An almost similar question was asked but subproblem class was implemented in OpenMDAO to tackle this issue but does not seem to work in my case
I am trying to solve Sellar in CO architecture , starting from subproblem example on 1.7.3 version and sellar classes It runs but it doesn't converge. My guess is that it is coming from initial value of each optimization (always restarting from "cold" start)
If anyone can help, i would be gratful ! here is the code (I guess I could have made it more compact using promotes of variables but I was a bit afraid to get lost for debug :-))
class CO1(Group):
"""
"""
def __init__(self):
super(CO1, self).__init__()
self.add('indep1x', IndepVarComp('x', 0.0))
self.add('indep1z', IndepVarComp('z', np.array([5.0, 2.0])))
self.add('indep1y2', IndepVarComp('y2', 10.0))
self.add('indep1zt', IndepVarComp('zt', np.array([5.0, 2.0])))
self.add('indep1xt', IndepVarComp('xt', 0.0))
self.add('indep1y2t', IndepVarComp('y2t', 10.0))
self.add('indep1y1t', IndepVarComp('y1t', 10.0))
self.add('d1', SellarDis1withDerivatives())
self.connect('indep1z.z', 'd1.z')
self.connect('indep1x.x', 'd1.x')
self.connect('indep1y2.y2', 'd1.y2')
self.add('obj1',ExecComp('obj1 = (zt[0] - z[0])**2 +(zt[1] - z[1])**2 + (xt - x)**2 +(y1t - y1)**2 + (y2t - y2)**2'
, z=np.array([5.0, 2.0]),zt=np.array([0.0, 0.0]), x=0.0, y1=10.0, y2=10.0,xt=0.0, y1t=10.0, y2t=10.0), promotes=['obj1'])
self.connect('d1.z','obj1.z')
self.connect('d1.x','obj1.x')
self.connect('d1.y2','obj1.y2')
self.connect('d1.y1','obj1.y1')
self.connect('indep1zt.zt', "obj1.zt")
self.connect('indep1xt.xt', "obj1.xt")
self.connect('indep1y2t.y2t', "obj1.y2t")
self.connect('indep1y1t.y1t', "obj1.y1t")
self.add('con_cmp1', ExecComp('con1 = 3.16 - y1'), promotes=['con1'])
self.connect('d1.y1', 'con_cmp1.y1')
class CO2(Group):
"""
"""
def __init__(self):
super(CO2, self).__init__()
self.add('indep2z', IndepVarComp('z', np.array([5.0, 2.0])))
self.add('indep2y1', IndepVarComp('y1', 10.0))
self.add('indep2zt', IndepVarComp('zt', np.array([5.0, 2.0])))
self.add('indep2y2t', IndepVarComp('y2t', 10.0))
self.add('indep2y1t', IndepVarComp('y1t', 10.0))
self.add('d2', SellarDis2withDerivatives())
self.connect("indep2z.z", "d2.z")
self.connect("indep2y1.y1", "d2.y1")
self.add('obj2',ExecComp('obj2 = (zt[0] - z[0])**2 +(zt[1] - z[1])**2 + (y1t - y1)**2 +(y2t - y2)**2'
,z=np.array([5.0, 2.0]),zt=np.array([0.0, 0.0]), y1=10.0, y2=10.0, y1t=10.0, y2t=10.0), promotes=['obj2'] )
self.connect('d2.z','obj2.z')
self.connect('d2.y2','obj2.y2')
self.connect('d2.y1','obj2.y1')
self.connect("indep2zt.zt", "obj2.zt")
self.connect("indep2y2t.y2t", "obj2.y2t")
self.connect("indep2y1t.y1t", "obj2.y1t")
self.add('con_cmp2', ExecComp('con2 = y2 - 24.0'), promotes=['con2'])
self.connect('d2.y2', 'con_cmp2.y2')
