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

.. only:: html

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

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

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

.. _sphx_glr_examples_mdo_plot_sobieski_use_case.py:


Application: Sobieski's Super-Sonic Business Jet (MDO)
======================================================

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This section describes how to setup and solve the MDO problem relative to the
:ref:`Sobieski test case <sobieski_problem>` with |g|.

.. seealso::

   To begin with a more simple MDO problem, and have a detailed description
   of how to plug a test case to |g|, see :ref:`sellar_mdo`.


.. _sobieski_use_case:

Solving with an :ref:`MDF formulation <mdf_formulation>`
--------------------------------------------------------

In this example, we solve the range optimization using the following
:ref:`MDF formulation <mdf_formulation>`:

- The :ref:`MDF formulation <mdf_formulation>` couples all the disciplines
  during the :ref:`mda` at each optimization iteration.
- All the :term:`design variables` are equally treated, concatenated in a
  single vector and given to a single :term:`optimization algorithm` as the
  unknowns of the problem.
- There is no specific :term:`constraint` due to the :ref:`MDF formulation
  <mdf_formulation>`.
- Only the design :term:`constraints` :math:`g\_1`, :math:`g\_2` and
  :math:`g\_3` are added to the problem.
- The :term:`objective function` is the range (the :math:`y\_4` variable in
  the model), computed after the :ref:`mda`.

Imports
-------
All the imports needed for the tutorials are performed here.
Note that some of the imports are related to the Python 2/3 compatibility.

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.. code-block:: Python

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo import get_available_formulations
    from gemseo.core.derivatives.jacobian_assembly import JacobianAssembly
    from gemseo.disciplines.utils import get_all_inputs
    from gemseo.disciplines.utils import get_all_outputs
    from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace

    configure_logger()





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

 .. code-block:: none


    <RootLogger root (INFO)>



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Step 1: Creation of :class:`.MDODiscipline`
-------------------------------------------

To build the scenario, we first instantiate the disciplines. Here, the
disciplines themselves have already been
developed and interfaced with |g| (see :ref:`benchmark_problems`).

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.. code-block:: Python


    disciplines = create_discipline([
        "SobieskiPropulsion",
        "SobieskiAerodynamics",
        "SobieskiMission",
        "SobieskiStructure",
    ])








.. GENERATED FROM PYTHON SOURCE LINES 88-95

.. tip::

   For the disciplines that are not interfaced with |g|, the |g|'s
   :mod:`~gemseo` eases the creation of disciplines without having
   to import them.

   See :ref:`api`.

.. GENERATED FROM PYTHON SOURCE LINES 97-113

Step 2: Creation of :class:`.Scenario`
--------------------------------------

The scenario delegates the creation of the optimization problem to the
:ref:`MDO formulation <mdo_formulations>`.

Therefore, it needs the list of ``disciplines``, the names of the formulation,
the name of the objective function and the design space.

- The ``design_space`` (shown below for reference, as ``design_space.txt``)
  defines the unknowns of the optimization problem, and their bounds. It contains
  all the design variables needed by the :ref:`MDF formulation <mdf_formulation>`.
  It can be imported from a text file, or created from scratch with the methods
  :func:`.create_design_space` and
  :meth:`~gemseo.algos.design_space.DesignSpace.add_variable`. In this case,
  we will create it directly from the API.

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.. code-block:: Python

    design_space = SobieskiDesignSpace()







.. GENERATED FROM PYTHON SOURCE LINES 115-148

.. code::

          vi design_space.csv

          name      lower_bound      value      upper_bound  type
          x_shared      0.01          0.05          0.09     float
          x_shared    30000.0       45000.0       60000.0    float
          x_shared      1.4           1.6           1.8      float
          x_shared      2.5           5.5           8.5      float
          x_shared      40.0          55.0          70.0     float
          x_shared     500.0         1000.0        1500.0    float
          x_1           0.1           0.25          0.4      float
          x_1           0.75          1.0           1.25     float
          x_2           0.75          1.0           1.25     float
          x_3           0.1           0.5           1.0      float
          y_14        24850.0    50606.9741711    77100.0    float
          y_14        -7700.0    7306.20262124    45000.0    float
          y_32         0.235       0.50279625      0.795     float
          y_31         2960.0    6354.32430691    10185.0    float
          y_24          0.44       4.15006276      11.13     float
          y_34          0.44       1.10754577       1.98     float
          y_23         3365.0    12194.2671934    26400.0    float
          y_21        24850.0    50606.9741711    77250.0    float
          y_12        24850.0      50606.9742     77250.0    float
          y_12          0.45          0.95          1.5      float

