Compare JAX and NumPy for the resolution of the Sobieski's SSBJ problem.
Note
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Compare JAX and NumPy for the resolution of the Sobieski's SSBJ problem.
from __future__ import annotations
from gemseo import configure
from gemseo import configure_logger
from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace
from gemseo.problems.mdo.sobieski.disciplines import SobieskiAerodynamics
from gemseo.problems.mdo.sobieski.disciplines import SobieskiMission
from gemseo.problems.mdo.sobieski.disciplines import SobieskiPropulsion
from gemseo.problems.mdo.sobieski.disciplines import SobieskiStructure
from gemseo.scenarios.doe_scenario import DOEScenario
from gemseo.scenarios.mdo_scenario import MDOScenario
from gemseo_jax.problems.sobieski.chain import JAXSobieskiChain
# Deactivate some checkers to speed up calculations in presence of cheap disciplines.
configure(False, False, True, False, False, False, False)
configure_logger()
Out:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/networkx/utils/backends.py:135: RuntimeWarning: networkx backend defined more than once: nx-loopback
backends.update(_get_backends("networkx.backends"))
<frozen importlib._bootstrap>:228: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute
<frozen importlib._bootstrap>:228: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute
<frozen importlib._bootstrap>:228: DeprecationWarning: builtin type swigvarlink has no __module__ attribute
<RootLogger root (INFO)>
DOE¶
MDF with MDAJacobi¶
Solve the Sobieski's SSBJ problem with JAX¶
chain = JAXSobieskiChain()
doe_scenario = DOEScenario(
[chain],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
)
doe_scenario.add_constraint("g_1", "ineq")
doe_scenario.add_constraint("g_2", "ineq")
doe_scenario.add_constraint("g_3", "ineq")
doe_scenario.execute(algo_name="OT_OPT_LHS", n_samples=100)
Out:
INFO - 13:31:47: Unable to initialize backend 'cuda':
INFO - 13:31:47: Unable to initialize backend 'rocm': module 'jaxlib.xla_extension' has no attribute 'GpuAllocatorConfig'
INFO - 13:31:47: Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory
INFO - 13:31:49: Compilation of the output function JAXSobieskiChain: 0:00:00.340010 seconds.
INFO - 13:31:49: Compilation of the Jacobian function JAXSobieskiChain: 0:00:01.357947 seconds.
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
INFO - 13:31:49: Variable y_14 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:49: Variable y_24 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:49: Variable y_34 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:49: Variable y_23 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:49: Variable y_21 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:49:
INFO - 13:31:49: *** Start DOEScenario execution ***
INFO - 13:31:49: DOEScenario
INFO - 13:31:49: Disciplines: JAXSobieskiChain
INFO - 13:31:49: MDO formulation: MDF
INFO - 13:31:49: Optimization problem:
INFO - 13:31:49: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:49: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:49: subject to constraints:
INFO - 13:31:49: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:49: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:49: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:49: over the design space:
INFO - 13:31:49: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:49: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:49: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:49: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:49: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:49: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:49: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:49: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:49: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:49: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:49: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:49: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:49: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:49: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:49: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 13:31:49: 1%| | 1/100 [00:00<00:00, 174.94 it/sec, obj=-1.25e+3]
INFO - 13:31:49: 2%|▏ | 2/100 [00:00<00:00, 209.34 it/sec, obj=-303]
INFO - 13:31:49: 3%|▎ | 3/100 [00:00<00:00, 194.97 it/sec, obj=-447]
INFO - 13:31:49: 4%|▍ | 4/100 [00:00<00:00, 195.84 it/sec, obj=-444]
INFO - 13:31:49: 5%|▌ | 5/100 [00:00<00:00, 202.15 it/sec, obj=-804]
INFO - 13:31:49: 6%|▌ | 6/100 [00:00<00:00, 206.46 it/sec, obj=-232]
INFO - 13:31:49: 7%|▋ | 7/100 [00:00<00:00, 208.32 it/sec, obj=-701]
INFO - 13:31:49: 8%|▊ | 8/100 [00:00<00:00, 210.55 it/sec, obj=-253]
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INFO - 13:31:50: 86%|████████▌ | 86/100 [00:00<00:00, 209.69 it/sec, obj=-381]
INFO - 13:31:50: 87%|████████▋ | 87/100 [00:00<00:00, 209.99 it/sec, obj=-447]
INFO - 13:31:50: 88%|████████▊ | 88/100 [00:00<00:00, 209.87 it/sec, obj=-707]
INFO - 13:31:50: 89%|████████▉ | 89/100 [00:00<00:00, 210.49 it/sec, obj=-384]
INFO - 13:31:50: 90%|█████████ | 90/100 [00:00<00:00, 210.67 it/sec, obj=-491]
INFO - 13:31:50: 91%|█████████ | 91/100 [00:00<00:00, 210.91 it/sec, obj=-285]
INFO - 13:31:50: 92%|█████████▏| 92/100 [00:00<00:00, 210.82 it/sec, obj=-433]
INFO - 13:31:50: 93%|█████████▎| 93/100 [00:00<00:00, 210.71 it/sec, obj=-377]
INFO - 13:31:50: 94%|█████████▍| 94/100 [00:00<00:00, 210.96 it/sec, obj=-827]
INFO - 13:31:50: 95%|█████████▌| 95/100 [00:00<00:00, 210.83 it/sec, obj=-712]
INFO - 13:31:50: 96%|█████████▌| 96/100 [00:00<00:00, 211.09 it/sec, obj=-245]
INFO - 13:31:50: 97%|█████████▋| 97/100 [00:00<00:00, 211.30 it/sec, obj=-502]
INFO - 13:31:50: 98%|█████████▊| 98/100 [00:00<00:00, 210.85 it/sec, obj=-309]
INFO - 13:31:50: 99%|█████████▉| 99/100 [00:00<00:00, 211.07 it/sec, obj=-971]
INFO - 13:31:50: 100%|██████████| 100/100 [00:00<00:00, 210.95 it/sec, obj=-649]
INFO - 13:31:50: Optimization result:
INFO - 13:31:50: Optimizer info:
INFO - 13:31:50: Status: None
INFO - 13:31:50: Message: None
INFO - 13:31:50: Number of calls to the objective function by the optimizer: 0
INFO - 13:31:50: Solution:
INFO - 13:31:50: The solution is feasible.