#######################################
################################################
# First, define a Problem to be able to optimize Discipline 1.
################################################
sub1 = Problem(root=CO1())
# set up our SLSQP optimizer
sub1.driver = ScipyOptimizer()
sub1.driver.options['optimizer'] = 'SLSQP'
#sub1.driver.options['disp'] = False # disable optimizer output
sub1.driver.add_desvar("indep1z.z", lower=np.array([-10.0,0.0]), upper=np.array([10.0,10.0]))
sub1.driver.add_desvar("indep1x.x", lower=0.0, upper=10.0)
sub1.driver.add_desvar("indep1y2.y2", lower=-100.0, upper=100.0)
# We are minimizing comp.fx, so that's our objective.
sub1.driver.add_objective("obj1")
sub1.driver.add_constraint('con1', upper=0.0)
################################################
# Second, define a Problem to be able to optimize Discipline 2.
################################################
sub2 = Problem(root=CO2())
# set up our SLSQP optimizer
sub2.driver = ScipyOptimizer()
sub2.driver.options['optimizer'] = 'SLSQP'
#sub2.driver.options['disp'] = False # disable optimizer output
sub2.driver.add_desvar("indep2z.z", lower=np.array([-10.0,0.0]), upper=np.array([10.0,10.0]))
sub2.driver.add_desvar("indep2y1.y1", lower=-100.0, upper=100.0)
# We are minimizing comp.fx, so that's our objective.
sub2.driver.add_objective("obj2")
sub2.driver.add_constraint('con2', upper=0.0)
################################################
# Thirs, create our top level problem
################################################
prob = Problem(root=Group())
prob.root.add("top_indepxt", IndepVarComp('xt', 0.0))
prob.root.add("top_indepzt", IndepVarComp('zt', np.array([5.0, 2.0])))
prob.root.add("top_indepy1t", IndepVarComp('y1t', 10.0))
prob.root.add("top_indepy2t", IndepVarComp('y2t', 10.0))
prob.root.add("subprob1", SubProblem(sub1, params=['indep1z.z','indep1x.x','indep1y2.y2','indep1xt.xt','indep1zt.zt','indep1y1t.y1t','indep1y2t.y2t'],
unknowns=['obj1','con1' ,'d1.y1']))
prob.root.add("subprob2", SubProblem(sub2, params=['indep2z.z','indep2y1.y1','indep2zt.zt','indep2y1t.y1t','indep2y2t.y2t'],
unknowns=['obj2','con2','d2.y2' ]))
prob.root.connect("top_indepxt.xt", "subprob1.indep1xt.xt")
prob.root.connect("top_indepzt.zt", "subprob1.indep1zt.zt")
prob.root.connect("top_indepy1t.y1t", "subprob1.indep1y1t.y1t")
prob.root.connect("top_indepy2t.y2t", "subprob1.indep1y2t.y2t")
prob.root.connect("top_indepzt.zt", "subprob2.indep2zt.zt")
prob.root.connect("top_indepy1t.y1t", "subprob2.indep2y1t.y1t")
prob.root.connect("top_indepy2t.y2t", "subprob2.indep2y2t.y2t")
prob.driver=ScipyOptimizer()
prob.driver.options['optimizer']='SLSQP'
prob.driver.add_desvar('top_indepzt.zt', lower=np.array([-10.0,0.0]), upper=np.array([10.0,10.0]))
prob.driver.add_desvar('top_indepxt.xt',lower=0.0, upper=10.0)
prob.driver.add_desvar('top_indepy1t.y1t',lower=-100.0, upper=100.0)
prob.driver.add_desvar('top_indepy2t.y2t',lower=-100.0, upper=100.0)
prob.root.add('obj_cmp', ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([5.0, 2.0]), x=0.0),