- The available :ref:`MDO formulations <mdo_formulations>` are located in the
  **gemseo.formulations** package, see :ref:`extending-gemseo` for extending
  GEMSEO with other formulations.
- The ``formulation`` classname (here, ``"MDF"``) shall be passed to
  the scenario to select them.
- The list of available formulations can be obtained by using
  :func:`.get_available_formulations`.

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.. code-block:: Python

    get_available_formulations()




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

 .. code-block:: none


    ['BiLevel', 'DisciplinaryOpt', 'IDF', 'MDF']



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- :math:`y\_4` corresponds to the ``objective_name``. This name must be one
  of the disciplines outputs, here the "SobieskiMission" discipline. The list of
  all outputs of the disciplines can be obtained by using
  :meth:`~gemseo.disciplines.utils.get_all_outputs`:

.. GENERATED FROM PYTHON SOURCE LINES 154-156

.. code-block:: Python

    get_all_outputs(disciplines)
    get_all_inputs(disciplines)




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

 .. code-block:: none


    ['c_0', 'c_1', 'c_2', 'c_3', 'c_4', 'x_1', 'x_2', 'x_3', 'x_shared', 'y_12', 'y_14', 'y_21', 'y_23', 'y_24', 'y_31', 'y_32', 'y_34']



.. GENERATED FROM PYTHON SOURCE LINES 157-160

From these :class:`~gemseo.core.discipline.MDODiscipline`, design space filename,
:ref:`MDO formulation <mdo_formulations>` name and objective function name,
we build the scenario:

.. GENERATED FROM PYTHON SOURCE LINES 160-167

.. code-block:: Python

    scenario = create_scenario(
        disciplines,
        "MDF",
        "y_4",
        design_space,
        maximize_objective=True,
    )







.. GENERATED FROM PYTHON SOURCE LINES 168-182

The range function (:math:`y\_4`) should be maximized. However, optimizers
minimize functions by default. Which is why, when creating the scenario, the argument
``maximize_objective`` shall be set to ``True``.

Scenario options
~~~~~~~~~~~~~~~~

We may provide additional options to the scenario:


**Function derivatives.** As analytical disciplinary derivatives are
vailable for Sobieski test-case, they can be used instead of computing
the derivatives with finite-differences or with the complex-step method.
The easiest way to set a method is to let the optimizer determine it:

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.. code-block:: Python

    scenario.set_differentiation_method()







.. GENERATED FROM PYTHON SOURCE LINES 184-209

The default behavior of the optimizer triggers :term:`finite differences`.
It corresponds to:

.. code::

  scenario.set_differentiation_method("finite_differences",1e-7)

It it also possible to differentiate functions by means of the
:term:`complex step` method:

.. code::

  scenario.set_differentiation_method("complex_step",1e-30j)

Constraints
~~~~~~~~~~~

Similarly to the objective function, the constraints names are a subset
of the disciplines' outputs. They can be obtained by using
:meth:`~gemseo.disciplines.utils.get_all_outputs`.

The formulation has a powerful feature to automatically dispatch the constraints
(:math:`g\_1, g\_2, g\_3`) and plug them to the optimizers depending on
the formulation. To do that, we use the method
:meth:`~gemseo.core.scenario.Scenario.add_constraint`:

.. GENERATED FROM PYTHON SOURCE LINES 210-212

.. code-block:: Python

    for constraint in ["g_1", "g_2", "g_3"]:
        scenario.add_constraint(constraint, constraint_type="ineq")







.. GENERATED FROM PYTHON SOURCE LINES 213-218

Step 3: Execution and visualization of the results
--------------------------------------------------

The algorithm arguments are provided as a dictionary to the execution
method of the scenario:

.. GENERATED FROM PYTHON SOURCE LINES 218-219

.. code-block:: Python

    algo_args = {"max_iter": 10, "algo": "SLSQP"}







.. GENERATED FROM PYTHON SOURCE LINES 220-230

.. warning::

   The mandatory arguments are the maximum number of iterations and the algorithm name.
   Any other options of the optimization algorithm can be prescribed through
   the argument ``algo_options`` with a dictionary, e.g.
   :code:`algo_args =
   {"max_iter": 10, "algo": "SLSQP": "algo_options": {"ftol_rel": 1e-6}}`.
   This list of available algorithm options are detailed here: :ref:`gen_opt_algos`.