INFO - 13:31:50: Objective: -659.1819334138517
INFO - 13:31:50: Standardized constraints:
INFO - 13:31:50: g_1 = [-0.11936975 -0.11352202 -0.10911984 -0.10600546 -0.10373211 -0.04196619
INFO - 13:31:50: -0.19803381]
INFO - 13:31:50: g_2 = -0.007088218675791014
INFO - 13:31:50: g_3 = [-0.43705891 -0.56294109 -0.64490518 -0.08117258]
INFO - 13:31:50: Design space:
INFO - 13:31:50: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:50: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:50: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:50: | x_shared[0] | 0.01 | 0.05822794533105225 | 0.09 | float |
INFO - 13:31:50: | x_shared[1] | 30000 | 44461.15862672209 | 60000 | float |
INFO - 13:31:50: | x_shared[2] | 1.4 | 1.58903403609043 | 1.8 | float |
INFO - 13:31:50: | x_shared[3] | 2.5 | 2.757102870923486 | 8.5 | float |
INFO - 13:31:50: | x_shared[4] | 40 | 68.73596231164589 | 70 | float |
INFO - 13:31:50: | x_shared[5] | 500 | 723.9554384869837 | 1500 | float |
INFO - 13:31:50: | x_1[0] | 0.1 | 0.2359336189021384 | 0.4 | float |
INFO - 13:31:50: | x_1[1] | 0.75 | 0.8931955972189334 | 1.25 | float |
INFO - 13:31:50: | x_2 | 0.75 | 0.9978936102804572 | 1.25 | float |
INFO - 13:31:50: | x_3 | 0.1 | 0.1551593150888611 | 1 | float |
INFO - 13:31:50: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:50: *** End DOEScenario execution (time: 0:00:00.533798) ***
Solve the Sobieski's SSBJ problem with NumPy¶
doe_scenario = DOEScenario(
[
SobieskiStructure(),
SobieskiAerodynamics(),
SobieskiPropulsion(),
SobieskiMission(),
],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
)
doe_scenario.add_constraint("g_1", "ineq")
doe_scenario.add_constraint("g_2", "ineq")
doe_scenario.add_constraint("g_3", "ineq")
doe_scenario.execute(algo_name="OT_OPT_LHS", n_samples=100)
Out:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
WARNING - 13:31:50: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
WARNING - 13:31:50: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
INFO - 13:31:50:
INFO - 13:31:50: *** Start DOEScenario execution ***
INFO - 13:31:50: DOEScenario
INFO - 13:31:50: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:31:50: MDO formulation: MDF
INFO - 13:31:50: Optimization problem:
INFO - 13:31:50: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:50: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:50: subject to constraints:
INFO - 13:31:50: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:50: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:50: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:50: over the design space:
INFO - 13:31:50: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:50: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:50: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:50: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:50: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:50: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:50: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:50: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:50: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:50: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:50: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:50: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:50: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:50: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:50: Solving optimization problem with algorithm OT_OPT_LHS:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/gemseo/problems/mdo/sobieski/core/utils.py:225: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
ai_coeff[index] = -(f_bound[2] - f_bound[0]) / (2 * mtx_shifted[0, 1])
INFO - 13:31:50: 1%| | 1/100 [00:00<00:01, 53.26 it/sec, obj=-1.25e+3]
INFO - 13:31:50: 2%|▏ | 2/100 [00:00<00:01, 55.61 it/sec, obj=-303]
INFO - 13:31:50: 3%|▎ | 3/100 [00:00<00:02, 47.03 it/sec, obj=-447]
INFO - 13:31:50: 4%|▍ | 4/100 [00:00<00:02, 45.25 it/sec, obj=-444]
INFO - 13:31:50: 5%|▌ | 5/100 [00:00<00:02, 46.78 it/sec, obj=-804]
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INFO - 13:31:52: 91%|█████████ | 91/100 [00:01<00:00, 46.73 it/sec, obj=-285]
INFO - 13:31:52: 92%|█████████▏| 92/100 [00:01<00:00, 46.76 it/sec, obj=-433]
INFO - 13:31:52: 93%|█████████▎| 93/100 [00:01<00:00, 46.79 it/sec, obj=-377]
INFO - 13:31:52: 94%|█████████▍| 94/100 [00:02<00:00, 46.82 it/sec, obj=-827]
INFO - 13:31:52: 95%|█████████▌| 95/100 [00:02<00:00, 46.86 it/sec, obj=-712]
INFO - 13:31:52: 96%|█████████▌| 96/100 [00:02<00:00, 46.94 it/sec, obj=-245]
INFO - 13:31:52: 97%|█████████▋| 97/100 [00:02<00:00, 47.07 it/sec, obj=-502]
INFO - 13:31:52: 98%|█████████▊| 98/100 [00:02<00:00, 46.91 it/sec, obj=-309]
INFO - 13:31:52: 99%|█████████▉| 99/100 [00:02<00:00, 46.90 it/sec, obj=-971]
INFO - 13:31:52: 100%|██████████| 100/100 [00:02<00:00, 46.90 it/sec, obj=-649]
INFO - 13:31:52: Optimization result:
INFO - 13:31:52: Optimizer info:
INFO - 13:31:52: Status: None
INFO - 13:31:52: Message: None
INFO - 13:31:52: Number of calls to the objective function by the optimizer: 0
INFO - 13:31:52: Solution:
INFO - 13:31:52: The solution is feasible.
INFO - 13:31:52: Objective: -659.181929189823
INFO - 13:31:52: Standardized constraints:
INFO - 13:31:52: g_1 = [-0.11936975 -0.11352202 -0.10911984 -0.10600546 -0.10373211 -0.04196619
INFO - 13:31:52: -0.19803381]
INFO - 13:31:52: g_2 = -0.007088218675791014
INFO - 13:31:52: g_3 = [-0.43705891 -0.56294109 -0.64490518 -0.08117258]
INFO - 13:31:52: Design space:
INFO - 13:31:52: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:52: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:52: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:52: | x_shared[0] | 0.01 | 0.05822794533105225 | 0.09 | float |
INFO - 13:31:52: | x_shared[1] | 30000 | 44461.15862672209 | 60000 | float |
INFO - 13:31:52: | x_shared[2] | 1.4 | 1.58903403609043 | 1.8 | float |
INFO - 13:31:52: | x_shared[3] | 2.5 | 2.757102870923486 | 8.5 | float |
INFO - 13:31:52: | x_shared[4] | 40 | 68.73596231164589 | 70 | float |
INFO - 13:31:52: | x_shared[5] | 500 | 723.9554384869837 | 1500 | float |
INFO - 13:31:52: | x_1[0] | 0.1 | 0.2359336189021384 | 0.4 | float |
INFO - 13:31:52: | x_1[1] | 0.75 | 0.8931955972189334 | 1.25 | float |
INFO - 13:31:52: | x_2 | 0.75 | 0.9978936102804572 | 1.25 | float |
INFO - 13:31:52: | x_3 | 0.1 | 0.1551593150888611 | 1 | float |
INFO - 13:31:52: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:52: *** End DOEScenario execution (time: 0:00:02.151497) ***
Conclusion¶
JAX is about 3 times faster than NumPy in the case of a sampling loop with a Jacobi MDA.
MDF with MDAGaussSeidel¶
Solve the Sobieski's SSBJ problem with JAX¶
chain = JAXSobieskiChain()
doe_scenario = DOEScenario(
[chain],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
main_mda_settings={"inner_mda_name": "MDAGaussSeidel"},
)
doe_scenario.add_constraint("g_1", "ineq")
doe_scenario.add_constraint("g_2", "ineq")
doe_scenario.add_constraint("g_3", "ineq")
doe_scenario.execute(algo_name="OT_OPT_LHS", n_samples=100)
Out:
INFO - 13:31:53: Compilation of the output function JAXSobieskiChain: 0:00:00.302696 seconds.
INFO - 13:31:53: Compilation of the Jacobian function JAXSobieskiChain: 0:00:00.950674 seconds.
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
INFO - 13:31:53: Variable y_14 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:53: Variable y_24 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:53: Variable y_34 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:53: Variable y_23 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:53: Variable y_21 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:53:
INFO - 13:31:53: *** Start DOEScenario execution ***
INFO - 13:31:53: DOEScenario
INFO - 13:31:53: Disciplines: JAXSobieskiChain
INFO - 13:31:53: MDO formulation: MDF
INFO - 13:31:53: Optimization problem:
INFO - 13:31:53: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:53: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:53: subject to constraints:
INFO - 13:31:53: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: over the design space:
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:53: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:53: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:53: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:53: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:53: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:53: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:53: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:53: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:53: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: Solving optimization problem with algorithm OT_OPT_LHS:
INFO - 13:31:53: 1%| | 1/100 [00:00<00:00, 387.64 it/sec, obj=-1.25e+3]
INFO - 13:31:53: 2%|▏ | 2/100 [00:00<00:00, 390.19 it/sec, obj=-303]
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INFO - 13:31:53: 100%|██████████| 100/100 [00:00<00:00, 301.98 it/sec, obj=-649]
INFO - 13:31:53: Optimization result:
INFO - 13:31:53: Optimizer info:
INFO - 13:31:53: Status: None
INFO - 13:31:53: Message: None
INFO - 13:31:53: Number of calls to the objective function by the optimizer: 0
INFO - 13:31:53: Solution:
INFO - 13:31:53: The solution is feasible.
