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Parametric scalable MDO problem - MDF

We define a ScalableProblem with a shared design variable of size 1 and 2 strongly coupled disciplines. The first one has a local design variable of size 1 and a coupling variable of size 2 while the second one has a local design variable of size 3 and a coupling variable of size 4.

We would like to solve this MDO problem by means of an MDF formulation.

from __future__ import annotations

from gemseo import configure_logger
from gemseo import execute_algo
from gemseo import execute_post
from gemseo import generate_n2_plot
from gemseo.problems.scalable.parametric.core.scalable_discipline_settings import (
    ScalableDisciplineSettings,
)
from gemseo.problems.scalable.parametric.scalable_problem import ScalableProblem

configure_logger()

<RootLogger root (INFO)>

Instantiation of the scalable problem

problem = ScalableProblem(
    [ScalableDisciplineSettings(1, 2), ScalableDisciplineSettings(3, 4)], 1
)

Display the coupling structure

generate_n2_plot(problem.disciplines, save=False, show=True)
plot scalable param mdf

Solve the MDO using an MDF formulation

scenario = problem.create_scenario()
scenario.execute({"algo": "NLOPT_SLSQP", "max_iter": 100})

    INFO - 16:25:10:
    INFO - 16:25:10: *** Start MDOScenario execution ***
    INFO - 16:25:10: MDOScenario
    INFO - 16:25:10:    Disciplines: MainDiscipline ScalableDiscipline[1] ScalableDiscipline[2]
    INFO - 16:25:10:    MDO formulation: MDF
    INFO - 16:25:10: Optimization problem:
    INFO - 16:25:10:    minimize f(x_0, x_1, x_2)
    INFO - 16:25:10:    with respect to x_0, x_1, x_2
    INFO - 16:25:10:    subject to constraints:
    INFO - 16:25:10:       c_1(x_0, x_1, x_2) <= 0.0
    INFO - 16:25:10:       c_2(x_0, x_1, x_2) <= 0.0
    INFO - 16:25:10:    over the design space:
    INFO - 16:25:10:    |                             Parameter space                             |
    INFO - 16:25:10:    +------+-------------+-------+-------------+-------+----------------------+
    INFO - 16:25:10:    | name | lower_bound | value | upper_bound | type  | Initial distribution |
    INFO - 16:25:10:    +------+-------------+-------+-------------+-------+----------------------+
    INFO - 16:25:10:    | x_0  |      0      |  0.5  |      1      | float |                      |
    INFO - 16:25:10:    | x_1  |      0      |  0.5  |      1      | float |                      |
    INFO - 16:25:10:    | x_2  |      0      |  0.5  |      1      | float |                      |
    INFO - 16:25:10:    | x_2  |      0      |  0.5  |      1      | float |                      |
    INFO - 16:25:10:    | x_2  |      0      |  0.5  |      1      | float |                      |
    INFO - 16:25:10:    +------+-------------+-------+-------------+-------+----------------------+
    INFO - 16:25:10: Solving optimization problem with algorithm NLOPT_SLSQP:
    INFO - 16:25:10: ...   0%|          | 0/100 [00:00<?, ?it]
    INFO - 16:25:10: ...   1%|          | 1/100 [00:00<00:04, 20.67 it/sec, obj=1]
    INFO - 16:25:10: ...   2%|▏         | 2/100 [00:00<00:09, 10.33 it/sec, obj=0.921]
