.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/post_process/plot_constraints_history.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_post_process_plot_constraints_history.py: Constraints history =================== In this example, we illustrate the use of the :class:`.ConstraintsHistory` plot on the Sobieski's SSBJ problem. .. GENERATED FROM PYTHON SOURCE LINES 30-34 .. code-block:: default from __future__ import division, unicode_literals from matplotlib import pyplot as plt .. GENERATED FROM PYTHON SOURCE LINES 35-39 Import ------ The first step is to import some functions from the API and a method to get the design space. .. GENERATED FROM PYTHON SOURCE LINES 39-45 .. code-block:: default from gemseo.api import configure_logger, create_discipline, create_scenario from gemseo.problems.sobieski.core import SobieskiProblem configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 46-50 Create disciplines ------------------ Then, we instantiate the disciplines of the Sobieski's SSBJ problem: Propulsion, Aerodynamics, Structure and Mission .. GENERATED FROM PYTHON SOURCE LINES 50-59 .. code-block:: default disciplines = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiStructure", "SobieskiMission", ] ) .. GENERATED FROM PYTHON SOURCE LINES 60-63 Create design space ------------------- We also read the design space from the :class:`.SobieskiProblem`. .. GENERATED FROM PYTHON SOURCE LINES 63-65 .. code-block:: default design_space = SobieskiProblem().read_design_space() .. GENERATED FROM PYTHON SOURCE LINES 66-73 Create and execute scenario --------------------------- The next step is to build an MDO scenario in order to maximize the range, encoded 'y_4', with respect to the design parameters, while satisfying the inequality constraints 'g_1', 'g_2' and 'g_3'. We can use the MDF formulation, the SLSQP optimization algorithm and a maximum number of iterations equal to 100. .. GENERATED FROM PYTHON SOURCE LINES 73-86 .. code-block:: default scenario = create_scenario( disciplines, formulation="MDF", objective_name="y_4", maximize_objective=True, design_space=design_space, ) scenario.set_differentiation_method("user") all_constraints = ["g_1", "g_2", "g_3"] for constraint in all_constraints: scenario.add_constraint(constraint, "ineq") scenario.execute({"algo": "SLSQP", "max_iter": 10}) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 09:25:16: INFO - 09:25:16: *** Start MDO Scenario execution *** INFO - 09:25:16: MDOScenario INFO - 09:25:16: Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiStructure SobieskiMission INFO - 09:25:16: MDOFormulation: MDF INFO - 09:25:16: Algorithm: SLSQP INFO - 09:25:16: Optimization problem: INFO - 09:25:16: Minimize: -y_4(x_shared, x_1, x_2, x_3) INFO - 09:25:16: With respect to: x_shared, x_1, x_2, x_3 INFO - 09:25:16: Subject to constraints: INFO - 09:25:16: g_1(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 09:25:16: g_2(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 09:25:16: g_3(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 09:25:16: Design Space: INFO - 09:25:16: +----------+-------------+-------+-------------+-------+ INFO - 09:25:16: | name | lower_bound | value | upper_bound | type | INFO - 09:25:16: +----------+-------------+-------+-------------+-------+ INFO - 09:25:16: | x_shared | 0.01 | 0.05 | 0.09 | float | INFO - 09:25:16: | x_shared | 30000 | 45000 | 60000 | float | INFO - 09:25:16: | x_shared | 1.4 | 1.6 | 1.8 | float | INFO - 09:25:16: | x_shared | 2.5 | 5.5 | 8.5 | float | INFO - 09:25:16: | x_shared | 40 | 55 | 70 | float | INFO - 09:25:16: | x_shared | 500 | 1000 | 1500 | float | INFO - 09:25:16: | x_1 | 0.1 | 0.25 | 0.4 | float | INFO - 09:25:16: | x_1 | 0.75 | 1 | 1.25 | float | INFO - 09:25:16: | x_2 | 0.75 | 1 | 1.25 | float | INFO - 09:25:16: | x_3 | 0.1 | 0.5 | 1 | float | INFO - 09:25:16: +----------+-------------+-------+-------------+-------+ INFO - 09:25:16: Optimization: 0%| | 0/10 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_constraints_history.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_