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.. "examples/post_process/post_process_opt_problem.py"
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.. only:: html

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

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

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

.. _sphx_glr_examples_post_process_post_process_opt_problem.py:


Post-process an optimization problem
====================================

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.. code-block:: Python


    from __future__ import annotations

    from gemseo import create_design_space
    from gemseo import execute_algo
    from gemseo import execute_post
    from gemseo.algos.opt_problem import OptimizationProblem
    from gemseo.core.mdofunctions.mdo_function import MDOFunction


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We consider a minimization problem over the interval :math:`[0,1]`
of the :math:`f(x)=x^2` objective function:

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.. code-block:: Python


    objective = MDOFunction(lambda x: x**2, "f", input_names=["x"], output_names=["y"])

    design_space = create_design_space()
    design_space.add_variable("x", l_b=0.0, u_b=1.0)

    optimization_problem = OptimizationProblem(design_space)
    optimization_problem.objective = objective


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We solve this optimization problem with the gradient-free algorithm COBYLA:

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.. code-block:: Python

    execute_algo(optimization_problem, "NLOPT_COBYLA", max_iter=10)


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Then,
we can post-process this :class:`.OptimizationProblem`
with the function :func:`.execute_post`:

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.. code-block:: Python

    execute_post(optimization_problem, "BasicHistory", variable_names=["y"])


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.. note::
   By default, |g| saves the images on the disk.
   Use ``save=False`` to not save figures and ``show=True`` to display them on the screen.

.. seealso:: `Post-processing algorithms <index.html#algorithms>`_.


.. _sphx_glr_download_examples_post_process_post_process_opt_problem.py:

.. only:: html

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

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

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

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

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


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