openMDAO: Optimization terminates successfully after 1 iteration, not at optimal point - openmdao

Question

I am trying to make a toy problem to learn a bit about the OpenMDAO software before applying the lessons to a larger problem. I have a problem set up so that the objective function should be minimized when both design variables are at a minimum. However both values stay at their originally assigned values despite receiving an 'Optimization terminated successfully' message.
I have been starting by writing the code based on the Sellar problem examples. ( http://openmdao.org/twodocs/versions/latest/basic_guide/sellar.html ) Additionally I have come across a stack overflow question that seems to be the same problem, but the solution there doesn't work. ( OpenMDAO: Solver converging to non-optimal point ) (When I add the declare_partials line to the IntermediateCycle or ScriptForTest I recieve an error saying either that self is not defined, or that the object has no attribute declare_partials)
This is the script that runs everything
import openmdao.api as om
from IntermediateForTest import IntermediateCycle
prob = om.Problem()
prob.model = IntermediateCycle()
prob.driver = om.ScipyOptimizeDriver()
#prob.driver.options['optimizer'] = 'SLSQP'
#prob.driver.options['tol'] = 1e-9
prob.model.add_design_var('n_gear', lower=2, upper=6)
prob.model.add_design_var('stroke', lower=0.0254, upper=1)
prob.model.add_objective('objective')
prob.setup()
prob.model.approx_totals()
prob.run_driver()
print(prob['objective'])
print(prob['cycle.f1.total_weight'])
print(prob['cycle.f1.stroke'])
print(prob['cycle.f1.n_gear'])
It calls an intermediate group, as per the Sellar example
import openmdao.api as om
from FunctionsForTest import FunctionForTest1
from FunctionsForTest import FunctionForTest2
class IntermediateCycle(om.Group):
def setup(self):
indeps = self.add_subsystem('indeps', om.IndepVarComp(), promotes=['*'])
indeps.add_output('n_gear', 3.0)
indeps.add_output('stroke', 0.2)
indeps.add_output('total_weight', 26000.0)
cycle = self.add_subsystem('cycle', om.Group())
cycle.add_subsystem('f1', FunctionForTest1())
cycle.add_subsystem('f2', FunctionForTest2())
cycle.connect('f1.landing_gear_weight','f2.landing_gear_weight')
cycle.connect('f2.total_weight','f1.total_weight')
self.connect('n_gear','cycle.f1.n_gear')
self.connect('stroke','cycle.f1.stroke')
#cycle.nonlinear_solver = om.NonlinearBlockGS()
self.nonlinear_solver = om.NonlinearBlockGS()
self.add_subsystem('objective', om.ExecComp('objective = total_weight', objective=26000, total_weight=26000), promotes=['objective', 'total_weight'])
Finally there is a file with the two functions in it:
import openmdao.api as om
class FunctionForTest1(om.ExplicitComponent):
def setup(self):
self.add_input('stroke', val=0.2)
self.add_input('n_gear', val=3.0)
self.add_input('total_weight', val=26000)
self.add_output('landing_gear_weight')
self.declare_partials('*', '*', method='fd')
def compute(self, inputs, outputs):
stroke = inputs['stroke']
n_gear = inputs['n_gear']
total_weight = inputs['total_weight']
outputs['landing_gear_weight'] = total_weight * 0.1 + 100*stroke * n_gear ** 2
class FunctionForTest2(om.ExplicitComponent):
def setup(self):
self.add_input('landing_gear_weight')
self.add_output('total_weight')
self.declare_partials('*', '*', method='fd')
def compute(self, inputs, outputs):
landing_gear_weight = inputs['landing_gear_weight']
outputs['total_weight'] = 26000 + landing_gear_weight
It reports optimization terminated successfully,
Optimization terminated successfully. (Exit mode 0)
Current function value: 26000.0
Iterations: 1
Function evaluations: 1
Gradient evaluations: 1
Optimization Complete
-----------------------------------
[26000.]
[29088.88888889]
[0.2]
[3.]
however the value for the function to optimize hasn't changed. It seems as it converges the loop to estimate the weight, but doesn't vary the design variables to find the optimum.
It arrives at 29088.9, which is correct for a value of n_gear=3 and stroke=0.2, but if both are decreased to the bounds of n_gear=2 and stroke=0.0254, it would arrive at a value of ~28900, ~188 less.
Any advice, links to tutorials, or solutions would be appreciated.