INFO - 13:31:53: Objective: -659.1818108437288
INFO - 13:31:53: Standardized constraints:
INFO - 13:31:53: g_1 = [-0.11936975 -0.11352202 -0.10911984 -0.10600546 -0.10373211 -0.04196619
INFO - 13:31:53: -0.19803381]
INFO - 13:31:53: g_2 = -0.007088218675791014
INFO - 13:31:53: g_3 = [-0.43705892 -0.56294108 -0.64490518 -0.08117258]
INFO - 13:31:53: Design space:
INFO - 13:31:53: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:53: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:53: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:53: | x_shared[0] | 0.01 | 0.05822794533105225 | 0.09 | float |
INFO - 13:31:53: | x_shared[1] | 30000 | 44461.15862672209 | 60000 | float |
INFO - 13:31:53: | x_shared[2] | 1.4 | 1.58903403609043 | 1.8 | float |
INFO - 13:31:53: | x_shared[3] | 2.5 | 2.757102870923486 | 8.5 | float |
INFO - 13:31:53: | x_shared[4] | 40 | 68.73596231164589 | 70 | float |
INFO - 13:31:53: | x_shared[5] | 500 | 723.9554384869837 | 1500 | float |
INFO - 13:31:53: | x_1[0] | 0.1 | 0.2359336189021384 | 0.4 | float |
INFO - 13:31:53: | x_1[1] | 0.75 | 0.8931955972189334 | 1.25 | float |
INFO - 13:31:53: | x_2 | 0.75 | 0.9978936102804572 | 1.25 | float |
INFO - 13:31:53: | x_3 | 0.1 | 0.1551593150888611 | 1 | float |
INFO - 13:31:53: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:53: *** End DOEScenario execution (time: 0:00:00.350495) ***
Solve the Sobieski's SSBJ problem with NumPy¶
doe_scenario = DOEScenario(
[
SobieskiStructure(),
SobieskiAerodynamics(),
SobieskiPropulsion(),
SobieskiMission(),
],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
main_mda_settings={"inner_mda_name": "MDAGaussSeidel"},
)
doe_scenario.add_constraint("g_1", "ineq")
doe_scenario.add_constraint("g_2", "ineq")
doe_scenario.add_constraint("g_3", "ineq")
doe_scenario.execute(algo_name="OT_OPT_LHS", n_samples=100)
Out:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
WARNING - 13:31:53: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
WARNING - 13:31:53: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
INFO - 13:31:53:
INFO - 13:31:53: *** Start DOEScenario execution ***
INFO - 13:31:53: DOEScenario
INFO - 13:31:53: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:31:53: MDO formulation: MDF
INFO - 13:31:53: Optimization problem:
INFO - 13:31:53: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:53: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:53: subject to constraints:
INFO - 13:31:53: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:53: over the design space:
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:53: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:53: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:53: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:53: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:53: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:53: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:53: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:53: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:53: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:53: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:53: Solving optimization problem with algorithm OT_OPT_LHS:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/gemseo/problems/mdo/sobieski/core/utils.py:225: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
ai_coeff[index] = -(f_bound[2] - f_bound[0]) / (2 * mtx_shifted[0, 1])
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INFO - 13:31:54: 98%|█████████▊| 98/100 [00:00<00:00, 107.01 it/sec, obj=-309]
INFO - 13:31:54: 99%|█████████▉| 99/100 [00:00<00:00, 107.13 it/sec, obj=-971]
INFO - 13:31:54: 100%|██████████| 100/100 [00:00<00:00, 106.60 it/sec, obj=-649]
INFO - 13:31:54: Optimization result:
INFO - 13:31:54: Optimizer info:
INFO - 13:31:54: Status: None
INFO - 13:31:54: Message: None
INFO - 13:31:54: Number of calls to the objective function by the optimizer: 0
INFO - 13:31:54: Solution:
INFO - 13:31:54: The solution is feasible.
INFO - 13:31:54: Objective: -659.1819417003436
INFO - 13:31:54: Standardized constraints:
INFO - 13:31:54: g_1 = [-0.11936975 -0.11352202 -0.10911984 -0.10600546 -0.10373211 -0.04196619
INFO - 13:31:54: -0.19803381]
INFO - 13:31:54: g_2 = -0.007088218675791014
INFO - 13:31:54: g_3 = [-0.43705892 -0.56294108 -0.64490518 -0.08117258]
INFO - 13:31:54: Design space:
INFO - 13:31:54: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:54: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:54: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:54: | x_shared[0] | 0.01 | 0.05822794533105225 | 0.09 | float |
INFO - 13:31:54: | x_shared[1] | 30000 | 44461.15862672209 | 60000 | float |
INFO - 13:31:54: | x_shared[2] | 1.4 | 1.58903403609043 | 1.8 | float |
INFO - 13:31:54: | x_shared[3] | 2.5 | 2.757102870923486 | 8.5 | float |
INFO - 13:31:54: | x_shared[4] | 40 | 68.73596231164589 | 70 | float |
INFO - 13:31:54: | x_shared[5] | 500 | 723.9554384869837 | 1500 | float |
INFO - 13:31:54: | x_1[0] | 0.1 | 0.2359336189021384 | 0.4 | float |
INFO - 13:31:54: | x_1[1] | 0.75 | 0.8931955972189334 | 1.25 | float |
INFO - 13:31:54: | x_2 | 0.75 | 0.9978936102804572 | 1.25 | float |
INFO - 13:31:54: | x_3 | 0.1 | 0.1551593150888611 | 1 | float |
INFO - 13:31:54: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:31:54: *** End DOEScenario execution (time: 0:00:00.957319) ***
Conclusion¶
JAX is about 3 times faster than NumPy in the case of a sampling loop with a Gauss-Seidel MDA.
Optimization¶
MDF with MDAJacobi¶
Solve the Sobieski's SSBJ problem with JAX¶
chain = JAXSobieskiChain()
mod_scenario = MDOScenario(
[chain],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
)
mod_scenario.add_constraint("g_1", "ineq")
mod_scenario.add_constraint("g_2", "ineq")
mod_scenario.add_constraint("g_3", "ineq")
mod_scenario.execute(algo_name="NLOPT_COBYLA", max_iter=100)
Out:
INFO - 13:31:56: Compilation of the output function JAXSobieskiChain: 0:00:00.303666 seconds.
INFO - 13:31:56: Compilation of the Jacobian function JAXSobieskiChain: 0:00:01.121086 seconds.
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
INFO - 13:31:56: Variable y_14 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:56: Variable y_24 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:56: Variable y_34 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:56: Variable y_23 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:56: Variable y_21 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:31:56:
INFO - 13:31:56: *** Start MDOScenario execution ***
INFO - 13:31:56: MDOScenario
INFO - 13:31:56: Disciplines: JAXSobieskiChain
INFO - 13:31:56: MDO formulation: MDF
INFO - 13:31:56: Optimization problem:
INFO - 13:31:56: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:56: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:56: subject to constraints:
INFO - 13:31:56: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:56: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:56: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:56: over the design space:
INFO - 13:31:56: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:56: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:56: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:56: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:56: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:56: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:56: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:56: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:56: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:56: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:56: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:56: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:56: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:56: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:56: Solving optimization problem with algorithm NLOPT_COBYLA:
INFO - 13:31:56: 1%| | 1/100 [00:00<00:00, 302.27 it/sec, obj=-536]
INFO - 13:31:56: 2%|▏ | 2/100 [00:00<00:00, 214.84 it/sec, obj=-539]
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INFO - 13:31:56: 93%|█████████▎| 93/100 [00:00<00:00, 141.06 it/sec, obj=-3.41e+3]
INFO - 13:31:56: 94%|█████████▍| 94/100 [00:00<00:00, 141.19 it/sec, obj=-3.77e+3]
INFO - 13:31:56: 95%|█████████▌| 95/100 [00:00<00:00, 141.32 it/sec, obj=-3.96e+3]
INFO - 13:31:56: 96%|█████████▌| 96/100 [00:00<00:00, 141.27 it/sec, obj=-3.48e+3]
INFO - 13:31:57: 97%|█████████▋| 97/100 [00:00<00:00, 141.20 it/sec, obj=-3.41e+3]
INFO - 13:31:57: 98%|█████████▊| 98/100 [00:00<00:00, 141.29 it/sec, obj=-3.97e+3]
INFO - 13:31:57: 99%|█████████▉| 99/100 [00:00<00:00, 141.04 it/sec, obj=-3.29e+3]
INFO - 13:31:57: 100%|██████████| 100/100 [00:00<00:00, 140.93 it/sec, obj=-3.33e+3]
INFO - 13:31:57: Optimization result:
INFO - 13:31:57: Optimizer info:
INFO - 13:31:57: Status: None
INFO - 13:31:57: Message: Maximum number of iterations reached. GEMSEO stopped the driver.