    INFO - 16:25:10: ...   3%|▎         | 3/100 [00:00<00:06, 15.46 it/sec, obj=0.513]
    INFO - 16:25:10: ...   4%|▍         | 4/100 [00:00<00:09, 10.62 it/sec, obj=0.438]
    INFO - 16:25:10: ...   5%|▌         | 5/100 [00:00<00:08, 11.26 it/sec, obj=0.418]
    INFO - 16:25:10: ...   6%|▌         | 6/100 [00:00<00:08, 11.68 it/sec, obj=0.416]
    INFO - 16:25:10: ...   7%|▋         | 7/100 [00:00<00:07, 11.96 it/sec, obj=0.415]
    INFO - 16:25:10: ...   8%|▊         | 8/100 [00:00<00:07, 12.19 it/sec, obj=0.415]
    INFO - 16:25:10: ...   9%|▉         | 9/100 [00:00<00:07, 12.38 it/sec, obj=0.415]
    INFO - 16:25:11: ...  10%|█         | 10/100 [00:00<00:07, 12.54 it/sec, obj=0.415]
    INFO - 16:25:11: ...  11%|█         | 11/100 [00:00<00:07, 12.66 it/sec, obj=0.415]
    INFO - 16:25:11: ...  12%|█▏        | 12/100 [00:00<00:06, 12.76 it/sec, obj=0.415]
    INFO - 16:25:11: ...  13%|█▎        | 13/100 [00:01<00:06, 12.85 it/sec, obj=0.415]
    INFO - 16:25:11: ...  14%|█▍        | 14/100 [00:01<00:06, 13.81 it/sec, obj=Not evaluated]
    INFO - 16:25:11: Optimization result:
    INFO - 16:25:11:    Optimizer info:
    INFO - 16:25:11:       Status: None
    INFO - 16:25:11:       Message: Successive iterates of the design variables are closer than xtol_rel or xtol_abs. GEMSEO Stopped the driver
    INFO - 16:25:11:       Number of calls to the objective function by the optimizer: 15
    INFO - 16:25:11:    Solution:
    INFO - 16:25:11:       The solution is feasible.
    INFO - 16:25:11:       Objective: 0.4147214934939103
    INFO - 16:25:11:       Standardized constraints:
    INFO - 16:25:11:          c_1 = [-0.32430624 -0.43254418]
    INFO - 16:25:11:          c_2 = [-5.55111512e-17 -2.51297019e-01 -2.35380057e-01 -4.99967998e-01]
    INFO - 16:25:11:       +---------------------------------------------------------------------------------------+
    INFO - 16:25:11:       |                                    Parameter space                                    |
    INFO - 16:25:11:       +------+-------------+---------------------+-------------+-------+----------------------+
    INFO - 16:25:11:       | name | lower_bound |        value        | upper_bound | type  | Initial distribution |
    INFO - 16:25:11:       +------+-------------+---------------------+-------------+-------+----------------------+
    INFO - 16:25:11:       | x_0  |      0      |  0.4836327085400904 |      1      | float |                      |
    INFO - 16:25:11:       | x_1  |      0      |  0.9999999999999999 |      1      | float |                      |
    INFO - 16:25:11:       | x_2  |      0      | 0.08671672721432468 |      1      | float |                      |
    INFO - 16:25:11:       | x_2  |      0      |  0.9085358010297666 |      1      | float |                      |
    INFO - 16:25:11:       | x_2  |      0      |  0.2480176715968745 |      1      | float |                      |
    INFO - 16:25:11:       +------+-------------+---------------------+-------------+-------+----------------------+
    INFO - 16:25:11: *** End MDOScenario execution (time: 0:00:01.029974) ***

