.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/formulations/plot_sobieski_bilevel_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_formulations_plot_sobieski_bilevel_example.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_formulations_plot_sobieski_bilevel_example.py:


BiLevel-based MDO on the Sobieski SSBJ test case
================================================

.. GENERATED FROM PYTHON SOURCE LINES 24-40

.. code-block:: Python


    from __future__ import annotations

    from copy import deepcopy
    from logging import WARNING

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo import execute_post
    from gemseo.algos.opt.nlopt.settings.nlopt_cobyla_settings import NLOPT_COBYLA_Settings
    from gemseo.algos.opt.scipy_local.settings.slsqp import SLSQP_Settings
    from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace

    configure_logger()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 41-48

Instantiate the  disciplines
----------------------------
First, we instantiate the four disciplines of the use case:
:class:`.SobieskiPropulsion`,
:class:`.SobieskiAerodynamics`,
:class:`.SobieskiMission`
and :class:`.SobieskiStructure`.

.. GENERATED FROM PYTHON SOURCE LINES 48-55

.. code-block:: Python

    propu, aero, mission, struct = create_discipline([
        "SobieskiPropulsion",
        "SobieskiAerodynamics",
        "SobieskiMission",
        "SobieskiStructure",
    ])








.. GENERATED FROM PYTHON SOURCE LINES 56-64

Build, execute and post-process the scenario
--------------------------------------------
Then, we build the scenario which links the disciplines
with the formulation and the optimization algorithm. Here, we use the
:class:`.BiLevel` formulation. We tell the scenario to minimize -y_4
instead of minimizing y_4 (range), which is the default option.

We need to define the design space.

.. GENERATED FROM PYTHON SOURCE LINES 64-66

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. GENERATED FROM PYTHON SOURCE LINES 67-70

Then, we build a sub-scenario for each strongly coupled disciplines,
using the following algorithm, maximum number of iterations and
algorithm settings:

.. GENERATED FROM PYTHON SOURCE LINES 70-89

.. code-block:: Python


    slsqp_settings = SLSQP_Settings(
        max_iter=30,
        xtol_rel=1e-7,
        xtol_abs=1e-7,
        ftol_rel=1e-7,
        ftol_abs=1e-7,
        ineq_tolerance=1e-4,
    )

    cobyla_settings = NLOPT_COBYLA_Settings(
        max_iter=50,
        xtol_rel=1e-7,
        xtol_abs=1e-7,
        ftol_rel=1e-7,
        ftol_abs=1e-7,
        ineq_tolerance=1e-4,
    )








.. GENERATED FROM PYTHON SOURCE LINES 90-93

Build a sub-scenario for Propulsion
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This sub-scenario will minimize SFC.

.. GENERATED FROM PYTHON SOURCE LINES 93-103

.. code-block:: Python

    sc_prop = create_scenario(
        propu,
        "y_34",
        design_space.filter("x_3", copy=True),
        name="PropulsionScenario",
        formulation_name="DisciplinaryOpt",
    )
    sc_prop.set_algorithm(slsqp_settings)
    sc_prop.add_constraint("g_3", constraint_type="ineq")








.. GENERATED FROM PYTHON SOURCE LINES 104-107

Build a sub-scenario for Aerodynamics
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This sub-scenario will minimize L/D.

.. GENERATED FROM PYTHON SOURCE LINES 107-118

.. code-block:: Python

    sc_aero = create_scenario(
        aero,
        "y_24",
        design_space.filter("x_2", copy=True),
        name="AerodynamicsScenario",
        maximize_objective=True,
        formulation_name="DisciplinaryOpt",
    )
    sc_aero.set_algorithm(slsqp_settings)
    sc_aero.add_constraint("g_2", constraint_type="ineq")








.. GENERATED FROM PYTHON SOURCE LINES 119-123

Build a sub-scenario for Structure
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This sub-scenario will maximize
log(aircraft total weight / (aircraft total weight - fuel weight)).

.. GENERATED FROM PYTHON SOURCE LINES 123-134

.. code-block:: Python

    sc_str = create_scenario(
        struct,
        "y_11",
        design_space.filter("x_1", copy=True),
        name="StructureScenario",
        maximize_objective=True,
        formulation_name="DisciplinaryOpt",
    )
    sc_str.add_constraint("g_1", constraint_type="ineq")
    sc_str.set_algorithm(slsqp_settings)








.. GENERATED FROM PYTHON SOURCE LINES 135-139

Build a scenario for Mission
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This scenario is based on the three previous sub-scenarios and on the
Mission and aims to maximize the range (Breguet).