INFO - 13:31:57: Number of calls to the objective function by the optimizer: 0
INFO - 13:31:57: Solution:
INFO - 13:31:57: The solution is feasible.
INFO - 13:31:57: Objective: -3963.5978188452605
INFO - 13:31:57: Standardized constraints:
INFO - 13:31:57: g_1 = [-0.01827806 -0.0334989 -0.04436674 -0.05192983 -0.05740621 -0.13708086
INFO - 13:31:57: -0.10291914]
INFO - 13:31:57: g_2 = 2.8205413821202896e-05
INFO - 13:31:57: g_3 = [-7.67134672e-01 -2.32865328e-01 -9.95245644e-06 -1.83255000e-01]
INFO - 13:31:57: Design space:
INFO - 13:31:57: +-------------+-------------+--------------------+-------------+-------+
INFO - 13:31:57: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:57: +-------------+-------------+--------------------+-------------+-------+
INFO - 13:31:57: | x_shared[0] | 0.01 | 0.0600070513534553 | 0.09 | float |
INFO - 13:31:57: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:31:57: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 13:31:57: | x_shared[3] | 2.5 | 2.500000000000004 | 8.5 | float |
INFO - 13:31:57: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:31:57: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 13:31:57: | x_1[0] | 0.1 | 0.3992532360683517 | 0.4 | float |
INFO - 13:31:57: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 13:31:57: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 13:31:57: | x_3 | 0.1 | 0.1562431906272621 | 1 | float |
INFO - 13:31:57: +-------------+-------------+--------------------+-------------+-------+
INFO - 13:31:57: *** End MDOScenario execution (time: 0:00:00.717845) ***
Solve the Sobieski's SSBJ problem with NumPy¶
mod_scenario = MDOScenario(
[
SobieskiStructure(),
SobieskiAerodynamics(),
SobieskiPropulsion(),
SobieskiMission(),
],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
)
mod_scenario.add_constraint("g_1", "ineq")
mod_scenario.add_constraint("g_2", "ineq")
mod_scenario.add_constraint("g_3", "ineq")
mod_scenario.execute(algo_name="NLOPT_COBYLA", max_iter=100)
Out:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
WARNING - 13:31:57: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
WARNING - 13:31:57: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
INFO - 13:31:57:
INFO - 13:31:57: *** Start MDOScenario execution ***
INFO - 13:31:57: MDOScenario
INFO - 13:31:57: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:31:57: MDO formulation: MDF
INFO - 13:31:57: Optimization problem:
INFO - 13:31:57: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:31:57: with respect to x_1, x_2, x_3, x_shared
INFO - 13:31:57: subject to constraints:
INFO - 13:31:57: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:57: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:57: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:31:57: over the design space:
INFO - 13:31:57: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:57: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:31:57: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:57: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:31:57: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:31:57: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:31:57: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:31:57: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:31:57: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:31:57: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:31:57: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:31:57: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:31:57: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:31:57: +-------------+-------------+-------+-------------+-------+
INFO - 13:31:57: Solving optimization problem with algorithm NLOPT_COBYLA:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/gemseo/problems/mdo/sobieski/core/utils.py:225: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
ai_coeff[index] = -(f_bound[2] - f_bound[0]) / (2 * mtx_shifted[0, 1])
INFO - 13:31:57: 1%| | 1/100 [00:00<00:02, 48.04 it/sec, obj=-536]
INFO - 13:31:57: 2%|▏ | 2/100 [00:00<00:02, 42.74 it/sec, obj=-539]
INFO - 13:31:57: 3%|▎ | 3/100 [00:00<00:02, 42.21 it/sec, obj=-763]
INFO - 13:31:57: 4%|▍ | 4/100 [00:00<00:02, 42.09 it/sec, obj=-713]
INFO - 13:31:57: 5%|▌ | 5/100 [00:00<00:02, 41.34 it/sec, obj=-687]
INFO - 13:31:57: 6%|▌ | 6/100 [00:00<00:02, 40.28 it/sec, obj=-897]
INFO - 13:31:57: 7%|▋ | 7/100 [00:00<00:02, 40.47 it/sec, obj=-957]
INFO - 13:31:57: 8%|▊ | 8/100 [00:00<00:02, 41.08 it/sec, obj=-956]
INFO - 13:31:57: 9%|▉ | 9/100 [00:00<00:02, 41.44 it/sec, obj=-930]
INFO - 13:31:57: 10%|█ | 10/100 [00:00<00:02, 41.15 it/sec, obj=-943]
INFO - 13:31:57: 11%|█ | 11/100 [00:00<00:02, 41.38 it/sec, obj=-768]
INFO - 13:31:57: 12%|█▏ | 12/100 [00:00<00:02, 39.26 it/sec, obj=-1e+3]
INFO - 13:31:57: 13%|█▎ | 13/100 [00:00<00:02, 37.71 it/sec, obj=-1.34e+3]
INFO - 13:31:57: 14%|█▍ | 14/100 [00:00<00:02, 36.82 it/sec, obj=-1.85e+3]
INFO - 13:31:57: 15%|█▌ | 15/100 [00:00<00:02, 35.46 it/sec, obj=-1.87e+3]
WARNING - 13:31:57: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.7204854871194414e-05 is still above the tolerance 1e-06.
INFO - 13:31:57: 16%|█▌ | 16/100 [00:00<00:02, 34.06 it/sec, obj=-1.99e+3]
INFO - 13:31:57: 17%|█▋ | 17/100 [00:00<00:02, 33.50 it/sec, obj=-2.18e+3]
INFO - 13:31:57: 18%|█▊ | 18/100 [00:00<00:02, 32.88 it/sec, obj=-2.2e+3]
INFO - 13:31:57: 19%|█▉ | 19/100 [00:00<00:02, 32.60 it/sec, obj=-2.42e+3]
WARNING - 13:31:57: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.9335592270087927e-05 is still above the tolerance 1e-06.
INFO - 13:31:57: 20%|██ | 20/100 [00:00<00:02, 31.56 it/sec, obj=-2.37e+3]
INFO - 13:31:57: 21%|██ | 21/100 [00:00<00:02, 31.41 it/sec, obj=-2.83e+3]
INFO - 13:31:57: 22%|██▏ | 22/100 [00:00<00:02, 30.83 it/sec, obj=-3.12e+3]
INFO - 13:31:57: 23%|██▎ | 23/100 [00:00<00:02, 30.71 it/sec, obj=-3.21e+3]
INFO - 13:31:57: 24%|██▍ | 24/100 [00:00<00:02, 30.71 it/sec, obj=-3.99e+3]
WARNING - 13:31:57: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.6602480542748623e-05 is still above the tolerance 1e-06.
INFO - 13:31:57: 25%|██▌ | 25/100 [00:00<00:02, 30.21 it/sec, obj=-2.6e+3]
INFO - 13:31:57: 26%|██▌ | 26/100 [00:00<00:02, 29.97 it/sec, obj=-3.06e+3]
INFO - 13:31:57: 27%|██▋ | 27/100 [00:00<00:02, 29.95 it/sec, obj=-2.98e+3]
WARNING - 13:31:57: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.131065719805529e-06 is still above the tolerance 1e-06.
INFO - 13:31:57: 28%|██▊ | 28/100 [00:00<00:02, 29.55 it/sec, obj=-3.24e+3]
INFO - 13:31:58: 29%|██▉ | 29/100 [00:00<00:02, 29.41 it/sec, obj=-3.67e+3]
INFO - 13:31:58: 30%|███ | 30/100 [00:01<00:02, 29.38 it/sec, obj=-3.16e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 0.00020046262987076315 is still above the tolerance 1e-06.
