toy_problem_dist_constraint.py

import openmdao.api as om
import numpy as np

from openmdao.utils.array_utils import evenly_distrib_idxs

N = 3

class DistribComp(om.ExplicitComponent):

    def initialize(self):
        self.options['distributed'] = True

    def setup(self):
        self.add_input('x', shape=1)
        rank = self.comm.rank
        sizes, offsets = evenly_distrib_idxs(self.comm.size, N)
        self.add_output('y', shape=sizes[rank])
        self.declare_partials('y', 'x')


    def compute(self, inputs, outputs):
        rank = self.comm.rank
        sizes, offsets = evenly_distrib_idxs(self.comm.size, N)
        outputs['y'] = inputs['x']*np.ones((sizes[rank],))

    def compute_partials(self, inputs, J):
        rank = self.comm.rank
        sizes, offsets = evenly_distrib_idxs(self.comm.size, N)
        J['y','x'] = 1.0

class GroupThatDoesNotWork(om.Group):
    def setup(self):
        dvs = om.IndepVarComp()
        dvs.add_output('x', val=6.0)
        self.add_subsystem('dvs', dvs, promotes_outputs=['*'])
        self.add_subsystem('distcomp',DistribComp(), promotes_inputs=['*'])
        self.add_design_var('x', lower=0.0, upper=10.0, scaler=1.0)
        self.add_constraint('distcomp.y', lower=4.2, scaler=1.0)
        self.add_objective('x')

class GroupThatDoesWork(om.Group):
    def setup(self):
        dvs = om.IndepVarComp()
        dvs.add_output('x', val=6.0)
        self.add_subsystem('dvs', dvs, promotes_outputs=['*'])
        self.add_subsystem('distcomp',DistribComp(), promotes_inputs=['*'])
        # simply adding an execcomp passthru fixes the problem
        self.add_subsystem('execcomp',om.ExecComp('z = y', y=np.zeros((N,)), z=np.zeros((N,))))
        self.connect('distcomp.y', 'execcomp.y')
        self.add_design_var('x', lower=0.0, upper=10.0, scaler=1.0)
        self.add_constraint('execcomp.z', lower=4.2, scaler=1.0)
        self.add_objective('x')

if __name__ == "__main__":

    prob = om.Problem()
    prob.model = GroupThatDoesNotWork()
    # prob.model = GroupThatDoesWork()
    prob.driver = om.pyOptSparseDriver()
    prob.driver.options['optimizer'] = "SLSQP"
    # prob.driver.opt_settings ={
    #     'Major feasibility tolerance': 1e-10, #1e-4,
    #     'Major optimality tolerance': 1e-10, #1e-8,
    #     # 'Verify level': 0,
    #     'Major iterations limit':200,
    #     'Minor iterations limit':1000000,
    #     'Iterations limit':1500000,
    #     'Nonderivative linesearch':None,
    #     'Major step limit': 0.01,
    #     # 'Function precision':1.0e-8,
    #     # 'Difference interval':1.0e-6,
    #     # 'Hessian full memory':None,
    #     'Hessian frequency' : 200,
    #     #'Linesearch tolerance':0.99,
    #     'Print file':'SNOPT_print.out',
    #     'Summary file': 'SNOPT_summary.out',
    #     'Problem Type':'Minimize',
    #     #'New superbasics limit':500,
    #     'Penalty parameter':1.0}
    prob.setup(mode='fwd')
    prob['dvs.x'] = 7.0
    prob.run_model()
    # print(prob['distcomp.y'])
    # print(prob['execcomp.z'])

    # prob.check_partials(compact_print=True)
    # prob.check_totals(compact_print=True)
    prob.run_driver()