Analytical test case # 2

In this example, we consider a simple optimization problem to illustrate algorithms interfaces and optimization libraries integration.

Imports

from __future__ import division, unicode_literals

from numpy import cos, exp, ones, sin

from gemseo.algos.design_space import DesignSpace
from gemseo.algos.doe.doe_factory import DOEFactory
from gemseo.algos.opt.opt_factory import OptimizersFactory
from gemseo.algos.opt_problem import OptimizationProblem
from gemseo.api import configure_logger, execute_post
from gemseo.core.mdofunctions.mdo_function import MDOFunction

configure_logger()

Out:

<RootLogger root (INFO)>

Define the objective function

We define the objective function \(f(x)=sin(x)-exp(x)\) using a MDOFunction defined by the sum of MDOFunction objects.

f_1 = MDOFunction(sin, name="f_1", jac=cos, expr="sin(x)")
f_2 = MDOFunction(exp, name="f_2", jac=exp, expr="exp(x)")
objective = f_1 - f_2

See also

The following operators are implemented: addition, subtraction and multiplication. The minus operator is also defined.

Define the design space

Then, we define the DesignSpace with GEMSEO.

design_space = DesignSpace()
design_space.add_variable("x", 1, l_b=-2.0, u_b=2.0, value=-0.5 * ones(1))

Define the optimization problem

Then, we define the OptimizationProblem with GEMSEO.

problem = OptimizationProblem(design_space)
problem.objective = objective

Solve the optimization problem using an optimization algorithm

Finally, we solve the optimization problems with GEMSEO interface.

Solve the problem

opt = OptimizersFactory().execute(problem, "L-BFGS-B", normalize_design_space=True)

print("Optimum = ", opt)

Out:

    INFO - 14:41:15: Optimization problem:
    INFO - 14:41:15:    Minimize: f_1-f_2 = sin(x)-exp(x)
    INFO - 14:41:15:    With respect to: x
    INFO - 14:41:15: Design space:
    INFO - 14:41:15: +------+-------------+-------+-------------+-------+
    INFO - 14:41:15: | name | lower_bound | value | upper_bound | type  |
    INFO - 14:41:15: +------+-------------+-------+-------------+-------+
    INFO - 14:41:15: | x    |      -2     |  -0.5 |      2      | float |
    INFO - 14:41:15: +------+-------------+-------+-------------+-------+
    INFO - 14:41:15: Optimization:   0%|          | 0/999 [00:00<?, ?it]
    INFO - 14:41:15: Optimization:   1%|          | 7/999 [00:00<00:00, 151464.35 it/sec, obj=[-1.23610834]]
    INFO - 14:41:15: Optimization result:
    INFO - 14:41:15: Objective value = [-1.23610834]
    INFO - 14:41:15: The result is feasible.
    INFO - 14:41:15: Status: 0
    INFO - 14:41:15: Optimizer message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
    INFO - 14:41:15: Number of calls to the objective function by the optimizer: 8
    INFO - 14:41:15: Design space:
    INFO - 14:41:15: +------+-------------+--------------------+-------------+-------+
    INFO - 14:41:15: | name | lower_bound |       value        | upper_bound | type  |
    INFO - 14:41:15: +------+-------------+--------------------+-------------+-------+
    INFO - 14:41:15: | x    |      -2     | -1.292695718944152 |      2      | float |
    INFO - 14:41:15: +------+-------------+--------------------+-------------+-------+
Optimum =  Optimization result:
Objective value = [-1.23610834]
The result is feasible.
Status: 0
Optimizer message: CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
Number of calls to the objective function by the optimizer: 8

Note that you can get all the optimization algorithms names:

algo_list = OptimizersFactory().algorithms
print("Available algorithms ", algo_list)

Out:

Available algorithms  ['NLOPT_MMA', 'NLOPT_COBYLA', 'NLOPT_SLSQP', 'NLOPT_BOBYQA', 'NLOPT_BFGS', 'NLOPT_NEWUOA', 'PDFO_COBYLA', 'PDFO_BOBYQA', 'PDFO_NEWUOA', 'PSEVEN', 'PSEVEN_FD', 'PSEVEN_MOM', 'PSEVEN_NCG', 'PSEVEN_NLS', 'PSEVEN_POWELL', 'PSEVEN_QP', 'PSEVEN_SQP', 'PSEVEN_SQ2P', 'DUAL_ANNEALING', 'SHGO', 'DIFFERENTIAL_EVOLUTION', 'LINEAR_INTERIOR_POINT', 'REVISED_SIMPLEX', 'SIMPLEX', 'SLSQP', 'L-BFGS-B', 'TNC', 'SNOPTB']

Save the optimization results

We can serialize the results for further exploitation.

problem.export_hdf("my_optim.hdf5")

Out:

INFO - 14:41:15: Export optimization problem to file: my_optim.hdf5

Post-process the results

execute_post(problem, "OptHistoryView", show=True, save=False)

Out:

 WARNING - 14:41:16: Failed to create Hessian approximation.
Traceback (most recent call last):
  File "/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/stable/lib/python3.8/site-packages/gemseo/post/opt_history_view.py", line 610, in _create_hessian_approx_plot
    _, diag, _, _ = approximator.build_approximation(
  File "/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/stable/lib/python3.8/site-packages/gemseo/post/core/hessians.py", line 368, in build_approximation
    x_hist, grad_hist, _, _ = self.get_x_grad_history(
  File "/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/stable/lib/python3.8/site-packages/gemseo/post/core/hessians.py", line 172, in get_x_grad_history
    raise ValueError(
ValueError: Inconsistent gradient and design variables optimization history.

<gemseo.post.opt_history_view.OptHistoryView object at 0x7f3b596b1be0>

Note

We can also save this plot using the arguments save=False and file_path='file_path'.

Solve the optimization problem using a DOE algorithm

We can also see this optimization problem as a trade-off and solve it by means of a design of experiments (DOE).

opt = DOEFactory().execute(problem, "lhs", n_samples=10, normalize_design_space=True)
print("Optimum = ", opt)

Out:

 WARNING - 14:41:16: Driver lhs has no option normalize_design_space, option is ignored.
    INFO - 14:41:16: Optimization problem:
    INFO - 14:41:16:    Minimize: f_1-f_2 = sin(x)-exp(x)
    INFO - 14:41:16:    With respect to: x
    INFO - 14:41:16: DOE sampling:   0%|          | 0/10 [00:00<?, ?it]
    INFO - 14:41:16: DOE sampling: 100%|██████████| 10/10 [00:00<00:00, 1475.36 it/sec, obj=[-1.00069899]]
    INFO - 14:41:16: Optimization result:
    INFO - 14:41:16: Objective value = [-5.1741088]
    INFO - 14:41:16: The result is feasible.
    INFO - 14:41:16: Status: None
    INFO - 14:41:16: Optimizer message: None
    INFO - 14:41:16: Number of calls to the objective function by the optimizer: 18
    INFO - 14:41:16: Design space:
    INFO - 14:41:16: +------+-------------+-------------------+-------------+-------+
    INFO - 14:41:16: | name | lower_bound |       value       | upper_bound | type  |
    INFO - 14:41:16: +------+-------------+-------------------+-------------+-------+
    INFO - 14:41:16: | x    |      -2     | 1.815526693601343 |      2      | float |
    INFO - 14:41:16: +------+-------------+-------------------+-------------+-------+
Optimum =  Optimization result:
Objective value = [-5.1741088]
The result is feasible.
Status: None
Optimizer message: None
Number of calls to the objective function by the optimizer: 18

Total running time of the script: ( 0 minutes 0.124 seconds)

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