INFO - 13:31:58: 31%|███ | 31/100 [00:01<00:02, 29.00 it/sec, obj=-3.09e+3]
INFO - 13:31:58: 32%|███▏ | 32/100 [00:01<00:02, 28.67 it/sec, obj=-3.47e+3]
INFO - 13:31:58: 33%|███▎ | 33/100 [00:01<00:02, 28.42 it/sec, obj=-3.52e+3]
INFO - 13:31:58: 34%|███▍ | 34/100 [00:01<00:02, 28.38 it/sec, obj=-3.52e+3]
INFO - 13:31:58: 35%|███▌ | 35/100 [00:01<00:02, 28.18 it/sec, obj=-3.2e+3]
INFO - 13:31:58: 36%|███▌ | 36/100 [00:01<00:02, 28.08 it/sec, obj=-3.52e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.5187500368837417e-06 is still above the tolerance 1e-06.
INFO - 13:31:58: 37%|███▋ | 37/100 [00:01<00:02, 27.77 it/sec, obj=-2.96e+3]
INFO - 13:31:58: 38%|███▊ | 38/100 [00:01<00:02, 27.79 it/sec, obj=-3.18e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.3580820715243732e-06 is still above the tolerance 1e-06.
INFO - 13:31:58: 39%|███▉ | 39/100 [00:01<00:02, 27.58 it/sec, obj=-3.15e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.0068765557476064e-05 is still above the tolerance 1e-06.
INFO - 13:31:58: 40%|████ | 40/100 [00:01<00:02, 27.40 it/sec, obj=-3.92e+3]
INFO - 13:31:58: 41%|████ | 41/100 [00:01<00:02, 27.38 it/sec, obj=-3.96e+3]
INFO - 13:31:58: 42%|████▏ | 42/100 [00:01<00:02, 27.39 it/sec, obj=-3.96e+3]
INFO - 13:31:58: 43%|████▎ | 43/100 [00:01<00:02, 27.36 it/sec, obj=-3.96e+3]
INFO - 13:31:58: 44%|████▍ | 44/100 [00:01<00:02, 27.27 it/sec, obj=-3.75e+3]
INFO - 13:31:58: 45%|████▌ | 45/100 [00:01<00:02, 27.27 it/sec, obj=-3.94e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.9699912018918902e-06 is still above the tolerance 1e-06.
INFO - 13:31:58: 46%|████▌ | 46/100 [00:01<00:01, 27.18 it/sec, obj=-3.38e+3]
INFO - 13:31:58: 47%|████▋ | 47/100 [00:01<00:01, 27.27 it/sec, obj=-3.79e+3]
INFO - 13:31:58: 48%|████▊ | 48/100 [00:01<00:01, 27.15 it/sec, obj=-3.41e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.9604198356566634e-05 is still above the tolerance 1e-06.
INFO - 13:31:58: 49%|████▉ | 49/100 [00:01<00:01, 26.99 it/sec, obj=-3.88e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.0414068021569107e-06 is still above the tolerance 1e-06.
INFO - 13:31:58: 50%|█████ | 50/100 [00:01<00:01, 26.87 it/sec, obj=-3.56e+3]
INFO - 13:31:58: 51%|█████ | 51/100 [00:01<00:01, 26.84 it/sec, obj=-3.57e+3]
WARNING - 13:31:58: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 4.339805325609159e-06 is still above the tolerance 1e-06.
INFO - 13:31:58: 52%|█████▏ | 52/100 [00:01<00:01, 26.71 it/sec, obj=-3.31e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.903076918194352e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 53%|█████▎ | 53/100 [00:01<00:01, 26.64 it/sec, obj=-3.18e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 4.222419054177024e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 54%|█████▍ | 54/100 [00:02<00:01, 26.49 it/sec, obj=-3.27e+3]
INFO - 13:31:59: 55%|█████▌ | 55/100 [00:02<00:01, 26.48 it/sec, obj=-3.83e+3]
INFO - 13:31:59: 56%|█████▌ | 56/100 [00:02<00:01, 26.55 it/sec, obj=-3.75e+3]
INFO - 13:31:59: 57%|█████▋ | 57/100 [00:02<00:01, 26.51 it/sec, obj=-3.42e+3]
INFO - 13:31:59: 58%|█████▊ | 58/100 [00:02<00:01, 26.38 it/sec, obj=-3.51e+3]
INFO - 13:31:59: 59%|█████▉ | 59/100 [00:02<00:01, 26.29 it/sec, obj=-3.62e+3]
INFO - 13:31:59: 60%|██████ | 60/100 [00:02<00:01, 26.24 it/sec, obj=-3.5e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.439216568543729e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 61%|██████ | 61/100 [00:02<00:01, 26.12 it/sec, obj=-3.46e+3]
INFO - 13:31:59: 62%|██████▏ | 62/100 [00:02<00:01, 26.11 it/sec, obj=-3.34e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 6.1842511238495055e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 63%|██████▎ | 63/100 [00:02<00:01, 26.01 it/sec, obj=-3.36e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.6372363218085485e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 64%|██████▍ | 64/100 [00:02<00:01, 25.93 it/sec, obj=-3.19e+3]
INFO - 13:31:59: 65%|██████▌ | 65/100 [00:02<00:01, 25.85 it/sec, obj=-3.45e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 6.279609664777154e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 66%|██████▌ | 66/100 [00:02<00:01, 25.79 it/sec, obj=-3.45e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 4.405999600169342e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 67%|██████▋ | 67/100 [00:02<00:01, 25.71 it/sec, obj=-3.45e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.4425135716984906e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 68%|██████▊ | 68/100 [00:02<00:01, 25.65 it/sec, obj=-3.32e+3]
INFO - 13:31:59: 69%|██████▉ | 69/100 [00:02<00:01, 25.58 it/sec, obj=-3.42e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.662998088252376e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 70%|███████ | 70/100 [00:02<00:01, 25.52 it/sec, obj=-3.54e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.981553508096536e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 71%|███████ | 71/100 [00:02<00:01, 25.48 it/sec, obj=-3.3e+3]
WARNING - 13:31:59: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.529222752819427e-06 is still above the tolerance 1e-06.
INFO - 13:31:59: 72%|███████▏ | 72/100 [00:02<00:01, 25.44 it/sec, obj=-3.34e+3]
INFO - 13:31:59: 73%|███████▎ | 73/100 [00:02<00:01, 25.39 it/sec, obj=-3.14e+3]
INFO - 13:31:59: 74%|███████▍ | 74/100 [00:02<00:01, 25.43 it/sec, obj=-3.64e+3]
INFO - 13:32:00: 75%|███████▌ | 75/100 [00:02<00:00, 25.39 it/sec, obj=-3.39e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.6083054685158987e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 76%|███████▌ | 76/100 [00:03<00:00, 25.33 it/sec, obj=-3.11e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.712717311801625e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 77%|███████▋ | 77/100 [00:03<00:00, 25.26 it/sec, obj=-3.51e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.1988893016520166e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 78%|███████▊ | 78/100 [00:03<00:00, 25.22 it/sec, obj=-3.07e+3]
INFO - 13:32:00: 79%|███████▉ | 79/100 [00:03<00:00, 25.25 it/sec, obj=-3.69e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 3.3277311553287673e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 80%|████████ | 80/100 [00:03<00:00, 25.20 it/sec, obj=-3.08e+3]
INFO - 13:32:00: 81%|████████ | 81/100 [00:03<00:00, 25.16 it/sec, obj=-3.39e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 5.388041776135559e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 82%|████████▏ | 82/100 [00:03<00:00, 25.13 it/sec, obj=-3.5e+3]
INFO - 13:32:00: 83%|████████▎ | 83/100 [00:03<00:00, 25.07 it/sec, obj=-3.08e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.3902672321401141e-05 is still above the tolerance 1e-06.
INFO - 13:32:00: 84%|████████▍ | 84/100 [00:03<00:00, 25.02 it/sec, obj=-3.79e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 8.258646891354083e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 85%|████████▌ | 85/100 [00:03<00:00, 24.99 it/sec, obj=-3.67e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.0805763550512402e-05 is still above the tolerance 1e-06.
