为C ++构建Lua(Building Lua for C++)

 使用不可能的基于渐变的优化器。 您需要使用无梯度方法。 与OpenMDAO 2.2相比，没有任何内置的方法来实施这种离散化。 您需要在问题类周围使用外部循环才能使其工作。 
 
 这是一个简单的例子： 
 
import numpy as np
from openmdao.api import Problem, ScipyOptimizeDriver, ExecComp, IndepVarComp

# build the model
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp())
indeps.add_output('x', 3.0)
indeps.add_output('y', -4.0)

prob.model.add_subsystem('paraboloid', ExecComp('f = (x-3)**2 + x*y + (y+4)**2 - 3'))

prob.model.connect('indeps.x', 'paraboloid.x')
prob.model.connect('indeps.y', 'paraboloid.y')

# setup the optimization
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'

prob.model.add_design_var('indeps.y', lower=-50, upper=50)
prob.model.add_objective('paraboloid.f')

prob.setup()


for x in np.arange(-10,12,2): 
    prob['indeps.x'] = x

    # could call just run_model if no optimization was desired
    #prob.run_model()

    # for each value of x, optimize for y
    prob.run_driver()

    # minimum value
    print(prob['paraboloid.f'])

    # location of the minimum
    print(prob['indeps.x'])
    print(prob['indeps.y'])

Using a gradient based optimizer that is not possible. You would need to use a gradient free method. As over OpenMDAO 2.2 there is not any built in way to enforce that kind of discretization. You would need to use an external loop around the problem class to get that to work. 
 
Here is a simple example: 
 
import numpy as np
from openmdao.api import Problem, ScipyOptimizeDriver, ExecComp, IndepVarComp

# build the model
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp())
indeps.add_output('x', 3.0)
indeps.add_output('y', -4.0)

prob.model.add_subsystem('paraboloid', ExecComp('f = (x-3)**2 + x*y + (y+4)**2 - 3'))

prob.model.connect('indeps.x', 'paraboloid.x')
prob.model.connect('indeps.y', 'paraboloid.y')

# setup the optimization
prob.driver = ScipyOptimizeDriver()
prob.driver.options['optimizer'] = 'SLSQP'

prob.model.add_design_var('indeps.y', lower=-50, upper=50)
prob.model.add_objective('paraboloid.f')

prob.setup()


for x in np.arange(-10,12,2): 
    prob['indeps.x'] = x

    # could call just run_model if no optimization was desired
    #prob.run_model()

    # for each value of x, optimize for y
    prob.run_driver()

    # minimum value
    print(prob['paraboloid.f'])

    # location of the minimum
    print(prob['indeps.x'])
    print(prob['indeps.y'])