11 it/sec, feas=False, obj=39.9]
INFO - 16:24:59: 63%|██████▎ | 63/100 [00:00<00:00, 108.19 it/sec, feas=False, obj=112]
INFO - 16:24:59: 64%|██████▍ | 64/100 [00:00<00:00, 108.27 it/sec, feas=False, obj=136]
INFO - 16:24:59: 65%|██████▌ | 65/100 [00:00<00:00, 108.35 it/sec, feas=False, obj=40.7]
INFO - 16:24:59: 66%|██████▌ | 66/100 [00:00<00:00, 108.27 it/sec, feas=False, obj=2.66e+3]
INFO - 16:24:59: 67%|██████▋ | 67/100 [00:00<00:00, 108.34 it/sec, feas=False, obj=160]
INFO - 16:24:59: 68%|██████▊ | 68/100 [00:00<00:00, 108.41 it/sec, feas=False, obj=138]
INFO - 16:24:59: 69%|██████▉ | 69/100 [00:00<00:00, 108.40 it/sec, feas=False, obj=899]
INFO - 16:24:59: 70%|███████ | 70/100 [00:00<00:00, 108.48 it/sec, feas=False, obj=276]
INFO - 16:24:59: 71%|███████ | 71/100 [00:00<00:00, 108.49 it/sec, feas=False, obj=44.1]
INFO - 16:24:59: 72%|███████▏ | 72/100 [00:00<00:00, 108.50 it/sec, feas=False, obj=114]
INFO - 16:24:59: 73%|███████▎ | 73/100 [00:00<00:00, 108.56 it/sec, feas=False, obj=596]
INFO - 16:24:59: 74%|███████▍ | 74/100 [00:00<00:00, 108.53 it/sec, feas=False, obj=120]
INFO - 16:24:59: 75%|███████▌ | 75/100 [00:00<00:00, 108.59 it/sec, feas=False, obj=407]
INFO - 16:24:59: 76%|███████▌ | 76/100 [00:00<00:00, 108.59 it/sec, feas=False, obj=51.4]
INFO - 16:24:59: 77%|███████▋ | 77/100 [00:00<00:00, 108.57 it/sec, feas=False, obj=148]
INFO - 16:24:59: 78%|███████▊ | 78/100 [00:00<00:00, 108.58 it/sec, feas=False, obj=93.9]
INFO - 16:24:59: 79%|███████▉ | 79/100 [00:00<00:00, 108.57 it/sec, feas=False, obj=72.1]
INFO - 16:24:59: 80%|████████ | 80/100 [00:00<00:00, 108.60 it/sec, feas=False, obj=292]
INFO - 16:24:59: 81%|████████ | 81/100 [00:00<00:00, 108.55 it/sec, feas=False, obj=98.6]
INFO - 16:24:59: 82%|████████▏ | 82/100 [00:00<00:00, 108.52 it/sec, feas=False, obj=25]
INFO - 16:24:59: 83%|████████▎ | 83/100 [00:00<00:00, 108.48 it/sec, feas=False, obj=33.1]
INFO - 16:24:59: 84%|████████▍ | 84/100 [00:00<00:00, 108.38 it/sec, feas=False, obj=114]
INFO - 16:24:59: 85%|████████▌ | 85/100 [00:00<00:00, 108.36 it/sec, feas=False, obj=115]
INFO - 16:24:59: 86%|████████▌ | 86/100 [00:00<00:00, 108.40 it/sec, feas=False, obj=118]
INFO - 16:24:59: 87%|████████▋ | 87/100 [00:00<00:00, 108.35 it/sec, feas=False, obj=67.1]
INFO - 16:24:59: 88%|████████▊ | 88/100 [00:00<00:00, 108.39 it/sec, feas=False, obj=105]
INFO - 16:24:59: 89%|████████▉ | 89/100 [00:00<00:00, 108.44 it/sec, feas=False, obj=92.6]
INFO - 16:24:59: 90%|█████████ | 90/100 [00:00<00:00, 108.11 it/sec, feas=False, obj=98.1]
INFO - 16:24:59: 91%|█████████ | 91/100 [00:00<00:00, 107.92 it/sec, feas=False, obj=133]
INFO - 16:24:59: 92%|█████████▏| 92/100 [00:00<00:00, 107.97 it/sec, feas=False, obj=197]
INFO - 16:24:59: 93%|█████████▎| 93/100 [00:00<00:00, 107.80 it/sec, feas=False, obj=82.6]
INFO - 16:24:59: 94%|█████████▍| 94/100 [00:00<00:00, 107.79 it/sec, feas=False, obj=27.2]
INFO - 16:24:59: 95%|█████████▌| 95/100 [00:00<00:00, 107.79 it/sec, feas=False, obj=126]
INFO - 16:24:59: 96%|█████████▌| 96/100 [00:00<00:00, 107.79 it/sec, feas=False, obj=162]
INFO - 16:24:59: 97%|█████████▋| 97/100 [00:00<00:00, 107.83 it/sec, feas=False, obj=45.7]
INFO - 16:24:59: 98%|█████████▊| 98/100 [00:00<00:00, 107.81 it/sec, feas=False, obj=21.2]
INFO - 16:24:59: 99%|█████████▉| 99/100 [00:00<00:00, 107.82 it/sec, feas=False, obj=342]
WARNING - 16:24:59: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:24:59: 100%|██████████| 100/100 [00:00<00:00, 107.58 it/sec, feas=False, obj=125]
INFO - 16:24:59: Optimization result:
INFO - 16:24:59: Optimizer info:
INFO - 16:24:59: Status: None
INFO - 16:24:59: Message: None
INFO - 16:24:59: Solution:
WARNING - 16:24:59: The solution is not feasible.
