Run a Driver

Once setup() is done, you can then run the optimization with run_driver().

run_driver() executes the driver, running the optimization, DOE, etc. that you’ve set up.

Examples

Set up a simple optimization problem and run it, by calling run_driver.

SellarDerivatives class definition

class SellarDerivatives(om.Group):
    """
    Group containing the Sellar MDA. This version uses the disciplines with derivatives.
    """

    def setup(self):
        self.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
        self.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

        self.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)', obj=0.0,
                                                  x=0.0, z=np.array([0.0, 0.0]), y1=0.0, y2=0.0),
                           promotes=['obj', 'x', 'z', 'y1', 'y2'])

        self.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1', con1=0.0, y1=0.0),
                           promotes=['con1', 'y1'])
        self.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0', con2=0.0, y2=0.0),
                           promotes=['con2', 'y2'])

        self.set_input_defaults('x', 1.0)
        self.set_input_defaults('z', np.array([5.0, 2.0]))

import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.sellar_feature import SellarDerivatives

prob = om.Problem(model=SellarDerivatives())

model = prob.model
model.nonlinear_solver = om.NonlinearBlockGS()
model.linear_solver = om.ScipyKrylov()

prob.driver = om.ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9

model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
model.add_design_var('x', lower=0.0, upper=10.0)
model.add_objective('obj')
model.add_constraint('con1', upper=0.0)
model.add_constraint('con2', upper=0.0)

prob.setup()
prob.run_driver()

print(prob.get_val('x'))

NL: NLBGS Converged in 8 iterations

NL: NLBGS Converged in 1 iterations

NL: NLBGS Converged in 9 iterations

NL: NLBGS Converged in 10 iterations

NL: NLBGS Converged in 10 iterations

NL: NLBGS Converged in 9 iterations

NL: NLBGS Converged in 6 iterations

Optimization terminated successfully    (Exit mode 0)
            Current function value: 3.183393951728078
            Iterations: 6
            Function evaluations: 6
            Gradient evaluations: 6
Optimization Complete
-----------------------------------
[3.56410563e-15]

print(prob.get_val('y1'))

[3.16]

print(prob.get_val('y2'))

[3.75527777]

print(prob.get_val('z'))

[1.97763888 0.        ]

print(prob.get_val('obj'))

[3.18339395]