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

.. only:: html

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

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

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

.. _sphx_glr_examples_scalable_plot_problem.py:


Scalable problem
================

We want to solve the Aerostructure MDO problem
by means of the :class:`.MDF` formulation
with a higher dimension for the sweep parameter.
For that, we use the :class:`.ScalableProblem` class.

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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.problems.aerostructure.aerostructure_design_space import (
        AerostructureDesignSpace,
    )
    from gemseo.problems.mdo.scalable.data_driven.problem import ScalableProblem

    configure_logger()




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

.. code-block:: pytb

    Traceback (most recent call last):
      File "/home/docs/checkouts/readthedocs.org/user_builds/gemseo/checkouts/develop/doc_src/_examples/scalable/plot_problem.py", line 36, in <module>
        from gemseo.problems.aerostructure.aerostructure_design_space import (
    ModuleNotFoundError: No module named 'gemseo.problems.aerostructure'




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Define the design problem
-------------------------
In a first step, we define the design problem in terms of
objective function (to maximize or minimize),
design variables (local and global)
and constraints (equality and inequality).

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.. code-block:: Python

    design_variables = ["thick_airfoils", "thick_panels", "sweep"]
    objective_function = "range"
    eq_constraints = ["c_rf"]
    ineq_constraints = ["c_lift"]
    maximize_objective = True


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Create the disciplinary datasets
--------------------------------
Then, we create the disciplinary :class:`.AbstractFullCache` datasets
based on a :class:`.DiagonalDOE`.

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.. code-block:: Python

    disciplines = create_discipline(["Aerodynamics", "Structure", "Mission"])
    datasets = []
    for discipline in disciplines:
        design_space = AerostructureDesignSpace()
        design_space.filter(discipline.get_input_data_names())
        output_names = iter(discipline.get_output_data_names())
        scenario = create_scenario(
            discipline,
            "DisciplinaryOpt",
            next(output_names),
            design_space,
            scenario_type="DOE",
        )
        for output_name in output_names:
            scenario.add_observable(output_name)
        scenario.execute({"algo": "DiagonalDOE", "n_samples": 10})
        datasets.append(scenario.to_dataset(name=discipline.name, opt_naming=False))


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Instantiate a scalable problem
------------------------------
In a third stage, we instantiate a :class:`.ScalableProblem`
from these disciplinary datasets and from the definition of the MDO problem.
We also increase the dimension of the sweep parameter.

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.. code-block:: Python

    problem = ScalableProblem(
        datasets,
        design_variables,
        objective_function,
        eq_constraints,
        ineq_constraints,
        maximize_objective,
        sizes={"sweep": 2},
    )
    print(problem)


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.. note::

   We could also provide options to the :class:`.ScalableModel` objects
   by means of the constructor of :class:`.ScalableProblem`,
   e.g. ``fill_factor`` in the frame of the :class:`.ScalableDiagonalModel`.
   In this example, we use the standard ones.

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Visualize the N2 chart
----------------------
We can see the coupling between disciplines through this N2 chart:

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.. code-block:: Python

    problem.plot_n2_chart(save=False, show=True)


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Create an MDO scenario
----------------------
Lastly, we create an :class:`.MDOScenario` with the :class:`.MDF` formulation
and start the optimization at equilibrium,
thus ensuring the feasibility of the first iterate.

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.. code-block:: Python

    scenario = problem.create_scenario("MDF", start_at_equilibrium=True)


.. GENERATED FROM PYTHON SOURCE LINES 120-126

.. note::

   We could also provide options for the scalable models to the constructor
   of :class:`.ScalableProblem`, e.g. ``fill_factor`` in the frame of
   the :class:`.ScalableDiagonalModel`.
   In this example, we use the standard ones.

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Once the scenario is created, we can execute it as any scenario.
Here, we use the ``NLOPT_SLSQP`` optimization algorithm
with no more than 100 iterations.

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.. code-block:: Python

    scenario.execute({"algo": "NLOPT_SLSQP", "max_iter": 100})


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We can post-process the results.
Here, we use the standard :class:`.OptHistoryView`.

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.. code-block:: Python

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


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

   **Total running time of the script:** (0 minutes 0.001 seconds)


.. _sphx_glr_download_examples_scalable_plot_problem.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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