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

.. only:: html

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

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

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

.. _sphx_glr_examples_post_process_algorithms_plot_gradient_sensitivity.py:


Gradient Sensitivity
====================

In this example, we illustrate the use of the :class:`.GradientSensitivity`
post-processing on the Sobieski's SSBJ problem.

The :class:`.GradientSensitivity` post-processor plots histograms of the objective and
the constraints derivatives.

By default, the sensitivities are calculated either at the optimum, or when the result
is not feasible, at the least-non feasible point. The iteration where the sensitivities
are computed can be modified via the `iteration` setting.

.. note::
  In some cases, the iteration used to compute the sensitivities corresponds to a
  point for which the algorithm did not request the evaluation of the gradients. In
  this case, a `ValueError` is raised by :class:`.GradientSensitivity`.
  To overcome this issue, one can set the `compute_missing_gradients` setting to True.
  This way, |g| will compute the gradients for the iterations where it is lacking.
  This can be done only if the underlying disciplines are available, explaing why
  why, unlike the other post-processing examples, we need to **post-process directly
  from the MDO scenario**.

.. warning::
   Please note that this extra computation may be expensive depending on the
   :class:`.OptimizationProblem` defined by the user. Additionally, keep in mind that
   |g| cannot compute missing gradients for an :class:`.OptimizationProblem` that was
   imported from an HDF5 file.

.. GENERATED FROM PYTHON SOURCE LINES 53-57

MDO scenario
------------

Let us first create and execute the MDF scenario on the Sobieski's SSBJ problem.

.. GENERATED FROM PYTHON SOURCE LINES 57-98

.. code-block:: Python

    from __future__ import annotations

    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.algos.opt.scipy_local.settings.slsqp import SLSQP_Settings
    from gemseo.formulations.mdf_settings import MDF_Settings
    from gemseo.mda.gauss_seidel_settings import MDAGaussSeidel_Settings
    from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace
    from gemseo.settings.post import GradientSensitivity_Settings

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

    formulation_settings = MDF_Settings(
        main_mda_name="MDAGaussSeidel",
        main_mda_settings=MDAGaussSeidel_Settings(
            max_mda_iter=30,
            tolerance=1e-10,
            warm_start=True,
            use_lu_fact=True,
        ),
    )

    scenario = create_scenario(
        disciplines,
        "y_4",
        design_space=SobieskiDesignSpace(),
        maximize_objective=True,
        formulation_settings_model=formulation_settings,
    )

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

    scenario.execute(SLSQP_Settings(max_iter=20))









.. GENERATED FROM PYTHON SOURCE LINES 99-102

Post-processing
---------------
Let us now post-process the scenario by means of the :class:`.GradientSensitivity`.

.. GENERATED FROM PYTHON SOURCE LINES 102-110

.. code-block:: Python


    scenario.post_process(
        settings_model=GradientSensitivity_Settings(
            compute_missing_gradients=True,
            save=False,
            show=True,
        ),
    )



.. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_gradient_sensitivity_001.png
   :alt: Derivatives of objective and constraints with respect to design variables, -y_4, g_1[0], g_1[1], g_1[2], g_1[3], g_1[4], g_1[5], g_1[6], g_2, g_3[0], g_3[1], g_3[2], g_3[3]
   :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_gradient_sensitivity_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gemseo.post.gradient_sensitivity.GradientSensitivity object at 0x7048fd3622a0>




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

   **Total running time of the script:** (0 minutes 1.861 seconds)


.. _sphx_glr_download_examples_post_process_algorithms_plot_gradient_sensitivity.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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