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Ich versuche, das Paraboloid-Beispiel aus der OpenMDAO-Dokumentation auszuführen, aber den ScipyOptimizer zu verwenden, und ich bekomme den Fehler im Titel. Ich bin mir nicht sicher, was ich falsch mache. Ich habe unten ein MWE angehängt.OpenMDAO/ScipyOptimizer "UnboundLocalError: lokale Variable 'f_new' referenziert vor Zuweisung"
OpenMDAO 1.7.3, Python 3.6.0.
# -*- coding: utf-8 -*-
"""
Paraboloid tutorial from OpenMDAO documentation:
https://openmdao.readthedocs.io/en/latest/usr-guide/tutorials/paraboloid-tutorial.html
"""
# import print function from Python 3 if we are using Python 2
from __future__ import print_function
# import all the components we need from openmdao module
from openmdao.api import IndepVarComp, Component, Problem, Group, ScipyOptimizer
class Paraboloid(Component):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """
def __init__(self):
super(Paraboloid, self).__init__()
self.add_param('x', val=0.0)
self.add_param('y', val=0.0)
self.add_output('f_xy', shape=1)
def solve_nonlinear(self, params, unknowns, resids):
"""f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3
"""
x = params['x']
y = params['y']
unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0
def linearize(self, params, unknowns, resids):
""" Jacobian for our paraboloid."""
x = params['x']
y = params['y']
J = {}
J['f_xy', 'x'] = 2.0*x - 6.0 + y
J['f_xy', 'y'] = 2.0*y + 8.0 + x
return J
# This if statement executes only if the script is run as a script, not if the
# script is imported as a module. It's not necessary for this demo, but it
# is good coding practice.
if __name__ == "__main__":
# initiallize the overall problem
top = Problem()
# each problem has a root "group" that contains the component evaluating
# the objective function
root = top.root = Group()
# define components with the independent variables and their initial guesses
root.add('p1', IndepVarComp('x', 3.0))
root.add('p2', IndepVarComp('y', -4.0))
# add the component that defines our objective function
root.add('p', Paraboloid())
# connect the components together
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
# specify our driver for the problem (optional)
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['disp'] = True # don't display optimizer output
# set up the problem
top.setup()
# run the optimization
top.run()
print(top['p.f_xy'])