.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/constraint_aggregation/plot_constraint_aggregation_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_constraint_aggregation_plot_constraint_aggregation_example.py>`
        to download the full example code.

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

.. _sphx_glr_examples_constraint_aggregation_plot_constraint_aggregation_example.py:


Examples for constraint aggregation
===================================

.. GENERATED FROM PYTHON SOURCE LINES 20-33

.. code-block:: Python


    from __future__ import annotations

    from copy import deepcopy

    from gemseo import configure_logger
    from gemseo import create_scenario
    from gemseo.algos.design_space import DesignSpace
    from gemseo.algos.opt.nlopt.settings.nlopt_mma_settings import NLOPT_MMA_Settings
    from gemseo.disciplines.analytic import AnalyticDiscipline
    from gemseo.disciplines.concatenater import Concatenater

    configure_logger()




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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 34-35

Number of constraints

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

    N = 100








.. GENERATED FROM PYTHON SOURCE LINES 38-39

Build the discipline

.. GENERATED FROM PYTHON SOURCE LINES 39-49

.. code-block:: Python

    constraint_names = [f"g_{k + 1}" for k in range(N)]
    function_names = ["o", *constraint_names]
    function_expressions = ["y"] + [f"{k + 1}*x*exp(1-{k + 1}*x)-y" for k in range(N)]
    disc = AnalyticDiscipline(
        name="function",
        expressions=dict(zip(function_names, function_expressions)),
    )
    # This step is required to put all constraints needed for aggregation in one variable.
    concat = Concatenater(constraint_names, "g")








.. GENERATED FROM PYTHON SOURCE LINES 50-51

Build the design space

.. GENERATED FROM PYTHON SOURCE LINES 51-68

.. code-block:: Python

    ds = DesignSpace()
    ds.add_variable(
        "x",
        lower_bound=0.0,
        upper_bound=1,
        value=1.0 / N / 2.0,
        type_=DesignSpace.DesignVariableType.FLOAT,
    )
    ds.add_variable(
        "y",
        lower_bound=0.0,
        upper_bound=1,
        value=1,
        type_=DesignSpace.DesignVariableType.FLOAT,
    )

    ds_new = deepcopy(ds)







.. GENERATED FROM PYTHON SOURCE LINES 69-70

Build the optimization solver settings

.. GENERATED FROM PYTHON SOURCE LINES 70-81

.. code-block:: Python

    mma_settings = NLOPT_MMA_Settings(
        ineq_tolerance=1e-5,
        eq_tolerance=1e-5,
        xtol_rel=1e-8,
        xtol_abs=1e-8,
        ftol_rel=1e-8,
        ftol_abs=1e-8,
        normalize_design_space=True,
        max_iter=1000,
    )








.. GENERATED FROM PYTHON SOURCE LINES 82-83

Build the optimization scenario

.. GENERATED FROM PYTHON SOURCE LINES 83-96

.. code-block:: Python

    original_scenario = create_scenario(
        [disc, concat],
        "o",
        ds,
        maximize_objective=False,
        formulation_name="DisciplinaryOpt",
    )
    original_scenario.add_constraint("g", constraint_type="ineq")

    original_scenario.execute(mma_settings)
    # Without constraint aggregation MMA iterations become more expensive, when a
    # large number of constraints are activated.





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

 .. code-block:: none

        INFO - 08:35:32:  
        INFO - 08:35:32: *** Start MDOScenario execution ***
        INFO - 08:35:32: MDOScenario
        INFO - 08:35:32:    Disciplines: Concatenater function
        INFO - 08:35:32:    MDO formulation: DisciplinaryOpt
        INFO - 08:35:32: Optimization problem:
        INFO - 08:35:32:    minimize o(x, y)
        INFO - 08:35:32:    with respect to x, y
        INFO - 08:35:32:    subject to constraints:
        INFO - 08:35:32:       g(x, y) <= 0
        INFO - 08:35:32:    over the design space:
        INFO - 08:35:32:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:32:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:35:32:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:32:       | x    |      0      | 0.005 |      1      | float |
        INFO - 08:35:32:       | y    |      0      |   1   |      1      | float |
        INFO - 08:35:32:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:32: Solving optimization problem with algorithm NLOPT_MMA:
        INFO - 08:35:32:      1%|          | 6/1000 [00:00<00:51, 19.41 it/sec, obj=0.00931]
        INFO - 08:35:32:      1%|          | 7/1000 [00:00<00:49, 20.25 it/sec, obj=8.28e-5]
        INFO - 08:35:32:      1%|          | 8/1000 [00:00<00:47, 21.00 it/sec, obj=5.2e-9]
        INFO - 08:35:45:      1%|          | 9/1000 [00:12<23:50, 41.57 it/min, obj=0]
        INFO - 08:35:45: Optimization result:
        INFO - 08:35:45:    Optimizer info:
        INFO - 08:35:45:       Status: 5
        INFO - 08:35:45:       Message: NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached
        INFO - 08:35:45:       Number of calls to the objective function by the optimizer: 1501
        INFO - 08:35:45:    Solution:
        INFO - 08:35:45:       The solution is feasible.
        INFO - 08:35:45:       Objective: 0.0
        INFO - 08:35:45:       Standardized constraints:
        INFO - 08:35:45:          g = [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
        INFO - 08:35:45:  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
        INFO - 08:35:45:  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
        INFO - 08:35:45:  0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
        INFO - 08:35:45:  0. 0. 0. 0.]
        INFO - 08:35:45:       Design space:
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:          | x    |      0      |   0   |      1      | float |
        INFO - 08:35:45:          | y    |      0      |   0   |      1      | float |
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45: *** End MDOScenario execution (time: 0:00:12.997006) ***




