ad_test.py

from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.api import Problem, Group, ExecComp, IndepVarComp
from openmdao.devtools.generate_derivative import GenerateDerivative

class Paraboloid(ExplicitComponent):
    """
    Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3.
    """

    def setup(self):
        self.add_input('x', val=0.0)
        self.add_input('y', val=0.0)

        self.add_output('f_xy', val=0.0)

        self.declare_partials('*', '*')


    @GenerateDerivative
    def compute(self, inputs, outputs):
        """
        f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3

        Optimal solution (minimum): x = 6.6667; y = -7.3333
        """
        x = inputs['x']
        y = inputs['y']

        outputs['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0

    def compute_partials(self, inputs, partials):
        """
        Jacobian for our paraboloid.
        """
        return


model = Group()
ivc = IndepVarComp()
ivc.add_output('x', 3.0)
ivc.add_output('y', -4.0)
model.add_subsystem('des_vars', ivc)
model.add_subsystem('parab_comp', Paraboloid())

model.connect('des_vars.x', 'parab_comp.x')
model.connect('des_vars.y', 'parab_comp.y')

prob = Problem(model)
prob.setup()
prob.run_model()


print(prob['parab_comp.f_xy'])

prob['des_vars.x'] = 5.0
prob['des_vars.y'] = -2.0
prob.run_model()

prob.check_partials(compact_print=True)