Answer 1

Lets take a look at the n2 of the model, as you provided it:
I've highlighted the connection from indeps.total_weight to objective.total_weight. So this means that your computed total_weight value is not being passed to your objective output at all. Instead you have a constant value being set there.
Now, taking a small step back, lets look at the computation of the objective itself:
self.add_subsystem('objective', om.ExecComp('objective = total_weight', objective=26000, total_weight=26000), promotes=['objective', 'total_weight'])
So this is an odd use of the ExecComp, because it just sets the output to exactly the input. It does nothing, and isn't really needed at all.
I believe what you wanted was simply to make the objective be the output f2.total_weight. When I do that (and make a few additional small cleanups to your code, like removing the unnecessary ExecComp, then I do get the correct answer in 2 major iterations of the optimizer:
import openmdao.api as om
class FunctionForTest1(om.ExplicitComponent):
def setup(self):
self.add_input('stroke', val=0.2)
self.add_input('n_gear', val=3.0)
self.add_input('total_weight', val=26000)
self.add_output('landing_gear_weight')
self.declare_partials('*', '*', method='fd')
def compute(self, inputs, outputs):
stroke = inputs['stroke']
n_gear = inputs['n_gear']
total_weight = inputs['total_weight']
outputs['landing_gear_weight'] = total_weight * 0.1 + 100*stroke * n_gear ** 2
class FunctionForTest2(om.ExplicitComponent):
def setup(self):
self.add_input('landing_gear_weight')
self.add_output('total_weight')
self.declare_partials('*', '*', method='fd')
def compute(self, inputs, outputs):
landing_gear_weight = inputs['landing_gear_weight']
outputs['total_weight'] = 26000 + landing_gear_weight
class IntermediateCycle(om.Group):
def setup(self):
indeps = self.add_subsystem('indeps', om.IndepVarComp(), promotes=['*'])
indeps.add_output('n_gear', 3.0)
indeps.add_output('stroke', 0.2)
cycle = self.add_subsystem('cycle', om.Group())
cycle.add_subsystem('f1', FunctionForTest1())
cycle.add_subsystem('f2', FunctionForTest2())
cycle.connect('f1.landing_gear_weight','f2.landing_gear_weight')
cycle.connect('f2.total_weight','f1.total_weight')
self.connect('n_gear','cycle.f1.n_gear')
self.connect('stroke','cycle.f1.stroke')
#cycle.nonlinear_solver = om.NonlinearBlockGS()
self.nonlinear_solver = om.NonlinearBlockGS()
prob = om.Problem()
prob.model = IntermediateCycle()
prob.driver = om.ScipyOptimizeDriver()
#prob.driver.options['optimizer'] = 'SLSQP'
#prob.driver.options['tol'] = 1e-9
prob.model.add_design_var('n_gear', lower=2, upper=6)
prob.model.add_design_var('stroke', lower=0.0254, upper=1)
prob.model.add_objective('cycle.f2.total_weight')
prob.model.approx_totals()
prob.setup()
prob.model.nl_solver.options['iprint'] = 2
prob.run_driver()
print(prob['cycle.f1.total_weight'])
print(prob['cycle.f2.total_weight'])
print(prob['cycle.f1.stroke'])
print(prob['cycle.f1.n_gear'])
gives:
Optimization terminated successfully. (Exit mode 0)
Current function value: 28900.177777779667
Iterations: 2
Function evaluations: 2
Gradient evaluations: 2
Optimization Complete
-----------------------------------
[28900.1777778]
[28900.17777778]
[0.0254]
[2.]