.. GENERATED FROM PYTHON SOURCE LINES 139-153

.. code-block:: Python

    system_scenario = create_scenario(
        [sc_prop, sc_aero, sc_str, mission],
        "y_4",
        design_space.filter("x_shared", copy=True),
        apply_cstr_tosub_scenarios=False,
        parallel_scenarios=False,
        multithread_scenarios=True,
        main_mda_settings={"tolerance": 1e-14, "max_mda_iter": 30},
        maximize_objective=True,
        sub_scenarios_log_level=WARNING,
        formulation_name="BiLevel",
    )
    system_scenario.add_constraint(["g_1", "g_2", "g_3"], constraint_type="ineq")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

     WARNING - 08:36:38: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
     WARNING - 08:36:38: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.




.. GENERATED FROM PYTHON SOURCE LINES 154-161

Visualize the XDSM
^^^^^^^^^^^^^^^^^^
Generate the XDSM on the fly:

- ``log_workflow_status=True`` will log the status of the workflow  in the console,
- ``save_html`` (default ``True``) will generate a self-contained HTML file,
  that can be automatically opened using ``show_html=True``.

.. GENERATED FROM PYTHON SOURCE LINES 161-163

.. code-block:: Python

    system_scenario.xdsmize(save_html=False, pdf_build=False)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

        <script type="text/javascript">
            document.addEventListener('DOMContentLoaded', () => {
              const mdo = {
      "root": {
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            "id": "Opt",
            "name": "Optimizer",
            "type": "optimization",
            "status": "DONE"
          },
          {
            "id": "Dis1",
            "name": "MDAJacobi",
            "type": "mda",
            "status": "DONE"
          },
          {
            "id": "Dis2",
            "name": "SobieskiPropulsion",
            "type": "analysis",
            "status": "DONE"
          },
          {
            "id": "Dis3",
            "name": "SobieskiAerodynamics",
            "type": "analysis",
            "status": "DONE"
          },
          {
            "id": "Dis4",
            "name": "SobieskiStructure",
            "type": "analysis",
            "status": "DONE"
          },
          {
            "id": "Dis5",
            "name": "PropulsionScenario_scn-1-1",
            "type": "mdo",
            "subxdsm": "PropulsionScenario_scn-1-1",
            "status": "DONE"
          },
          {
            "id": "Dis6",
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            "type": "mdo",
            "subxdsm": "AerodynamicsScenario_scn-1-2",
            "status": "DONE"
          },
          {
            "id": "Dis7",
            "name": "StructureScenario_scn-1-3",
            "type": "mdo",
            "subxdsm": "StructureScenario_scn-1-3",
            "status": "DONE"
          },
          {
            "id": "Dis8",
            "name": "MDAJacobi",
            "type": "mda",
            "status": "DONE"
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          },
          {
            "from": "Dis3",
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          {
            "from": "Dis4",
            "to": "Opt",
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          {
            "from": "Dis3",
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          },
          {
            "from": "Opt",
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            "name": "x_shared"
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          {
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          },
          {
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              "Dis5",
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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 164-166

Execute the main scenario
^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 166-168