promotes=['obj'])
prob.root.connect("top_indepzt.zt", "obj_cmp.z")
prob.root.connect("top_indepxt.xt", "obj_cmp.x")
prob.root.connect("top_indepy1t.y1t", "obj_cmp.y1")
prob.root.connect("top_indepy2t.y2t", "obj_cmp.y2")
prob.driver.add_objective('obj')
prob.root.add('con1_cmpt',ExecComp('con1t = (zt[0] - z[0])**2 +'
'(zt[1] - z[1])**2 + '
'(xt - x)**2 + '
'(y1t - y1)**2 + '
'(y2t - y2)**2', z=np.array([5.0, 2.0]),zt=np.array([5.0, 2.0]), x=0.0, y1=10.0, y2=10.0,xt=0.0, y1t=10.0, y2t=10.0), promotes=['con1t'])
prob.root.connect("top_indepzt.zt", "con1_cmpt.zt")
prob.root.connect("top_indepxt.xt", "con1_cmpt.xt")
prob.root.connect("top_indepy1t.y1t", "con1_cmpt.y1t")
prob.root.connect("top_indepy2t.y2t", "con1_cmpt.y2t")
prob.root.connect("subprob1.indep1z.z", "con1_cmpt.z")
prob.root.connect("subprob1.indep1x.x", "con1_cmpt.x")
prob.root.connect("subprob1.d1.y1", "con1_cmpt.y1")
prob.root.connect("subprob1.indep1y2.y2", "con1_cmpt.y2")
prob.driver.add_constraint('con1t', upper=0.0)
prob.root.add('con2_cmpt',ExecComp('con2t = (zt[0] - z[0])**2 +'
'(zt[1] - z[1])**2 + '
'(xt - x)**2 + '
'(y1t - y1)**2 + '
'(y2t - y2)**2', z=np.array([5.0, 2.0]),zt=np.array([5.0, 2.0]), x=0.0, y1=10.0, y2=10.0,xt=0.0, y1t=0.0, y2t=10.0), promotes=['con2t'])
prob.root.connect("top_indepzt.zt", "con2_cmpt.zt")
prob.root.connect("top_indepy1t.y1t", "con2_cmpt.y1t")
prob.root.connect("top_indepy2t.y2t", "con2_cmpt.y2t")
prob.root.connect("subprob2.indep2z.z", "con2_cmpt.z")
prob.root.connect("subprob2.indep2y1.y1", "con2_cmpt.y1")
prob.root.connect("subprob2.d2.y2", "con2_cmpt.y2")
prob.driver.add_constraint('con2t', upper=0.0)
prob.setup()
# run the concurrent optimizations
prob.run()
print("\n")
print("Design var at convergence (%f,%f,%f)"% (prob['top_indepzt.zt'][0],prob['top_indepzt.zt'][1],prob['top_indepxt.xt']))
print("Coupling var at convergence (%f,%f) "% (prob['top_indepy1t.y1t'],prob['top_indepy2t.y2t']))
print("Objective and constraints at (%f, %f,%f)"% (prob['obj'],prob['con1t'],prob['con2t']))
print("Sub1 : Design var at convergence (%f,%f,%f)"% (prob['subprob1.indep1z.z'][0],prob['subprob1.indep1z.z'][1],prob['subprob1.indep1x.x']))
print("Sub2 : Design var at convergence (%f,%f)"% (prob['subprob2.indep2z.z'][0],prob['subprob2.indep2z.z'][1]))
I've implemented a somewhat reasonable solution to this problem in OpenMDAO 2.0. It was a bit tricky to get to work correctly, with the most notable issue being that I couldn't use ScipyOptimizer on both the top level and sub-optimizations because it appears that its not re-entrant.
The other trick, which was in the above question is that you have to create components that have sub-problems in them. This means you have openmdao running within openmdao. Its not the most efficient setup in the world, and there are numerical challenges because you end up finite-differencing around the optimizations. In theory it would be possible to implement post-optimality sensitivities to get more efficient derivatives here.
