I am trying to implement Collaborative Optimization & other multi-level architectures on OpenMDAO. I read here that this can be done by defining a separate solve_nonlinear method in the Subclass of Problem.
The issue is that while running the problem instance the defined solve_linear is not being called. Here is the code -
from __future__ import print_function, division
import numpy as np
import time
from openmdao.api import Component,Group, IndepVarComp, ExecComp,\
Problem, ScipyOptimizer, NLGaussSeidel, ScipyGMRES
class SellarDis1(Component):
"""Component containing Discipline 1."""
def __init__(self):
super(SellarDis1, self).__init__()
self.add_param('z', val=np.zeros(2))
self.add_param('x', val=0.0)
self.add_param('y2', val=1.0)
self.add_output('y1', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
y1 = z1**2 + z2 + x1 - 0.2*y2"""
z1 = params['z'][0]
z2 = params['z'][1]
x1 = params['x']
y2 = params['y2']
unknowns['y1'] = z1**2 + z2 + x1 - 0.2*y2
def linearize(self, params, unknowns, resids):
J = {}
J['y1','y2'] = -0.2
J['y1','z'] = np.array([[2*params['z'][0], 1.0]])
J['y1','x'] = 1.0
return J
class SellarDis2(Component):
def __init__(self):
super(SellarDis2, self).__init__()
self.add_param('z', val=np.zeros(2))
self.add_param('y1', val=1.0)
self.add_output('y2', val=1.0)
def solve_nonlinear(self, params, unknowns, resids):
z1 = params['z'][0]
z2 = params['z'][1]
y1 = params['y1']
y1 = abs(y1)
unknowns['y2'] = y1**.5 + z1 + z2
def linearize(self, params, unknowns, resids):
J = {}
J['y2', 'y1'] = 0.5*params['y1']**-0.5
J['y2', 'z'] = np.array([[1.0, 1.0]])
return J
class Sellar(Group):
def __init__(self):
super(Sellar, self).__init__()
self.add('px', IndepVarComp('x', 1.0), promotes=['*'])
self.add('pz', IndepVarComp('z', np.array([5.0,2.0])), promotes=['*'])
self.add('d1', SellarDis1(), promotes=['*'])
self.add('d2', SellarDis2(), promotes=['*'])
self.add('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]), x=0.0, y1=0.0, y2=0.0),
promotes=['*'])
self.add('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['*'])
self.add('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['*'])
self.nl_solver = NLGaussSeidel()
self.nl_solver.options['atol'] = 1.0e-12
self.ln_solver = ScipyGMRES()
def solve_nonlinear(self, params=None, unknowns=None, resids=None, metadata=None):
print("Group's solve_nonlinear was called!!")
# Discipline Optimizer would be called here?
super(Sellar, self).solve_nonlinear(params, unknowns, resids)
class ModifiedProblem(Problem):
def solve_nonlinear(self, params, unknowns, resids):
print("Problem's solve_nonlinear was called!!")
# or here ?
super(ModifiedProblem, self).solve_nonlinear()
top = ModifiedProblem()
top.root = Sellar()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
top.driver.add_desvar('x', lower=0., upper=10.0)
top.driver.add_objective('obj')
top.driver.add_constraint('con1', upper=0.0)
top.driver.add_constraint('con2', upper=0.0)
top.setup(check=False)
top.run()
The output of above code is -
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Group's solve_nonlinear was called!!
Optimization terminated successfully. (Exit mode 0)
Current function value: [ 3.18339395]
Iterations: 6
Function evaluations: 6
Gradient evaluations: 6
Optimization Complete
-----------------------------------
which means that the solve_nonlinear defined in subclass of Problem isn't called at any time. So, should I call the discipline optimizers in Group's Subclass?
Also, how do I pass the target variables between the two optimization problems (System & Disciplines), specially returning the optimized global variables from individual disciplines back to the system optimizer.
Thanks to all.
You are right that solve_nonlinear on Problem is never called, because Problem is not an OpenMDAO component and doesn't have a solve_nonlinear method. What you want to do in order to run a submodel problem inside another problem is to encapsulate it in a Component instance. It would look something like this:
class SubOptimization(Component)
def __init__(self):
super(SubOptimization, self).__init__()
# Inputs to this subprob
self.add_param('z', val=np.zeros(2))
self.add_param('x', val=0.0)
self.add_param('y2', val=1.0)
# Unknowns for this sub prob
self.add_output('y1', val=1.0)
self.problem = prob = Problem()
prob.root = Group()
prob.add('px', IndepVarComp('x', 1.0), promotes=['*'])
prob.add('d1', SellarDis1(), promotes=['*'])
# TODO - add cons/objs for sub prob
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('x', lower=0., upper=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.setup()
# Must finite difference across optimizer
self.fd_options['force_fd'] = True
def solve_nonlinear(self, params, unknowns, resids):
prob = self.problem
# Pass values into our problem
prob['x'] = params['x']
prob['z'] = params['z']
prob['y2'] = params['y2']
# Run problem
prob.run()
# Pull values from problem
unknowns['y1'] = prob['y1']
You can place this component into your main Problem (along with one for discipline 2, though 2 doesn't really need a sub-optimization since it has no local design variabes) and optimize the global design variable around it.
One caveat: this isn't something I have tried (nor have I tested the incomplete code snippet above), but it should get you on the right track. It's possible you may encounter a bug since this isn't really tested much. When I get some time, I will put together a CO test like this for the OpenMDAO tests so that we are safe.
来源：https://stackoverflow.com/questions/35287786/how-to-use-nested-problems-in-openmdao-1-x