.. code-block:: Python

    system_scenario.execute(cobyla_settings)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 08:36:38:  
        INFO - 08:36:38: *** Start MDOScenario execution ***
        INFO - 08:36:38: MDOScenario
        INFO - 08:36:38:    Disciplines: AerodynamicsScenario PropulsionScenario SobieskiMission StructureScenario
        INFO - 08:36:38:    MDO formulation: BiLevel
        INFO - 08:36:38: Optimization problem:
        INFO - 08:36:38:    minimize -y_4(x_shared)
        INFO - 08:36:38:    with respect to x_shared
        INFO - 08:36:38:    subject to constraints:
        INFO - 08:36:38:       g_1_g_2_g_3(x_shared) <= 0
        INFO - 08:36:38:    over the design space:
        INFO - 08:36:38:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:36:38:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:36:38:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:36:38:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 08:36:38:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 08:36:38:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 08:36:38:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 08:36:38:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 08:36:38:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 08:36:38:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:36:38: Solving optimization problem with algorithm NLOPT_COBYLA:
        INFO - 08:36:38:      2%|▏         | 1/50 [00:00<00:09,  5.03 it/sec, obj=-553]
     WARNING - 08:36:38: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:38:       The solution is not feasible.
        INFO - 08:36:38:      4%|▍         | 2/50 [00:00<00:09,  5.01 it/sec, obj=-574]
     WARNING - 08:36:38: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:38:       The solution is not feasible.
        INFO - 08:36:38:      6%|▌         | 3/50 [00:00<00:09,  5.05 it/sec, obj=-813]
     WARNING - 08:36:38: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:38:       The solution is not feasible.
        INFO - 08:36:39:      8%|▊         | 4/50 [00:00<00:09,  4.92 it/sec, obj=-751]
     WARNING - 08:36:39: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:39:       The solution is not feasible.
        INFO - 08:36:39:     10%|█         | 5/50 [00:01<00:10,  4.48 it/sec, obj=-734]
     WARNING - 08:36:39: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:39:       The solution is not feasible.
        INFO - 08:36:39:     12%|█▏        | 6/50 [00:01<00:09,  4.61 it/sec, obj=-977]
     WARNING - 08:36:39: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:39:       The solution is not feasible.
        INFO - 08:36:39:     14%|█▍        | 7/50 [00:01<00:09,  4.46 it/sec, obj=-1.05e+3]
     WARNING - 08:36:39: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:39:       The solution is not feasible.
        INFO - 08:36:40:     16%|█▌        | 8/50 [00:01<00:09,  4.43 it/sec, obj=-1.67e+3]
        INFO - 08:36:40:     18%|█▊        | 9/50 [00:02<00:09,  4.42 it/sec, obj=-1.73e+3]
     WARNING - 08:36:40: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:40:       The solution is not feasible.
        INFO - 08:36:40:     20%|██        | 10/50 [00:02<00:09,  4.36 it/sec, obj=-2.59e+3]
        INFO - 08:36:40:     22%|██▏       | 11/50 [00:02<00:08,  4.36 it/sec, obj=-2.94e+3]
     WARNING - 08:36:40: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:40:       The solution is not feasible.
        INFO - 08:36:41:     24%|██▍       | 12/50 [00:02<00:08,  4.28 it/sec, obj=-2.39e+3]
        INFO - 08:36:41:     26%|██▌       | 13/50 [00:03<00:08,  4.28 it/sec, obj=-2.66e+3]
     WARNING - 08:36:41: MDAJacobi has reached its maximum number of iterations but the normed residual 4.00581977874369e-13 is still above the tolerance 1e-14.
        INFO - 08:36:41:     28%|██▊       | 14/50 [00:03<00:08,  4.26 it/sec, obj=-2.54e+3]
     WARNING - 08:36:41: MDAJacobi has reached its maximum number of iterations but the normed residual 2.6870836615612218e-14 is still above the tolerance 1e-14.
        INFO - 08:36:41:     30%|███       | 15/50 [00:03<00:08,  4.19 it/sec, obj=-2.51e+3]
     WARNING - 08:36:42: MDAJacobi has reached its maximum number of iterations but the normed residual 2.6870836615612218e-14 is still above the tolerance 1e-14.
        INFO - 08:36:42:     32%|███▏      | 16/50 [00:03<00:08,  4.14 it/sec, obj=-2.2e+3]
        INFO - 08:36:42:     34%|███▍      | 17/50 [00:04<00:07,  4.16 it/sec, obj=-2.76e+3]
        INFO - 08:36:42:     36%|███▌      | 18/50 [00:04<00:07,  4.13 it/sec, obj=-2.47e+3]
     WARNING - 08:36:42: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:42:       The solution is not feasible.
        INFO - 08:36:42:     38%|███▊      | 19/50 [00:04<00:07,  4.13 it/sec, obj=-2.97e+3]
        INFO - 08:36:43:     40%|████      | 20/50 [00:04<00:07,  4.16 it/sec, obj=-2.51e+3]
        INFO - 08:36:43:     42%|████▏     | 21/50 [00:04<00:06,  4.21 it/sec, obj=-2.85e+3]
        INFO - 08:36:43:     44%|████▍     | 22/50 [00:05<00:06,  4.21 it/sec, obj=-2.99e+3]
        INFO - 08:36:43:     46%|████▌     | 23/50 [00:05<00:06,  4.24 it/sec, obj=-2.7e+3]
        INFO - 08:36:43:     48%|████▊     | 24/50 [00:05<00:06,  4.25 it/sec, obj=-3.1e+3]