{'max_iter': 100, 'algo': 'NLOPT_SLSQP'}

Post-process the results

scenario.post_process("OptHistoryView", save=False, show=True)
  • Evolution of the optimization variables
  • Evolution of the objective value
  • Distance to the optimum
  • Hessian diagonal approximation
  • Evolution of the inequality constraints
/home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/stable/lib/python3.9/site-packages/genson/schema/strategies/base.py:42: UserWarning: Schema incompatible. Keyword 'description' has conflicting values ('The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.' vs. 'The width and height of the figure in inches, e.g. `(w, h)`.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'). Using 'The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'
  warn(('Schema incompatible. Keyword {0!r} has conflicting '

<gemseo.post.opt_history_view.OptHistoryView object at 0x7ffaef9765b0>

Solve the associated quadratic programming problem

problem = problem.create_quadratic_programming_problem()
execute_algo(problem, algo_name="NLOPT_SLSQP", max_iter=100)

    INFO - 16:25:12: Optimization problem:
    INFO - 16:25:12:    minimize f = 0.5x'Qx + c'x + d
    INFO - 16:25:12:    with respect to x
    INFO - 16:25:12:    subject to constraints:
    INFO - 16:25:12:       g: Ax-b <= 0 <= 0.0
    INFO - 16:25:12:    over the design space:
    INFO - 16:25:12:    +------+-------------+-------+-------------+-------+
    INFO - 16:25:12:    | name | lower_bound | value | upper_bound | type  |
    INFO - 16:25:12:    +------+-------------+-------+-------------+-------+
    INFO - 16:25:12:    | x[0] |      0      |  0.5  |      1      | float |
    INFO - 16:25:12:    | x[1] |      0      |  0.5  |      1      | float |
    INFO - 16:25:12:    | x[2] |      0      |  0.5  |      1      | float |
    INFO - 16:25:12:    | x[3] |      0      |  0.5  |      1      | float |
    INFO - 16:25:12:    | x[4] |      0      |  0.5  |      1      | float |
    INFO - 16:25:12:    +------+-------------+-------+-------------+-------+
    INFO - 16:25:12: Solving optimization problem with algorithm NLOPT_SLSQP:
    INFO - 16:25:12: ...   0%|          | 0/100 [00:00<?, ?it]
    INFO - 16:25:12: ...   1%|          | 1/100 [00:00<00:00, 1985.94 it/sec, obj=1]
    INFO - 16:25:12: ...   2%|▏         | 2/100 [00:00<00:00, 555.87 it/sec, obj=0.921]
    INFO - 16:25:12: ...   3%|▎         | 3/100 [00:00<00:00, 775.43 it/sec, obj=0.513]
    INFO - 16:25:12: ...   4%|▍         | 4/100 [00:00<00:00, 496.93 it/sec, obj=0.438]
    INFO - 16:25:12: ...   5%|▌         | 5/100 [00:00<00:00, 506.88 it/sec, obj=0.418]
    INFO - 16:25:12: ...   6%|▌         | 6/100 [00:00<00:00, 516.54 it/sec, obj=0.416]
    INFO - 16:25:12: ...   7%|▋         | 7/100 [00:00<00:00, 522.63 it/sec, obj=0.415]
    INFO - 16:25:12: ...   8%|▊         | 8/100 [00:00<00:00, 528.77 it/sec, obj=0.415]
    INFO - 16:25:12: ...   9%|▉         | 9/100 [00:00<00:00, 532.15 it/sec, obj=0.415]
    INFO - 16:25:12: ...  10%|█         | 10/100 [00:00<00:00, 536.61 it/sec, obj=0.415]
    INFO - 16:25:12: ...  11%|█         | 11/100 [00:00<00:00, 539.31 it/sec, obj=0.415]
    INFO - 16:25:12: ...  12%|█▏        | 12/100 [00:00<00:00, 542.48 it/sec, obj=0.415]
    INFO - 16:25:12: ...  13%|█▎        | 13/100 [00:00<00:00, 581.07 it/sec, obj=0.415]
    INFO - 16:25:12: Optimization result:
    INFO - 16:25:12:    Optimizer info:
    INFO - 16:25:12:       Status: None
    INFO - 16:25:12:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
    INFO - 16:25:12:       Number of calls to the objective function by the optimizer: 15
    INFO - 16:25:12:    Solution:
    INFO - 16:25:12:       The solution is feasible.
    INFO - 16:25:12:       Objective: 0.414721553468123
    INFO - 16:25:12:       Standardized constraints:
    INFO - 16:25:12:          g = [-3.24306238e-01 -4.32544172e-01 -4.44089210e-16 -2.51297007e-01
    INFO - 16:25:12:  -2.35380057e-01 -4.99968031e-01]
    INFO - 16:25:12:       Design space:
    INFO - 16:25:12:       +------+-------------+---------------------+-------------+-------+
    INFO - 16:25:12:       | name | lower_bound |        value        | upper_bound | type  |
    INFO - 16:25:12:       +------+-------------+---------------------+-------------+-------+
    INFO - 16:25:12:       | x[0] |      0      |  0.4836327595359132 |      1      | float |
    INFO - 16:25:12:       | x[1] |      0      |  0.9999999999999998 |      1      | float |
    INFO - 16:25:12:       | x[2] |      0      | 0.08671677713547089 |      1      | float |
    INFO - 16:25:12:       | x[3] |      0      |  0.9085357909356101 |      1      | float |
    INFO - 16:25:12:       | x[4] |      0      |  0.2480176979962943 |      1      | float |
    INFO - 16:25:12:       +------+-------------+---------------------+-------------+-------+

Optimization result:
   Design variables: [0.48363276 1.         0.08671678 0.90853579 0.2480177 ]
   Objective function: 0.414721553468123
   Feasible solution: True

Post-process the results

execute_post(problem, "OptHistoryView", save=False, show=True)
  • Evolution of the optimization variables
  • Evolution of the objective value
  • Distance to the optimum
  • Hessian diagonal approximation
  • Evolution of the inequality constraints
<gemseo.post.opt_history_view.OptHistoryView object at 0x7ffaef2bb1c0>

Total running time of the script: ( 0 minutes 4.115 seconds)

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