The scenario is executed by means of the line:

.. GENERATED FROM PYTHON SOURCE LINES 230-231

.. code-block:: Python

    scenario.execute(algo_args)




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

 .. code-block:: none

        INFO - 08:56:19:  
        INFO - 08:56:19: *** Start MDOScenario execution ***
        INFO - 08:56:19: MDOScenario
        INFO - 08:56:19:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 08:56:19:    MDO formulation: MDF
        INFO - 08:56:19: Optimization problem:
        INFO - 08:56:19:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 08:56:19:    with respect to x_1, x_2, x_3, x_shared
        INFO - 08:56:19:    subject to constraints:
        INFO - 08:56:19:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 08:56:19:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 08:56:19:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 08:56:19:    over the design space:
        INFO - 08:56:19:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:19:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:56:19:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:19:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 08:56:19:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 08:56:19:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 08:56:19:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 08:56:19:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 08:56:19:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 08:56:19:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 08:56:19:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 08:56:19:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 08:56:19:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 08:56:19:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:19: Solving optimization problem with algorithm SLSQP:
        INFO - 08:56:19:     10%|█         | 1/10 [00:00<00:00, 10.95 it/sec, obj=-536]
        INFO - 08:56:19:     20%|██        | 2/10 [00:00<00:01,  7.84 it/sec, obj=-2.12e+3]
     WARNING - 08:56:19: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7130677857005655e-05 is still above the tolerance 1e-06.
        INFO - 08:56:19:     30%|███       | 3/10 [00:00<00:01,  6.57 it/sec, obj=-3.75e+3]
        INFO - 08:56:19:     40%|████      | 4/10 [00:00<00:00,  6.29 it/sec, obj=-3.96e+3]
        INFO - 08:56:20:     50%|█████     | 5/10 [00:00<00:00,  6.14 it/sec, obj=-3.96e+3]
        INFO - 08:56:20: Optimization result:
        INFO - 08:56:20:    Optimizer info:
        INFO - 08:56:20:       Status: 8
        INFO - 08:56:20:       Message: Positive directional derivative for linesearch
        INFO - 08:56:20:       Number of calls to the objective function by the optimizer: 6
        INFO - 08:56:20:    Solution:
        INFO - 08:56:20:       The solution is feasible.
        INFO - 08:56:20:       Objective: -3963.408265187933
        INFO - 08:56:20:       Standardized constraints:
        INFO - 08:56:20:          g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346  -0.05732607 -0.13720865
        INFO - 08:56:20:  -0.10279135]
        INFO - 08:56:20:          g_2 = 3.333278582928756e-06
        INFO - 08:56:20:          g_3 = [-7.67181773e-01 -2.32818227e-01  8.30379541e-07 -1.83255000e-01]
        INFO - 08:56:20:       Design space:
        INFO - 08:56:20:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:56:20:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 08:56:20:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:56:20:          | x_shared[0] |     0.01    | 0.06000083331964572 |     0.09    | float |
        INFO - 08:56:20:          | x_shared[1] |    30000    |        60000        |    60000    | float |
        INFO - 08:56:20:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 08:56:20:          | x_shared[3] |     2.5     |         2.5         |     8.5     | float |
        INFO - 08:56:20:          | x_shared[4] |      40     |          70         |      70     | float |
        INFO - 08:56:20:          | x_shared[5] |     500     |         1500        |     1500    | float |
        INFO - 08:56:20:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 08:56:20:          | x_1[1]      |     0.75    |         0.75        |     1.25    | float |
        INFO - 08:56:20:          | x_2         |     0.75    |         0.75        |     1.25    | float |
        INFO - 08:56:20:          | x_3         |     0.1     |  0.1562448753887276 |      1      | float |
        INFO - 08:56:20:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:56:20: *** End MDOScenario execution (time: 0:00:00.930844) ***