INFO - 13:32:00: 86%|████████▌ | 86/100 [00:03<00:00, 24.94 it/sec, obj=-3.87e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 2.9796750242748394e-06 is still above the tolerance 1e-06.
INFO - 13:32:00: 87%|████████▋ | 87/100 [00:03<00:00, 24.91 it/sec, obj=-3.38e+3]
INFO - 13:32:00: 88%|████████▊ | 88/100 [00:03<00:00, 24.92 it/sec, obj=-3.96e+3]
INFO - 13:32:00: 89%|████████▉ | 89/100 [00:03<00:00, 24.94 it/sec, obj=-3.94e+3]
INFO - 13:32:00: 90%|█████████ | 90/100 [00:03<00:00, 24.96 it/sec, obj=-3.96e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.7338183877226514e-05 is still above the tolerance 1e-06.
INFO - 13:32:00: 91%|█████████ | 91/100 [00:03<00:00, 24.91 it/sec, obj=-3.92e+3]
WARNING - 13:32:00: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.5198708905547502e-05 is still above the tolerance 1e-06.
INFO - 13:32:00: 92%|█████████▏| 92/100 [00:03<00:00, 24.88 it/sec, obj=-3.9e+3]
INFO - 13:32:00: 93%|█████████▎| 93/100 [00:03<00:00, 24.89 it/sec, obj=-3.96e+3]
INFO - 13:32:00: 94%|█████████▍| 94/100 [00:03<00:00, 24.92 it/sec, obj=-3.92e+3]
INFO - 13:32:00: 95%|█████████▌| 95/100 [00:03<00:00, 24.96 it/sec, obj=-3.96e+3]
INFO - 13:32:00: 96%|█████████▌| 96/100 [00:03<00:00, 24.97 it/sec, obj=-3.94e+3]
INFO - 13:32:00: 97%|█████████▋| 97/100 [00:03<00:00, 24.99 it/sec, obj=-3.92e+3]
INFO - 13:32:00: 98%|█████████▊| 98/100 [00:03<00:00, 25.01 it/sec, obj=-3.92e+3]
INFO - 13:32:01: 99%|█████████▉| 99/100 [00:03<00:00, 25.02 it/sec, obj=-3.91e+3]
WARNING - 13:32:01: MDAJacobi has reached its maximum number of iterations, but the normalized residual norm 1.842849391330206e-05 is still above the tolerance 1e-06.
INFO - 13:32:01: 100%|██████████| 100/100 [00:04<00:00, 24.97 it/sec, obj=-3.93e+3]
INFO - 13:32:01: Optimization result:
INFO - 13:32:01: Optimizer info:
INFO - 13:32:01: Status: None
INFO - 13:32:01: Message: Maximum number of iterations reached. GEMSEO stopped the driver.
INFO - 13:32:01: Number of calls to the objective function by the optimizer: 0
INFO - 13:32:01: Solution:
INFO - 13:32:01: The solution is feasible.
INFO - 13:32:01: Objective: -3963.3793053483255
INFO - 13:32:01: Standardized constraints:
INFO - 13:32:01: g_1 = [-0.01805093 -0.03333915 -0.04424381 -0.05182998 -0.05732217 -0.13720865
INFO - 13:32:01: -0.10279135]
INFO - 13:32:01: g_2 = 0.0
INFO - 13:32:01: g_3 = [-7.67185581e-01 -2.32814419e-01 -1.20219524e-06 -1.83255000e-01]
INFO - 13:32:01: Design space:
INFO - 13:32:01: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:01: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:32:01: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:01: | x_shared[0] | 0.01 | 0.05999999999999999 | 0.09 | float |
INFO - 13:32:01: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:32:01: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 13:32:01: | x_shared[3] | 2.5 | 2.500000000000015 | 8.5 | float |
INFO - 13:32:01: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:32:01: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 13:32:01: | x_1[0] | 0.1 | 0.4 | 0.4 | float |
INFO - 13:32:01: | x_1[1] | 0.75 | 0.7500000000000009 | 1.25 | float |
INFO - 13:32:01: | x_2 | 0.75 | 0.7500000000000089 | 1.25 | float |
INFO - 13:32:01: | x_3 | 0.1 | 0.1562445578095981 | 1 | float |
INFO - 13:32:01: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:01: *** End MDOScenario execution (time: 0:00:04.013561) ***
Conclusion¶
JAX is about 4 times faster than NumPy in the case of an optimization loop with a Jacobi MDA.
MDF with MDAGaussSeidel¶
Solve the Sobieski's SSBJ problem with JAX¶
chain = JAXSobieskiChain()
mod_scenario = MDOScenario(
[chain],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
main_mda_settings={"inner_mda_name": "MDAGaussSeidel"},
)
mod_scenario.add_constraint("g_1", "ineq")
mod_scenario.add_constraint("g_2", "ineq")
mod_scenario.add_constraint("g_3", "ineq")
mod_scenario.execute(algo_name="NLOPT_COBYLA", max_iter=100)
Out:
INFO - 13:32:02: Compilation of the output function JAXSobieskiChain: 0:00:00.305794 seconds.
INFO - 13:32:02: Compilation of the Jacobian function JAXSobieskiChain: 0:00:00.956701 seconds.
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
INFO - 13:32:02: Variable y_14 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:32:02: Variable y_24 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:32:02: Variable y_34 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:32:02: Variable y_23 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:32:02: Variable y_21 was removed from the Design Space, it is not an input of any discipline.
INFO - 13:32:02:
INFO - 13:32:02: *** Start MDOScenario execution ***
INFO - 13:32:02: MDOScenario
INFO - 13:32:02: Disciplines: JAXSobieskiChain
INFO - 13:32:02: MDO formulation: MDF
INFO - 13:32:02: Optimization problem:
INFO - 13:32:02: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:32:02: with respect to x_1, x_2, x_3, x_shared
INFO - 13:32:02: subject to constraints:
INFO - 13:32:02: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: over the design space:
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:32:02: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:32:02: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:32:02: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:32:02: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:32:02: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:32:02: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:32:02: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:32:02: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:32:02: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: Solving optimization problem with algorithm NLOPT_COBYLA:
INFO - 13:32:02: 1%| | 1/100 [00:00<00:00, 315.93 it/sec, obj=-536]
INFO - 13:32:02: 2%|▏ | 2/100 [00:00<00:00, 223.85 it/sec, obj=-539]
INFO - 13:32:02: 3%|▎ | 3/100 [00:00<00:00, 214.38 it/sec, obj=-763]
INFO - 13:32:02: 4%|▍ | 4/100 [00:00<00:00, 211.21 it/sec, obj=-713]
INFO - 13:32:02: 5%|▌ | 5/100 [00:00<00:00, 206.84 it/sec, obj=-687]
INFO - 13:32:02: 6%|▌ | 6/100 [00:00<00:00, 204.38 it/sec, obj=-897]
INFO - 13:32:02: 7%|▋ | 7/100 [00:00<00:00, 201.29 it/sec, obj=-957]
INFO - 13:32:02: 8%|▊ | 8/100 [00:00<00:00, 191.79 it/sec, obj=-956]
INFO - 13:32:02: 9%|▉ | 9/100 [00:00<00:00, 183.76 it/sec, obj=-930]
INFO - 13:32:02: 10%|█ | 10/100 [00:00<00:00, 183.17 it/sec, obj=-943]
INFO - 13:32:02: 11%|█ | 11/100 [00:00<00:00, 182.89 it/sec, obj=-768]
INFO - 13:32:02: 12%|█▏ | 12/100 [00:00<00:00, 180.92 it/sec, obj=-1e+3]
INFO - 13:32:02: 13%|█▎ | 13/100 [00:00<00:00, 177.45 it/sec, obj=-1.34e+3]
INFO - 13:32:02: 14%|█▍ | 14/100 [00:00<00:00, 173.89 it/sec, obj=-1.85e+3]
WARNING - 13:32:02: MDAGaussSeidel has reached its maximum number of iterations, but the normalized residual norm 1.5897449643940727e-05 is still above the tolerance 1e-06.