INFO - 16:24:59: Objective: 30.3484674529711
INFO - 16:24:59: Standardized constraints:
INFO - 16:24:59: c_1 = -12.474091450044018
INFO - 16:24:59: c_2 = -10.003409829769815
INFO - 16:24:59: Design space:
INFO - 16:24:59: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:59: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:59: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:59: | x_1 | 0 | 2.740483886137183 | 10 | float |
INFO - 16:24:59: | x_2 | 0 | 0.07091828603166261 | 10 | float |
INFO - 16:24:59: | x_shared[0] | -10 | 2.914417911498957 | 10 | float |
INFO - 16:24:59: | x_shared[1] | 0 | 7.199093835086931 | 10 | float |
INFO - 16:24:59: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:24:59: *** End MDOScenario execution (time: 0:00:00.932318) ***
WARNING - 16:24:59: Optimization found no feasible point; the least infeasible point is selected.
WARNING - 16:24:59: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:24:59: *** Start MDOScenario execution ***
INFO - 16:24:59: MDOScenario
INFO - 16:24:59: Disciplines: Sellar1 Sellar2 SellarSystem
INFO - 16:24:59: MDO formulation: MDF
INFO - 16:24:59: Optimization problem:
INFO - 16:24:59: minimize obj(x_1, x_2, x_shared)
INFO - 16:24:59: with respect to x_1, x_2, x_shared
INFO - 16:24:59: under the equality constraints
INFO - 16:24:59: c_1(x_1, x_2, x_shared) = 0
INFO - 16:24:59: c_2(x_1, x_2, x_shared) = 0
INFO - 16:24:59: over the design space:
INFO - 16:24:59: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:59: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:24:59: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:59: | x_1 | 0 | 1 | 10 | float |
INFO - 16:24:59: | x_2 | 0 | 1 | 10 | float |
INFO - 16:24:59: | x_shared[0] | -10 | 4 | 10 | float |
INFO - 16:24:59: | x_shared[1] | 0 | 3 | 10 | float |
INFO - 16:24:59: +-------------+-------------+-------+-------------+-------+
INFO - 16:24:59: Solving optimization problem with algorithm MC:
WARNING - 16:25:00: Optimization found no feasible point; the least infeasible point is selected.
INFO - 16:25:00: Optimization result:
INFO - 16:25:00: Optimizer info:
INFO - 16:25:00: Status: None
INFO - 16:25:00: Message: None
INFO - 16:25:00: Solution:
WARNING - 16:25:00: The solution is not feasible.
INFO - 16:25:00: Objective: 30.348468138942017
INFO - 16:25:00: Standardized constraints:
INFO - 16:25:00: c_1 = [-12.47409214]
INFO - 16:25:00: c_2 = [-10.00340912]
INFO - 16:25:00: Design space:
INFO - 16:25:00: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:25:00: | Name | Lower bound | Value | Upper bound | Type |
INFO - 16:25:00: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:25:00: | x_1 | 0 | 2.740483886137183 | 10 | float |
INFO - 16:25:00: | x_2 | 0 | 0.07091828603166261 | 10 | float |
INFO - 16:25:00: | x_shared[0] | -10 | 2.914417911498957 | 10 | float |
INFO - 16:25:00: | x_shared[1] | 0 | 7.199093835086931 | 10 | float |
INFO - 16:25:00: +-------------+-------------+---------------------+-------------+-------+
INFO - 16:25:00: *** End MDOScenario execution (time: 0:00:00.309145) ***
WARNING - 16:25:00: Optimization found no feasible point; the least infeasible point is selected.
WARNING - 16:25:00: Optimization found no feasible point; the least infeasible point is selected.
However, the gain is slightly lower (but still represents a significant gain!), which may be counterintuitive and will be the subject of future investigations:
f"{round((time_ref - time) / time_ref * 100)} %"
'67 %'
Total running time of the script: (0 minutes 2.061 seconds)