        INFO - 08:36:44:     50%|█████     | 25/50 [00:05<00:05,  4.31 it/sec, obj=-2.8e+3]
        INFO - 08:36:44:     52%|█████▏    | 26/50 [00:06<00:05,  4.28 it/sec, obj=-3.07e+3]
        INFO - 08:36:44:     54%|█████▍    | 27/50 [00:06<00:05,  4.32 it/sec, obj=-3.15e+3]
     WARNING - 08:36:44: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:44:       The solution is not feasible.
        INFO - 08:36:44:     56%|█████▌    | 28/50 [00:06<00:05,  4.28 it/sec, obj=-2.8e+3]
     WARNING - 08:36:44: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:44:       The solution is not feasible.
     WARNING - 08:36:45: MDAJacobi has reached its maximum number of iterations but the normed residual 2.6870836615612218e-14 is still above the tolerance 1e-14.
        INFO - 08:36:45:     58%|█████▊    | 29/50 [00:06<00:04,  4.27 it/sec, obj=-3.14e+3]
     WARNING - 08:36:45: Optimization found no feasible point; the least infeasible point is selected.
     WARNING - 08:36:45:       The solution is not feasible.
        INFO - 08:36:45:     60%|██████    | 30/50 [00:06<00:04,  4.32 it/sec, obj=-3.12e+3]
     WARNING - 08:36:45: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7359275648513364e-14 is still above the tolerance 1e-14.
        INFO - 08:36:45:     62%|██████▏   | 31/50 [00:07<00:04,  4.34 it/sec, obj=-3.12e+3]
     WARNING - 08:36:45: MDAJacobi has reached its maximum number of iterations but the normed residual 2.6870836615612218e-14 is still above the tolerance 1e-14.
        INFO - 08:36:45:     64%|██████▍   | 32/50 [00:07<00:04,  4.36 it/sec, obj=-3.12e+3]
        INFO - 08:36:45:     66%|██████▌   | 33/50 [00:07<00:03,  4.40 it/sec, obj=-3.22e+3]
        INFO - 08:36:45:     68%|██████▊   | 34/50 [00:07<00:03,  4.43 it/sec, obj=-3.12e+3]
        INFO - 08:36:46:     70%|███████   | 35/50 [00:07<00:03,  4.45 it/sec, obj=-3.13e+3]
        INFO - 08:36:46:     72%|███████▏  | 36/50 [00:08<00:03,  4.49 it/sec, obj=-3.11e+3]
        INFO - 08:36:46:     74%|███████▍  | 37/50 [00:08<00:02,  4.52 it/sec, obj=-3.14e+3]
        INFO - 08:36:46:     76%|███████▌  | 38/50 [00:08<00:02,  4.55 it/sec, obj=-3.16e+3]
        INFO - 08:36:46:     78%|███████▊  | 39/50 [00:08<00:02,  4.58 it/sec, obj=-3.2e+3]
     WARNING - 08:36:46: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7359275648513364e-14 is still above the tolerance 1e-14.
        INFO - 08:36:46:     80%|████████  | 40/50 [00:08<00:02,  4.59 it/sec, obj=-3.16e+3]
        INFO - 08:36:47:     82%|████████▏ | 41/50 [00:08<00:01,  4.62 it/sec, obj=-3.16e+3]
        INFO - 08:36:47:     84%|████████▍ | 42/50 [00:09<00:01,  4.65 it/sec, obj=-3.15e+3]
        INFO - 08:36:47:     86%|████████▌ | 43/50 [00:09<00:01,  4.68 it/sec, obj=-3.17e+3]
        INFO - 08:36:47:     88%|████████▊ | 44/50 [00:09<00:01,  4.70 it/sec, obj=-3.18e+3]
        INFO - 08:36:47:     90%|█████████ | 45/50 [00:09<00:01,  4.73 it/sec, obj=-3.19e+3]
     WARNING - 08:36:47: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7359275648513364e-14 is still above the tolerance 1e-14.
        INFO - 08:36:47:     92%|█████████▏| 46/50 [00:09<00:00,  4.73 it/sec, obj=-3.2e+3]
        INFO - 08:36:48:     94%|█████████▍| 47/50 [00:09<00:00,  4.76 it/sec, obj=-3.21e+3]
        INFO - 08:36:48:     96%|█████████▌| 48/50 [00:10<00:00,  4.78 it/sec, obj=-3.22e+3]
        INFO - 08:36:48:     98%|█████████▊| 49/50 [00:10<00:00,  4.80 it/sec, obj=-3.23e+3]
        INFO - 08:36:48:    100%|██████████| 50/50 [00:10<00:00,  4.82 it/sec, obj=-3.24e+3]
        INFO - 08:36:48: Optimization result:
        INFO - 08:36:48:    Optimizer info:
        INFO - 08:36:48:       Status: None
        INFO - 08:36:48:       Message: Maximum number of iterations reached. GEMSEO stopped the driver.
        INFO - 08:36:48:       Number of calls to the objective function by the optimizer: 52
        INFO - 08:36:48:    Solution:
        INFO - 08:36:48:       The solution is feasible.
        INFO - 08:36:48:       Objective: -3240.7083588450046
        INFO - 08:36:48:       Standardized constraints:
        INFO - 08:36:48:          g_1_g_2_g_3 = [-3.41793260e-12 -1.26719747e-02 -2.55059715e-02 -3.52857326e-02
        INFO - 08:36:48:  -4.26719747e-02 -1.80140397e-01 -5.98596026e-02  0.00000000e+00
        INFO - 08:36:48:  -7.67227050e-01 -2.32772950e-01  2.22044605e-16 -1.83255000e-01]
        INFO - 08:36:48:       Design space:
        INFO - 08:36:48:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:36:48:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 08:36:48:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:36:48:          | x_shared[0] |     0.01    | 0.05999999999999999 |     0.09    | float |
        INFO - 08:36:48:          | x_shared[1] |    30000    |        60000        |    60000    | float |
        INFO - 08:36:48:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 08:36:48:          | x_shared[3] |     2.5     |  3.729811315256278  |     8.5     | float |
        INFO - 08:36:48:          | x_shared[4] |      40     |          70         |      70     | float |
        INFO - 08:36:48:          | x_shared[5] |     500     |         1500        |     1500    | float |
        INFO - 08:36:48:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:36:48: *** End MDOScenario execution (time: 0:00:10.391182) ***




