.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/post_process/plot_pareto_front_binhkorn.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_pareto_front_binhkorn.py: Pareto front on Binh and Korn problem ===================================== In this example, we illustrate the use of the :class:`.ParetoFront` plot on the Binh and Korn multi-objective problem. .. GENERATED FROM PYTHON SOURCE LINES 30-36 .. code-block:: default from __future__ import unicode_literals from matplotlib import pyplot as plt from gemseo.algos.doe.doe_factory import DOEFactory .. GENERATED FROM PYTHON SOURCE LINES 37-41 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 41-48 .. code-block:: default from gemseo.api import configure_logger from gemseo.post.post_factory import PostFactory from gemseo.problems.analytical.binh_korn import BinhKorn configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 49-52 Import the optimization problem ------------------------------- Then, we instantiate the BinkKorn optimization problem. .. GENERATED FROM PYTHON SOURCE LINES 52-55 .. code-block:: default problem = BinhKorn() .. GENERATED FROM PYTHON SOURCE LINES 56-60 Create and execute scenario --------------------------- Then, we create a Design of Experiment factory, and we request the execution a a full-factorial DOE using 100 samples .. GENERATED FROM PYTHON SOURCE LINES 60-63 .. code-block:: default doe_factory = DOEFactory() doe_factory.execute(problem, algo_name="OT_OPT_LHS", n_samples=100) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 09:25:33: Optimization problem: INFO - 09:25:33: Minimize: compute_binhkorn(x, y) = (4*x**2+ 4*y**2, (x-5.)**2 + (y-5.)**2) INFO - 09:25:33: With respect to: x, y INFO - 09:25:33: Subject to constraints: INFO - 09:25:33: ineq1(x, y): (x-5.)**2 + y**2 <= 25. <= 0.0 INFO - 09:25:33: ineq2(x, y): (x-8.)**2 + (y+3)**2 >= 7.7 <= 0.0 INFO - 09:25:33: Generation of OT_OPT_LHS DOE with OpenTurns INFO - 09:25:33: Creating default composed distribution based on Uniform INFO - 09:25:33: Creation of a uniform distribution INFO - 09:25:33: DOE sampling: 0%| | 0/100 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_pareto_front_binhkorn.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_