NOTE: As expected for CO, the convergence properties are terrible. Solving the Sellar problem this way is horribly inefficient. But it shows the rough approach to setting up MDAO architectures in OpenMDAO 2.0
import numpy as np
from openmdao.api import ExplicitComponent, ImplicitComponent, Group, IndepVarComp, ExecComp
class SellarDis1(ExplicitComponent):
def setup(self):
# Global Design Variable
self.add_input('z', val=np.zeros(2))
# Local Design Variable
self.add_input('x', val=0.)
# Coupling parameter
self.add_input('y2', val=1.0)
# Coupling output
self.add_output('y1', val=1.0)
# Finite difference all partials.
self.declare_partials('*', '*')
def compute(self, inputs, outputs):
z1 = inputs['z'][0]
z2 = inputs['z'][1]
x1 = inputs['x']
y2 = inputs['y2']
outputs['y1'] = z1**2 + z2 + x1 - 0.2*y2
def compute_partials(self, inputs, partials):
"""
Jacobian for Sellar discipline 1.
"""
partials['y1', 'y2'] = -0.2
partials['y1', 'z'] = np.array([[2.0 * inputs['z'][0], 1.0]])
partials['y1', 'x'] = 1.0
class SellarDis2(ExplicitComponent):
def setup(self):
# Global Design Variable
self.add_input('z', val=np.zeros(2))
# Coupling parameter
self.add_input('y1', val=1.0)
# Coupling output
self.add_output('y2', val=1.0)
# Finite difference all partials.
self.declare_partials('*', '*')
def compute(self, inputs, outputs):
z1 = inputs['z'][0]
z2 = inputs['z'][1]
y1 = inputs['y1']
# Note: this may cause some issues. However, y1 is constrained to be
# above 3.16, so lets just let it converge, and the optimizer will
# throw it out
if y1.real < 0.0:
y1 *= -1
outputs['y2'] = y1**.5 + z1 + z2
def compute_partials(self, inputs, J):
"""
Jacobian for Sellar discipline 2.
"""
y1 = inputs['y1']
if y1.real < 0.0:
y1 *= -1
J['y2', 'y1'] = .5*y1**-.5
J['y2', 'z'] = np.array([[1.0, 1.0]])
class SubOpt1(ExplicitComponent):
''' minimize differences between target and local variables of the first discipline of the sellar problem '''
def setup(self):
self.add_input('z', val=np.array([5.0, 2.0]))
self.add_input('x_hat', val=1.)
self.add_input('y1_hat', val=1)
self.add_input('y2_hat', val=1)
self.add_output('y1', val=1.0)
self.add_output('z_hat', val=np.ones(2))
self.add_output('x', val=1.0)
# using FD to get derivatives across the sub-optimization
# note: the sub-optimization itself is using analytic derivatives
self.declare_partials('y1', ['z', 'x_hat', 'y1_hat', 'y2_hat'], method='fd', step=1e-4, step_calc='abs')
self.declare_partials('z_hat', ['z', 'x_hat', 'y1_hat', 'y2_hat'], method='fd', step=1e-4, step_calc='abs')
self.declare_partials('x', ['z', 'x_hat', 'y1_hat', 'y2_hat'], method='fd', step=1e-4, step_calc='abs')
self.prob = p = Problem()
# have to define these copies so that OpenMDAO can compute derivs wrt these variables
params = p.model.add_subsystem('params', IndepVarComp(), promotes=['*'])
params.add_output('z', val=np.ones(2))
params.add_output('x_hat', val=1.)
params.add_output('y1_hat', val=1.)
params.add_output('y2_hat', val=1.)
des_vars = p.model.add_subsystem('des_vars', IndepVarComp(), promotes=['*'])
des_vars.add_output('z_hat', val=np.array([5.0, 2.0]))
des_vars.add_output('x', val=1.)