INFO - 13:32:02: 15%|█▌ | 15/100 [00:00<00:00, 169.52 it/sec, obj=-1.87e+3]
WARNING - 13:32:02: MDAGaussSeidel has reached its maximum number of iterations, but the normalized residual norm 3.1764060178304437e-06 is still above the tolerance 1e-06.
INFO - 13:32:02: 16%|█▌ | 16/100 [00:00<00:00, 166.16 it/sec, obj=-1.99e+3]
WARNING - 13:32:02: MDAGaussSeidel has reached its maximum number of iterations, but the normalized residual norm 1.2161314217002324e-06 is still above the tolerance 1e-06.
INFO - 13:32:02: 17%|█▋ | 17/100 [00:00<00:00, 163.26 it/sec, obj=-2.18e+3]
INFO - 13:32:02: 18%|█▊ | 18/100 [00:00<00:00, 160.56 it/sec, obj=-2.51e+3]
INFO - 13:32:02: 19%|█▉ | 19/100 [00:00<00:00, 159.87 it/sec, obj=-2.92e+3]
INFO - 13:32:02: 20%|██ | 20/100 [00:00<00:00, 160.09 it/sec, obj=-3.03e+3]
INFO - 13:32:02: 21%|██ | 21/100 [00:00<00:00, 161.41 it/sec, obj=-3.16e+3]
INFO - 13:32:02: 22%|██▏ | 22/100 [00:00<00:00, 160.24 it/sec, obj=-2.75e+3]
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INFO - 13:32:02: 26%|██▌ | 26/100 [00:00<00:00, 164.87 it/sec, obj=-3.3e+3]
INFO - 13:32:02: 27%|██▋ | 27/100 [00:00<00:00, 166.29 it/sec, obj=-3.22e+3]
INFO - 13:32:02: 28%|██▊ | 28/100 [00:00<00:00, 165.82 it/sec, obj=-3.79e+3]
INFO - 13:32:02: 29%|██▉ | 29/100 [00:00<00:00, 165.62 it/sec, obj=-3.59e+3]
INFO - 13:32:02: 30%|███ | 30/100 [00:00<00:00, 165.46 it/sec, obj=-3.63e+3]
INFO - 13:32:02: 31%|███ | 31/100 [00:00<00:00, 165.38 it/sec, obj=-3.69e+3]
INFO - 13:32:02: 32%|███▏ | 32/100 [00:00<00:00, 165.53 it/sec, obj=-3.42e+3]
INFO - 13:32:02: 33%|███▎ | 33/100 [00:00<00:00, 166.14 it/sec, obj=-3.47e+3]
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INFO - 13:32:02: 35%|███▌ | 35/100 [00:00<00:00, 166.15 it/sec, obj=-3.94e+3]
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INFO - 13:32:02: 40%|████ | 40/100 [00:00<00:00, 164.12 it/sec, obj=-3.39e+3]
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INFO - 13:32:02: 56%|█████▌ | 56/100 [00:00<00:00, 161.66 it/sec, obj=-3.93e+3]
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INFO - 13:32:02: 60%|██████ | 60/100 [00:00<00:00, 161.51 it/sec, obj=-3.93e+3]
INFO - 13:32:02: 61%|██████ | 61/100 [00:00<00:00, 161.63 it/sec, obj=-3.84e+3]
INFO - 13:32:02: 62%|██████▏ | 62/100 [00:00<00:00, 161.70 it/sec, obj=-4.29e+3]
INFO - 13:32:02: 63%|██████▎ | 63/100 [00:00<00:00, 161.55 it/sec, obj=-3.27e+3]
INFO - 13:32:02: 64%|██████▍ | 64/100 [00:00<00:00, 161.58 it/sec, obj=-4.07e+3]
INFO - 13:32:02: 65%|██████▌ | 65/100 [00:00<00:00, 161.65 it/sec, obj=-3.09e+3]
INFO - 13:32:02: 66%|██████▌ | 66/100 [00:00<00:00, 161.71 it/sec, obj=-4.22e+3]
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INFO - 13:32:02: 78%|███████▊ | 78/100 [00:00<00:00, 160.97 it/sec, obj=-3.66e+3]
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INFO - 13:32:02: 80%|████████ | 80/100 [00:00<00:00, 160.71 it/sec, obj=-3.65e+3]
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INFO - 13:32:02: 89%|████████▉ | 89/100 [00:00<00:00, 159.83 it/sec, obj=-3.97e+3]
INFO - 13:32:02: 90%|█████████ | 90/100 [00:00<00:00, 159.84 it/sec, obj=-3.92e+3]
INFO - 13:32:02: 91%|█████████ | 91/100 [00:00<00:00, 159.85 it/sec, obj=-3.96e+3]
INFO - 13:32:02: 92%|█████████▏| 92/100 [00:00<00:00, 159.84 it/sec, obj=-3.95e+3]
INFO - 13:32:02: 93%|█████████▎| 93/100 [00:00<00:00, 159.87 it/sec, obj=-3.95e+3]
INFO - 13:32:02: 94%|█████████▍| 94/100 [00:00<00:00, 159.90 it/sec, obj=-3.95e+3]
INFO - 13:32:02: 95%|█████████▌| 95/100 [00:00<00:00, 159.86 it/sec, obj=-3.91e+3]
INFO - 13:32:02: 96%|█████████▌| 96/100 [00:00<00:00, 159.86 it/sec, obj=-3.96e+3]
INFO - 13:32:02: 97%|█████████▋| 97/100 [00:00<00:00, 159.88 it/sec, obj=-3.96e+3]
INFO - 13:32:02: 98%|█████████▊| 98/100 [00:00<00:00, 159.91 it/sec, obj=-3.96e+3]
INFO - 13:32:02: 99%|█████████▉| 99/100 [00:00<00:00, 159.89 it/sec, obj=-3.96e+3]
INFO - 13:32:02: 100%|██████████| 100/100 [00:00<00:00, 159.87 it/sec, obj=-3.96e+3]
INFO - 13:32:02: Optimization result:
INFO - 13:32:02: Optimizer info:
INFO - 13:32:02: Status: None
INFO - 13:32:02: Message: Maximum number of iterations reached. GEMSEO stopped the driver.
INFO - 13:32:02: Number of calls to the objective function by the optimizer: 0
INFO - 13:32:02: Solution:
INFO - 13:32:02: The solution is feasible.