.. GENERATED FROM PYTHON SOURCE LINES 97-98

exploiting constraint aggregation on the same scenario:

.. GENERATED FROM PYTHON SOURCE LINES 98-107

.. code-block:: Python

    new_scenario = create_scenario(
        [disc, concat],
        "o",
        ds_new,
        maximize_objective=False,
        formulation_name="DisciplinaryOpt",
    )
    new_scenario.add_constraint("g", constraint_type="ineq")








.. GENERATED FROM PYTHON SOURCE LINES 108-109

This method aggregates the constraints using the lower bound KS function

.. GENERATED FROM PYTHON SOURCE LINES 109-114

.. code-block:: Python

    new_scenario.formulation.optimization_problem.constraints.aggregate(
        0, method="lower_bound_KS", rho=10.0
    )
    new_scenario.execute(mma_settings)





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

 .. code-block:: none

        INFO - 08:35:45:  
        INFO - 08:35:45: *** Start MDOScenario execution ***
        INFO - 08:35:45: MDOScenario
        INFO - 08:35:45:    Disciplines: Concatenater function
        INFO - 08:35:45:    MDO formulation: DisciplinaryOpt
        INFO - 08:35:45: Optimization problem:
        INFO - 08:35:45:    minimize o(x, y)
        INFO - 08:35:45:    with respect to x, y
        INFO - 08:35:45:    subject to constraints:
        INFO - 08:35:45:       lower_bound_KS() <= 0.0
        INFO - 08:35:45:    over the design space:
        INFO - 08:35:45:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:35:45:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:       | x    |      0      | 0.005 |      1      | float |
        INFO - 08:35:45:       | y    |      0      |   1   |      1      | float |
        INFO - 08:35:45:       +------+-------------+-------+-------------+-------+
        INFO - 08:35:45: Solving optimization problem with algorithm NLOPT_MMA:
        INFO - 08:35:45:      1%|          | 6/1000 [00:00<00:31, 31.63 it/sec, obj=0.00773]
        INFO - 08:35:45:      1%|          | 7/1000 [00:00<00:30, 32.29 it/sec, obj=5.72e-5]
        INFO - 08:35:45:      1%|          | 8/1000 [00:00<00:30, 32.84 it/sec, obj=2.63e-9]
        INFO - 08:35:45:      1%|          | 9/1000 [00:00<00:53, 18.44 it/sec, obj=0]
        INFO - 08:35:45: Optimization result:
        INFO - 08:35:45:    Optimizer info:
        INFO - 08:35:45:       Status: 5
        INFO - 08:35:45:       Message: NLOPT_MAXEVAL_REACHED: Optimization stopped because maxeval (above) was reached
        INFO - 08:35:45:       Number of calls to the objective function by the optimizer: 1501
        INFO - 08:35:45:    Solution:
        INFO - 08:35:45:       The solution is feasible.
        INFO - 08:35:45:       Objective: 0.0
        INFO - 08:35:45:       Standardized constraints:
        INFO - 08:35:45:          lower_bound_KS(g) = 2.7755575615628914e-16
        INFO - 08:35:45:       Design space:
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45:          | x    |      0      |   0   |      1      | float |
        INFO - 08:35:45:          | y    |      0      |   0   |      1      | float |
        INFO - 08:35:45:          +------+-------------+-------+-------------+-------+
        INFO - 08:35:45: *** End MDOScenario execution (time: 0:00:00.491943) ***




.. GENERATED FROM PYTHON SOURCE LINES 115-116

with constraint aggregation the last iteration is faster.


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

   **Total running time of the script:** (0 minutes 14.851 seconds)


.. _sphx_glr_download_examples_constraint_aggregation_plot_constraint_aggregation_example.py:

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

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