p.model.add_subsystem('d1', SellarDis1())
# using (global-local)**2 ordering
p.model.add_subsystem('J', ExecComp('f = sum((z-z_hat)**2) + (x_hat-x)**2 +(y1_hat-y1)**2', z=np.zeros(2), z_hat=np.zeros(2)))
p.model.add_subsystem('con', ExecComp('c = 3.16 - y1'))
# data connections in the !!!sub-problem!!!
p.model.connect('z', 'J.z')
p.model.connect('x_hat', 'J.x_hat')
p.model.connect('y2_hat', 'd1.y2')
p.model.connect('y1_hat', 'J.y1_hat')
p.model.connect('d1.y1', ['J.y1','con.y1'])
p.model.connect('z_hat', ['J.z_hat', 'd1.z'])
p.model.connect('x', ['J.x','d1.x'])
p.driver = ScipyOptimizer()
p.driver.options['optimizer'] = 'SLSQP'
p.driver.options['maxiter'] = 100
p.driver.options['tol'] = 1e-8
p.model.add_design_var('x', lower=0, upper=10)
p.model.add_design_var('z_hat', lower=-10.0, upper=10)
p.model.add_objective('J.f')
p.model.add_constraint('con.c', upper=0)
p.setup()
p.final_setup()
def compute(self, inputs, outputs):
p = self.prob
# push any global inputs down, using full absolute path names
p['y2_hat'] = inputs['y2_hat']
p['z'] = inputs['z']
p['x_hat'] = inputs['x_hat']
p['y1_hat'] = inputs['y1_hat']
#run the optimization
print('subopt 1 solve')
# print(' ', inputs['z'], inputs['x_hat'], inputs['y1_hat'], inputs['y2_hat'], outputs['y1'], outputs['z_hat'])
p.run_driver()
# pull the values back up into the output array
outputs['y1'] = p['d1.y1']
outputs['z_hat'] = p['z_hat']
outputs['x'] = p['x']
class SubOpt2(ExplicitComponent):
''' minimize differences between target and local variables of the second discipline of the sellar problem '''
def setup(self):
self.add_input('z', val=np.array([5.0, 2.0]))
self.add_input('y1_hat', val=1)
self.add_input('y2_hat', val=1)
self.add_output('y2', val=1.0)
self.add_output('z_hat', val=np.ones(2))
# using FD to get derivatives across the sub-optimization
# note: the sub-optimization itself is using analytic derivatives
self.declare_partials('y2', ['z', 'y1_hat', 'y2_hat'], method='fd', step=1e-4, step_calc='abs')
self.declare_partials('z_hat', ['z', 'y1_hat', 'y2_hat'], method='fd', step=1e-4, step_calc='abs')
self.prob = p = Problem()
# have to define these copies so that OpenMDAO can compute derivs wrt these variables
params = p.model.add_subsystem('params', IndepVarComp(), promotes=['*'])
params.add_output('z', val=np.ones(2))
params.add_output('y1_hat', val=1.)
params.add_output('y2_hat', val=1.)
des_vars = p.model.add_subsystem('des_vars', IndepVarComp(), promotes=['*'])
des_vars.add_output('z_hat', val=np.array([5.0, 2.0]))
p.model.add_subsystem('d2', SellarDis2())
# using (global-local)**2 ordering
p.model.add_subsystem('J', ExecComp('f = sum((z-z_hat)**2) + (y2_hat-y2)**2', z=np.zeros(2), z_hat=np.zeros(2)))
p.model.add_subsystem('con', ExecComp('c = y2 - 24.0'))