    {'max_iter': 10, 'algo': 'SLSQP'}



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Post-processing options
~~~~~~~~~~~~~~~~~~~~~~~
A whole variety of visualizations may be displayed for both MDO and DOE
scenarios. These features are illustrated on the SSBJ use case in
:ref:`post_processing`.

To visualize the optimization history:

.. GENERATED FROM PYTHON SOURCE LINES 239-241

.. code-block:: Python

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




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


    *

      .. image-sg:: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_001.png
         :alt: Evolution of the optimization variables
         :srcset: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_002.png
         :alt: Evolution of the objective value
         :srcset: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_003.png
         :alt: Distance to the optimum
         :srcset: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_004.png
         :alt: Hessian diagonal approximation
         :srcset: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_005.png
         :alt: Evolution of the inequality constraints
         :srcset: /examples/mdo/images/sphx_glr_plot_sobieski_use_case_005.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none


    <gemseo.post.opt_history_view.OptHistoryView object at 0x7f1db318b880>



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Influence of gradient computation method on performance
-------------------------------------------------------

As mentioned in :ref:`jacobian_assembly`, several methods
are available in order to perform  the gradient computations: classical finite
differences, complex step and :ref:`mda` linearization in direct or adjoint mode.
These modes are automatically selected by |g| to minimize the CPU time. Yet, they
can be forced on demand in each :ref:`mda`:

.. GENERATED FROM PYTHON SOURCE LINES 250-252

.. code-block:: Python

    scenario.formulation.mda.linearization_mode = JacobianAssembly.DerivationMode.DIRECT
    scenario.formulation.mda.matrix_type = JacobianAssembly.JacobianType.LINEAR_OPERATOR







.. GENERATED FROM PYTHON SOURCE LINES 253-258

The method used to solve the adjoint or direct linear problem may also be selected.
|g| can either assemble a sparse residual Jacobian matrix of the :ref:`mda` from the
disciplines matrices. This has the advantage that LU factorizations may be stored to
solve multiple right hand sides problems in a cheap way. But this requires
extra memory.

.. GENERATED FROM PYTHON SOURCE LINES 258-260

.. code-block:: Python

    scenario.formulation.mda.matrix_type = JacobianAssembly.JacobianType.MATRIX
    scenario.formulation.mda.use_lu_fact = True







.. GENERATED FROM PYTHON SOURCE LINES 261-265

Altenatively, |g| can implicitly create a matrix-vector product operator,
which is sufficient for GMRES-like solvers. It avoids to create an additional
data structure. This can also be mandatory if the disciplines do not provide
full Jacobian matrices but only matrix-vector product operators.

.. GENERATED FROM PYTHON SOURCE LINES 265-266

.. code-block:: Python

    scenario.formulation.mda.matrix_type = JacobianAssembly.JacobianType.LINEAR_OPERATOR







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The next table shows the performance of each method for solving the Sobieski use case
with :ref:`MDF <mdf_formulation>` and :ref:`IDF <idf_formulation>` formulations.
Efficiency of linearization is clearly visible has it takes from 10 to 20 times
less CPU time to compute analytic derivatives of an :ref:`mda` compared
to finite difference and complex step.
For :ref:`IDF <idf_formulation>`, improvements are less consequent,
but direct linearization is more than 2.5 times faster than other methods.

.. tabularcolumns:: |l|c|c|

+-----------------------+------------------------------+------------------------------+
|                       |  Execution time (s)                                         |
+  Derivation Method    +------------------------------+------------------------------+
|                       | :ref:`MDF <mdf_formulation>` | :ref:`IDF <idf_formulation>` |
+=======================+==============================+==============================+
| Finite differences    | 8.22                         | 1.93                         |
+-----------------------+------------------------------+------------------------------+
| Complex step          | 18.11                        | 2.07                         |
+-----------------------+------------------------------+------------------------------+
| Linearized (direct)   | 0.90                         | 0.68                         |
+-----------------------+------------------------------+------------------------------+


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

   **Total running time of the script:** (0 minutes 2.009 seconds)


.. _sphx_glr_download_examples_mdo_plot_sobieski_use_case.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_use_case.ipynb <plot_sobieski_use_case.ipynb>`

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

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


.. only:: html

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

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