def test_simple_paraboloid_equality(self):
prob = Problem()
root = prob.root = Group()
root.add("p1", ParamComp("x", 50.0), promotes=["*"])
root.add("p2", ParamComp("y", 50.0), promotes=["*"])
root.add("comp", Paraboloid(), promotes=["*"])
root.add("con", ExecComp("c = 15.0 - x + y"), promotes=["*"])
prob.driver = ScipyOptimizer()
prob.driver.options["optimizer"] = "SLSQP"
prob.driver.options["tol"] = 1.0e-8
prob.driver.add_param("x", low=-50.0, high=50.0)
prob.driver.add_param("y", low=-50.0, high=50.0)
prob.driver.add_objective("f_xy")
prob.driver.add_constraint("c", ctype="ineq")
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob["x"], 7.16667, 1e-6)
assert_rel_error(self, prob["y"], -7.833334, 1e-6)
def test_simple_paraboloid_constrained_COBYLA_upper(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = y - x'), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'COBYLA'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_desvar('x', low=-50.0, high=50.0)
prob.driver.add_desvar('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_index_array_param(self):
prob = Problem()
prob.root = SellarStateConnection()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_desvar('z', low=np.array([-10.0]), high=np.array([10.0]),
indices=[0])
prob.driver.add_desvar('x', low=0.0, high=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.driver.options['disp'] = False
prob.setup(check=False)
prob['z'][1] = 5.0
prob.run()
assert_rel_error(self, prob['z'][0], 0.1005, 1e-3)
assert_rel_error(self, prob['z'][1], 5.0, 1e-3)
assert_rel_error(self, prob['x'], 0.0, 1e-3)
def test_index_error_messages_con(self):
prob = Problem()
prob.root = Group()
prob.root.fd_options['force_fd'] = True
prob.root.ln_solver.options['mode'] = 'auto'
prob.root.add('myparams', IndepVarComp('x', np.zeros(4)))
prob.root.add('rosen', Rosenbrock(4))
prob.root.connect('myparams.x', 'rosen.x')
prob.driver = MySimpleDriver()
prob.driver.add_desvar('myparams.x')
prob.driver.add_constraint('rosen.xxx', upper=0.0, indices=[4])
prob.setup(check=False)
# Make sure we can't do this
with self.assertRaises(IndexError) as cm:
prob.run()
msg = "Index for constraint 'rosen.xxx' is out of bounds. "
msg += "Requested index: [4], "
msg += "shape: (4,)."
raised_error = str(cm.exception)
raised_error = raised_error.replace('(4L,', '(4,')
self.assertEqual(msg, raised_error)
def test_index_error_messages_con(self):
prob = Problem()
prob.root = Group()
prob.root.fd_options['force_fd'] = True
prob.root.ln_solver.options['mode'] = 'auto'
prob.root.add('myparams', ParamComp('x', np.zeros(4)))
prob.root.add('rosen', Rosenbrock(4))
prob.root.connect('myparams.x', 'rosen.x')
prob.driver = MySimpleDriver()
prob.driver.add_param('myparams.x')
prob.driver.add_constraint('rosen.xxx', indices=[4])
prob.setup(check=False)
# Make sure we can't do this
with self.assertRaises(IndexError) as cm:
prob.run()
msg = "Index for constraint 'rosen.xxx' is out of bounds. "
msg += "Requested index: [4], "
msg += "Parameter shape: (4,)."
raised_error = str(cm.exception)
raised_error = raised_error.replace('(4L,', '(4,')
self.assertEqual(msg, raised_error)
def test_eq_ineq_error_messages(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
# Don't try this at home, kids
prob.driver.supports['equality_constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', ctype='eq')
self.assertEqual(
str(cm.exception),
"Driver does not support equality constraint 'con1'.")
# Don't try this at home, kids
prob.driver.supports['inequality_constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', ctype='ineq')
self.assertEqual(
str(cm.exception),
"Driver does not support inequality constraint 'con1'.")