# data connections in the !!!sub-problem!!!
p.model.connect('y1_hat', 'd2.y1')
p.model.connect('z', 'J.z')
p.model.connect('y2_hat', 'J.y2_hat')
p.model.connect('d2.y2', ['J.y2','con.y2'])
p.model.connect('z_hat', ['J.z_hat', 'd2.z'])
p.driver = ScipyOptimizer()
p.driver.options['optimizer'] = 'SLSQP'
p.driver.options['maxiter'] = 100
p.driver.options['tol'] = 1e-8
p.model.add_design_var('z_hat', lower=-10.0, upper=10)
p.model.add_objective('J.f')
p.model.add_constraint('con.c', upper=0)
p.setup()
p.final_setup()
def compute(self, inputs, outputs):
p = self.prob
# push any global inputs down, using full absolute path names
p['y1_hat'] = inputs['y1_hat']
p['z'] = inputs['z']
p['y2_hat'] = inputs['y2_hat']
#run the optimization
print('subopt 2 solve')
p.run_driver()
# pull the values back up into the output array
outputs['y2'] = p['d2.y2']
outputs['z_hat'] = p['z_hat']
# print(' ', inputs['z'], inputs['y1_hat'], inputs['y2_hat'], outputs['y2'], outputs['z_hat'])
class SellarCO(Group):
''' optimize top objective function of the sellar problem with the target variables '''
def setup(self):
des_vars = self.add_subsystem('des_vars', IndepVarComp(), promotes=['*'])
des_vars.add_output('z', val=np.array([5.0, 2.0]))
des_vars.add_output('x_hat', val=1)
des_vars.add_output('y1_hat', val=1)
des_vars.add_output('y2_hat', val=2.5)
self.add_subsystem('subopt_1', SubOpt1())
self.add_subsystem('subopt_2', SubOpt2())
self.add_subsystem('J', ExecComp('c = (sum((z-z1_hat)**2) + sum((z-z2_hat)**2) + (x_hat-x) + (y1_hat-y1)**2 + (y2_hat-y2)**2)**.5',
z=np.zeros(2), z1_hat=np.zeros(2), z2_hat=np.zeros(2)))
self.add_subsystem('obj', ExecComp('f = x_hat**2 + z[1] + y1_hat + exp(-y2_hat)', z=np.zeros(2)))
self.connect('z', ['subopt_1.z', 'subopt_2.z', 'obj.z', 'J.z'])
self.connect('x_hat', ['obj.x_hat', 'J.x_hat', 'subopt_1.x_hat'])
self.connect('y1_hat', ['subopt_1.y1_hat', 'subopt_2.y1_hat', 'J.y1_hat', 'obj.y1_hat'])
self.connect('y2_hat', ['subopt_1.y2_hat', 'subopt_2.y2_hat', 'J.y2_hat', 'obj.y2_hat'])
self.connect('subopt_1.z_hat', 'J.z1_hat')
self.connect('subopt_1.y1', 'J.y1')
self.connect('subopt_1.x', 'J.x')
self.connect('subopt_2.z_hat', 'J.z2_hat')
self.connect('subopt_2.y2', 'J.y2')
if __name__ == '__main__':
from openmdao.api import Problem, ScipyOptimizer, pyOptSparseDriver
prob = Problem()
prob.model = SellarCO()
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SNOPT'
prob.driver.opt_settings['Major optimality tolerance'] = 1e-1
prob.driver.opt_settings['Major feasibility tolerance'] = 1e-3
prob.model.add_design_var('z', lower=np.array([-10.0, 0.0]),upper=np.array([10.0, 10.0]))
prob.model.add_design_var('x_hat', lower=0.0, upper=10.0)
prob.model.add_design_var('y1_hat', lower=-10.0, upper=10.0)
prob.model.add_design_var('y2_hat', lower=-10.0, upper=10.0)
prob.model.add_objective('obj.f')
prob.model.add_constraint('J.c', upper=0.005)
prob.setup()
prob.run_driver()
print("\n")
print( "Minimum target found at (%f, %f, %f)" % (prob['z'][0], prob['z'][1], prob['x_hat']))
print("Coupling vars target: %f, %f" % (prob['y1_hat'], prob['y2_hat']))
print("Minimum objective: ", prob['obj.f'])
# print("constraints: ", prob['con1'] , prob['con2'])
来源：https://stackoverflow.com/questions/42611927/openmdao-co-collaborative-optimization-on-sellar-test-case