INFO - 13:32:02: Objective: -3960.4216625697427
INFO - 13:32:02: Standardized constraints:
INFO - 13:32:02: g_1 = [-0.05722668 -0.06051502 -0.06502273 -0.06864369 -0.07143946 -0.10339509
INFO - 13:32:02: -0.13660491]
INFO - 13:32:02: g_2 = -2.220446049250313e-16
INFO - 13:32:02: g_3 = [-7.67261040e-01 -2.32738960e-01 -7.20534743e-14 -1.83255000e-01]
INFO - 13:32:02: Design space:
INFO - 13:32:02: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:02: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:32:02: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:02: | x_shared[0] | 0.01 | 0.05999999999999997 | 0.09 | float |
INFO - 13:32:02: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:32:02: | x_shared[2] | 1.4 | 1.4 | 1.8 | float |
INFO - 13:32:02: | x_shared[3] | 2.5 | 2.5 | 8.5 | float |
INFO - 13:32:02: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:32:02: | x_shared[5] | 500 | 1500 | 1500 | float |
INFO - 13:32:02: | x_1[0] | 0.1 | 0.2198966756241587 | 0.4 | float |
INFO - 13:32:02: | x_1[1] | 0.75 | 0.75 | 1.25 | float |
INFO - 13:32:02: | x_2 | 0.75 | 0.75 | 1.25 | float |
INFO - 13:32:02: | x_3 | 0.1 | 0.1562447456462762 | 1 | float |
INFO - 13:32:02: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:02: *** End MDOScenario execution (time: 0:00:00.633472) ***
Solve the Sobieski's SSBJ problem with NumPy¶
mod_scenario = MDOScenario(
[
SobieskiStructure(),
SobieskiAerodynamics(),
SobieskiPropulsion(),
SobieskiMission(),
],
"y_4",
SobieskiDesignSpace(),
maximize_objective=True,
formulation_name="MDF",
main_mda_settings={"inner_mda_name": "MDAGaussSeidel"},
)
mod_scenario.add_constraint("g_1", "ineq")
mod_scenario.add_constraint("g_2", "ineq")
mod_scenario.add_constraint("g_3", "ineq")
mod_scenario.execute(algo_name="NLOPT_COBYLA", max_iter=100)
Out:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/pydantic/main.py:209: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
WARNING - 13:32:02: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
WARNING - 13:32:02: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
INFO - 13:32:02:
INFO - 13:32:02: *** Start MDOScenario execution ***
INFO - 13:32:02: MDOScenario
INFO - 13:32:02: Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
INFO - 13:32:02: MDO formulation: MDF
INFO - 13:32:02: Optimization problem:
INFO - 13:32:02: minimize -y_4(x_shared, x_1, x_2, x_3)
INFO - 13:32:02: with respect to x_1, x_2, x_3, x_shared
INFO - 13:32:02: subject to constraints:
INFO - 13:32:02: g_1(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: g_2(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: g_3(x_shared, x_1, x_2, x_3) <= 0
INFO - 13:32:02: over the design space:
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: | x_shared[0] | 0.01 | 0.05 | 0.09 | float |
INFO - 13:32:02: | x_shared[1] | 30000 | 45000 | 60000 | float |
INFO - 13:32:02: | x_shared[2] | 1.4 | 1.6 | 1.8 | float |
INFO - 13:32:02: | x_shared[3] | 2.5 | 5.5 | 8.5 | float |
INFO - 13:32:02: | x_shared[4] | 40 | 55 | 70 | float |
INFO - 13:32:02: | x_shared[5] | 500 | 1000 | 1500 | float |
INFO - 13:32:02: | x_1[0] | 0.1 | 0.25 | 0.4 | float |
INFO - 13:32:02: | x_1[1] | 0.75 | 1 | 1.25 | float |
INFO - 13:32:02: | x_2 | 0.75 | 1 | 1.25 | float |
INFO - 13:32:02: | x_3 | 0.1 | 0.5 | 1 | float |
INFO - 13:32:02: +-------------+-------------+-------+-------------+-------+
INFO - 13:32:02: Solving optimization problem with algorithm NLOPT_COBYLA:
/builds/gemseo/dev/gemseo-jax/.tox/doc/lib64/python3.9/site-packages/gemseo/problems/mdo/sobieski/core/utils.py:225: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
ai_coeff[index] = -(f_bound[2] - f_bound[0]) / (2 * mtx_shifted[0, 1])
INFO - 13:32:03: 1%| | 1/100 [00:00<00:00, 101.14 it/sec, obj=-536]
INFO - 13:32:03: 2%|▏ | 2/100 [00:00<00:01, 78.31 it/sec, obj=-539]
INFO - 13:32:03: 3%|▎ | 3/100 [00:00<00:01, 76.12 it/sec, obj=-763]
INFO - 13:32:03: 4%|▍ | 4/100 [00:00<00:01, 74.58 it/sec, obj=-713]
INFO - 13:32:03: 5%|▌ | 5/100 [00:00<00:01, 74.00 it/sec, obj=-687]
INFO - 13:32:03: 6%|▌ | 6/100 [00:00<00:01, 72.78 it/sec, obj=-897]
INFO - 13:32:03: 7%|▋ | 7/100 [00:00<00:01, 71.23 it/sec, obj=-957]
INFO - 13:32:03: 8%|▊ | 8/100 [00:00<00:01, 70.04 it/sec, obj=-956]
INFO - 13:32:03: 9%|▉ | 9/100 [00:00<00:01, 66.68 it/sec, obj=-930]
INFO - 13:32:03: 10%|█ | 10/100 [00:00<00:01, 66.27 it/sec, obj=-943]
INFO - 13:32:03: 11%|█ | 11/100 [00:00<00:01, 65.97 it/sec, obj=-768]
INFO - 13:32:03: 12%|█▏ | 12/100 [00:00<00:01, 65.18 it/sec, obj=-1e+3]
INFO - 13:32:03: 13%|█▎ | 13/100 [00:00<00:01, 64.26 it/sec, obj=-1.34e+3]
INFO - 13:32:03: 14%|█▍ | 14/100 [00:00<00:01, 63.20 it/sec, obj=-1.85e+3]
WARNING - 13:32:03: MDAGaussSeidel has reached its maximum number of iterations, but the normalized residual norm 1.0691449634588397e-05 is still above the tolerance 1e-06.
INFO - 13:32:03: 15%|█▌ | 15/100 [00:00<00:01, 61.51 it/sec, obj=-1.87e+3]
WARNING - 13:32:03: MDAGaussSeidel has reached its maximum number of iterations, but the normalized residual norm 1.6881473826143826e-06 is still above the tolerance 1e-06.
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INFO - 13:32:04: 95%|█████████▌| 95/100 [00:01<00:00, 65.95 it/sec, obj=-4.21e+3]
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INFO - 13:32:04: 97%|█████████▋| 97/100 [00:01<00:00, 66.03 it/sec, obj=-4.19e+3]
INFO - 13:32:04: 98%|█████████▊| 98/100 [00:01<00:00, 66.07 it/sec, obj=-4.49e+3]
INFO - 13:32:04: 99%|█████████▉| 99/100 [00:01<00:00, 66.06 it/sec, obj=-4.13e+3]
INFO - 13:32:04: 100%|██████████| 100/100 [00:01<00:00, 66.08 it/sec, obj=-4.49e+3]
INFO - 13:32:04: Optimization result:
INFO - 13:32:04: Optimizer info:
INFO - 13:32:04: Status: None
INFO - 13:32:04: Message: Maximum number of iterations reached. GEMSEO stopped the driver.
INFO - 13:32:04: Number of calls to the objective function by the optimizer: 0
INFO - 13:32:04: Solution:
INFO - 13:32:04: The solution is feasible.
INFO - 13:32:04: Objective: -3003.8995230595424
INFO - 13:32:04: Standardized constraints:
INFO - 13:32:04: g_1 = [-0.18177087 -0.15303684 -0.13797373 -0.12871311 -0.12244655 -0.02429768
INFO - 13:32:04: -0.21570232]
INFO - 13:32:04: g_2 = -0.002615771711972803
INFO - 13:32:04: g_3 = [-0.78613294 -0.21386706 -0.07631583 -0.17779628]
INFO - 13:32:04: Design space:
INFO - 13:32:04: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:04: | Name | Lower bound | Value | Upper bound | Type |
INFO - 13:32:04: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:04: | x_shared[0] | 0.01 | 0.05934605707200682 | 0.09 | float |
INFO - 13:32:04: | x_shared[1] | 30000 | 60000 | 60000 | float |
INFO - 13:32:04: | x_shared[2] | 1.4 | 1.440947137798596 | 1.8 | float |
INFO - 13:32:04: | x_shared[3] | 2.5 | 2.682168463995586 | 8.5 | float |
INFO - 13:32:04: | x_shared[4] | 40 | 70 | 70 | float |
INFO - 13:32:04: | x_shared[5] | 500 | 1315.825005558175 | 1500 | float |
INFO - 13:32:04: | x_1[0] | 0.1 | 0.1000000000000001 | 0.4 | float |
INFO - 13:32:04: | x_1[1] | 0.75 | 0.9200062439484883 | 1.25 | float |
INFO - 13:32:04: | x_2 | 0.75 | 0.8112680369735645 | 1.25 | float |
INFO - 13:32:04: | x_3 | 0.1 | 0.1498242691956934 | 1 | float |
INFO - 13:32:04: +-------------+-------------+---------------------+-------------+-------+
INFO - 13:32:04: *** End MDOScenario execution (time: 0:00:01.520483) ***
Conclusion¶
JAX is about 3 times faster than NumPy in the case of an optimization loop with a Gauss-Seidel MDA.
Total running time of the script: ( 0 minutes 22.480 seconds)
Download Python source code: plot_sobieski_mdf.py