def run(typ="adjoint", m=300):
t = time.time()
model = Problem(impl=impl)
model.root = MPPT_MDP(m)
# add optimizer
model.driver = pyOptSparseDriver()
model.driver.options['optimizer'] = "SNOPT"
model.driver.options['print_results'] = False
model.driver.opt_settings = {
'Major optimality tolerance': 1e-3,
'Major feasibility tolerance': 1.0e-5,
'Iterations limit': 500000000,
"Verify level" : -1
}
model.driver.add_objective("perf.result")
model.driver.add_desvar("pt0.param.CP_Isetpt", lower=0., upper=0.4)
#model.driver.add_desvar("pt1.param.CP_Isetpt", lower=0., upper=0.4)
if typ == "fd":
model.root.fd_options['force_fd'] = True
elif typ == "fwd":
model.root.ln_solver.options['mode'] = "fwd"
else:
model.root.ln_solver.options['mode'] = "rev"
model.setup(check=False)
model.run()
#os.rename("SNOPT_summary.out", "SNOPT_summary_%s_%d.out" % (typ, m))
return time.time() - t
def test_simple_paraboloid_equality(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = 15.0 - x + y'), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', ctype='ineq')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_driver_param_indices_snopt(self):
""" Test driver param indices with pyOptSparse and force_fd=False
"""
prob = Problem()
prob.root = SellarStateConnection()
prob.root.fd_options['force_fd'] = False
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('z', low=np.array([-10.0]),
high=np.array([10.0]),indices=[0])
prob.driver.add_desvar('x', low=0.0, high=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(check=False)
prob['z'][1] = 0.0
prob.run()
assert_rel_error(self, prob['z'][0], 1.9776, 1e-3)
assert_rel_error(self, prob['z'][1], 0.0, 1e-3)
assert_rel_error(self, prob['x'], 0.0, 1e-3)
def test_Sellar_state_SLSQP(self):
""" Baseline Sellar test case without specifying indices.
"""
prob = Problem()
prob.root = SellarStateConnection()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_desvar('z', low=np.array([-10.0, 0.0]),
high=np.array([10.0, 10.0]))
prob.driver.add_desvar('x', low=0.0, high=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob['z'][0], 1.9776, 1e-3)
assert_rel_error(self, prob['z'][1], 0.0, 1e-3)
assert_rel_error(self, prob['x'], 0.0, 1e-3)
def test_simple_array_comp(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['*'])
root.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', ctype='eq')
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_paraboloid_equality(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = 15.0 - x + y'), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', ctype='ineq')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_driver_param_indices_slsqp_force_fd(self):
""" Test driver param indices with ScipyOptimizer SLSQP and force_fd=True
"""
prob = Problem()
prob.root = SellarStateConnection()
prob.root.fd_options['force_fd'] = True
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_desvar('z', low=np.array([-10.0]),
high=np.array([10.0]),indices=[0])
prob.driver.add_desvar('x', low=0.0, high=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
#prob.driver.options['disp'] = False
prob.setup(check=False)
prob['z'][1] = 0.0
prob.run()
assert_rel_error(self, prob['z'][0], 1.9776, 1e-3)
assert_rel_error(self, prob['z'][1], 0.0, 1e-3)
assert_rel_error(self, prob['x'], 0.0, 1e-3)
def test_simple_array_comp2D(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', np.zeros((2, 2))), promotes=['*'])
root.add('comp', ArrayComp2D(), promotes=['*'])
root.add('con',
ExecComp('c = y - 20.0',
c=np.zeros((2, 2)),
y=np.zeros((2, 2))),
promotes=['*'])
root.add('obj',
ExecComp('o = y[0, 0]', y=np.zeros((2, 2))),
promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', ctype='eq')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_rosenbrock(self):
prob = Problem()
root = prob.root = Group()
root.add("rosen", Rosenbrock(), promotes=["x", "obj"])
root.add("desvars", IndepVarComp("x", np.ones(2) * -1.0), promotes=["x"])
prob.driver = KonaOptimizer()
prob.driver.options["algorithm"] = ReducedSpaceQuasiNewton
# prob.driver.options['algorithm'] = Verifier
optns = {
"verify": {
"primal_vec": True,
"state_vec": True,
"dual_vec": True,
"gradients": True,
"pde_jac": True,
"cnstr_jac": True,
"red_grad": True,
"lin_solve": True,
"out_file": sys.stdout, # 'kona_verify.dat',
}
}
prob.driver.options["algorithm_options"] = optns
prob.driver.add_desvar("x")
prob.driver.add_objective("obj")
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob["x"], np.ones(2), 1e-5)