.. GENERATED FROM PYTHON SOURCE LINES 169-172

Plot the history of the MDA residuals
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
For the first MDA:

.. GENERATED FROM PYTHON SOURCE LINES 172-176

.. code-block:: Python

    system_scenario.formulation.mda1.plot_residual_history(save=False, show=True)
    # For the second MDA:
    system_scenario.formulation.mda2.plot_residual_history(save=False, show=True)




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_001.png
         :alt: MDAJacobi: residual plot
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_002.png
         :alt: MDAJacobi: residual plot
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_002.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 177-179

Plot the system optimization history view
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 179-181

.. code-block:: Python

    system_scenario.post_process(post_name="OptHistoryView", save=False, show=True)




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_003.png
         :alt: Evolution of the optimization variables
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_004.png
         :alt: Evolution of the objective value
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_005.png
         :alt: Evolution of the distance to the optimum
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_006.png
         :alt: Evolution of the inequality constraints
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_006.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <gemseo.post.opt_history_view.OptHistoryView object at 0x7f6dc1162700>



.. GENERATED FROM PYTHON SOURCE LINES 182-184

Plot the structure optimization histories of the 2 first iterations
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 184-193

.. code-block:: Python


    struct_databases = system_scenario.formulation.scenario_adapters[2].databases
    for database in struct_databases[:2]:
        opt_problem = deepcopy(sc_str.formulation.optimization_problem)
        opt_problem.database = database
        execute_post(opt_problem, post_name="OptHistoryView", save=False, show=True)

    for disc in [propu, aero, mission, struct]:
        print(f"{disc.name}: {disc.execution_statistics.n_calls} calls.")



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_007.png
         :alt: Evolution of the optimization variables
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_007.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_008.png
         :alt: Evolution of the objective value
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_008.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_009.png
         :alt: Evolution of the distance to the optimum
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_009.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_010.png
         :alt: Evolution of the inequality constraints
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_010.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_011.png
         :alt: Evolution of the optimization variables
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_011.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_012.png
         :alt: Evolution of the objective value
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_012.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_013.png
         :alt: Evolution of the distance to the optimum
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_013.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_014.png
         :alt: Evolution of the inequality constraints
         :srcset: /examples/formulations/images/sphx_glr_plot_sobieski_bilevel_example_014.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    SobieskiPropulsion: 1642 calls.
    SobieskiAerodynamics: 1784 calls.
    SobieskiMission: 50 calls.
    SobieskiStructure: 1956 calls.





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 14.708 seconds)


.. _sphx_glr_download_examples_formulations_plot_sobieski_bilevel_example.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_sobieski_bilevel_example.ipynb <plot_sobieski_bilevel_example.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_sobieski_bilevel_example.py <plot_sobieski_bilevel_example.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_sobieski_bilevel_example.zip <plot_sobieski_bilevel_example.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_