.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/scenario_adapter/plot_multistart_example.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_scenario_adapter_plot_multistart_example.py: Multistart optimization ======================= Runs simple optimization problem with multiple starting points Nests a :class:`.MDOScenario` in a :class:`.DOEScenario` using a :class:`.MDOScenarioAdapter`. .. GENERATED FROM PYTHON SOURCE LINES 31-46 .. code-block:: default from __future__ import division, unicode_literals from matplotlib import pyplot as plt from gemseo.api import ( configure_logger, create_design_space, create_discipline, create_scenario, ) from gemseo.core.mdo_scenario import MDOScenarioAdapter configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 47-49 Create the disciplines ---------------------- .. GENERATED FROM PYTHON SOURCE LINES 49-56 .. code-block:: default objective = create_discipline( "AnalyticDiscipline", expressions_dict={"obj": "x**3-x+1"} ) constraint = create_discipline( "AnalyticDiscipline", expressions_dict={"cstr": "x**2+obj**2-1.5"} ) .. GENERATED FROM PYTHON SOURCE LINES 57-59 Create the design space ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 59-62 .. code-block:: default design_space = create_design_space() design_space.add_variable("x", 1, l_b=-1.5, u_b=1.5, value=1.5) .. GENERATED FROM PYTHON SOURCE LINES 63-65 Create the MDO scenario ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 65-74 .. code-block:: default scenario = create_scenario( [objective, constraint], formulation="DisciplinaryOpt", objective_name="obj", design_space=design_space, ) scenario.default_inputs = {"algo": "SLSQP", "max_iter": 10} scenario.add_constraint("cstr", "ineq") .. GENERATED FROM PYTHON SOURCE LINES 75-77 Create the scenario adapter --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 77-82 .. code-block:: default dv_names = scenario.formulation.opt_problem.design_space.variables_names adapter = MDOScenarioAdapter( scenario, dv_names, ["obj", "cstr"], set_x0_before_opt=True ) .. GENERATED FROM PYTHON SOURCE LINES 83-85 Create the DOE scenario ----------------------- .. GENERATED FROM PYTHON SOURCE LINES 85-96 .. code-block:: default scenario_doe = create_scenario( adapter, formulation="DisciplinaryOpt", objective_name="obj", design_space=design_space, scenario_type="DOE", ) scenario_doe.add_constraint("cstr", "ineq") run_inputs = {"n_samples": 10, "algo": "fullfact"} scenario_doe.execute(run_inputs) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 09:23:48: INFO - 09:23:48: *** Start DOE Scenario execution *** INFO - 09:23:48: DOEScenario INFO - 09:23:48: Disciplines: MDOScenario_adapter INFO - 09:23:48: MDOFormulation: DisciplinaryOpt INFO - 09:23:48: Algorithm: fullfact INFO - 09:23:48: Optimization problem: INFO - 09:23:48: Minimize: obj(x) INFO - 09:23:48: With respect to: x INFO - 09:23:48: Subject to constraints: INFO - 09:23:48: cstr(x) <= 0.0 INFO - 09:23:48: Full factorial design required. Number of samples along each direction for a design vector of size 1 with 10 samples: 10 INFO - 09:23:48: Final number of samples for DOE = 10 vs 10 requested INFO - 09:23:48: DOE sampling: 0%| | 0/10 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_multistart_example.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_