def test_Sellar_state_SLSQP(self):
prob = Problem()
prob.root = SellarStateConnection()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_param('z',
low=np.array([-10.0, 0.0]),
high=np.array([10.0, 10.0]))
prob.driver.add_param('x', low=0.0, high=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1')
prob.driver.add_constraint('con2')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob['z'][0], 1.9776, 1e-3)
assert_rel_error(self, prob['z'][1], 0.0, 1e-3)
assert_rel_error(self, prob['x'], 0.0, 1e-3)
def test_record_derivs_dicts(self):
if OPT is None:
raise unittest.SkipTest("pyoptsparse is not installed")
if OPTIMIZER is None:
raise unittest.SkipTest(
"pyoptsparse is not providing SNOPT or SLSQP")
prob = Problem()
prob.root = SellarDerivativesGrouped()
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('z',
lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
prob.driver.add_desvar('x', lower=0.0, 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.driver.add_recorder(self.recorder)
self.recorder.options['record_metadata'] = False
self.recorder.options['record_derivs'] = True
prob.setup(check=False)
prob.run()
prob.cleanup()
hdf = h5py.File(self.filename, 'r')
deriv_group = hdf['rank0:SLSQP|1']['Derivs']
self.assertEqual(deriv_group.attrs['success'], 1)
self.assertEqual(deriv_group.attrs['msg'], '')
J1 = deriv_group['Derivatives']
Jbase = {}
Jbase['con1'] = {}
Jbase['con1']['x'] = -0.98061433
Jbase['con1']['z'] = np.array([-9.61002285, -0.78449158])
Jbase['con2'] = {}
Jbase['con2']['x'] = 0.09692762
Jbase['con2']['z'] = np.array([1.94989079, 1.0775421])
Jbase['obj'] = {}
Jbase['obj']['x'] = 2.98061392
Jbase['obj']['z'] = np.array([9.61001155, 1.78448534])
for key1, val1 in Jbase.items():
for key2, val2 in val1.items():
assert_rel_error(self, J1[key1][key2][:], val2, .00001)
hdf.close()
def test_record_derivs_dicts(self):
if OPT is None:
raise unittest.SkipTest("pyoptsparse is not installed")
if OPTIMIZER is None:
raise unittest.SkipTest("pyoptsparse is not providing SNOPT or SLSQP")
prob = Problem()
prob.root = SellarDerivativesGrouped()
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
prob.driver.add_desvar('x', lower=0.0, 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.driver.add_recorder(self.recorder)
self.recorder.options['record_metadata'] = False
self.recorder.options['record_derivs'] = True
prob.setup(check=False)
prob.run()
prob.cleanup()
hdf = h5py.File(self.filename, 'r')
deriv_group = hdf['rank0:SLSQP|1']['Derivs']
self.assertEqual(deriv_group.attrs['success'],1)
self.assertEqual(deriv_group.attrs['msg'],'')
J1 = deriv_group['Derivatives']
Jbase = {}
Jbase['con1'] = {}
Jbase['con1']['x'] = -0.98061433
Jbase['con1']['z'] = np.array([-9.61002285, -0.78449158])
Jbase['con2'] = {}
Jbase['con2']['x'] = 0.09692762
Jbase['con2']['z'] = np.array([1.94989079, 1.0775421 ])
Jbase['obj'] = {}
Jbase['obj']['x'] = 2.98061392
Jbase['obj']['z'] = np.array([9.61001155, 1.78448534])
for key1, val1 in Jbase.items():
for key2, val2 in val1.items():
assert_rel_error(self, J1[key1][key2][:], val2, .00001)
hdf.close()
def test_simple_driver_recording(self, m):
self.setup_endpoints(m)
recorder = WebRecorder(self._accepted_token, suppress_output=True)
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
model.add_subsystem('comp', Paraboloid(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
model.suppress_solver_output = True
prob.driver = pyOptSparseDriver()
prob.driver.add_recorder(recorder)
prob.driver.recording_options['record_desvars'] = True
prob.driver.recording_options['record_responses'] = True
prob.driver.recording_options['record_objectives'] = True
prob.driver.recording_options['record_constraints'] = True
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
model.add_design_var('x', lower=-50.0, upper=50.0)
model.add_design_var('y', lower=-50.0, upper=50.0)
model.add_objective('f_xy')
model.add_constraint('c', upper=-15.0)
prob.setup(check=False)
t0, t1 = run_driver(prob)
prob.cleanup()
driver_iteration_data = json.loads(self.driver_iteration_data)
expected_desvars = [
{'name': 'p1.x', 'values': [7.1666666]},
{'name': 'p2.y', 'values': [-7.8333333]}
]
expected_objectives = [
{'name': 'comp.f_xy', 'values': [-27.083333]}
]
expected_constraints = [
{'name': 'con.c', 'values': [-15.0]}
]
for d in expected_desvars:
self.assert_array_close(d, driver_iteration_data['desvars'])
for o in expected_objectives:
self.assert_array_close(o, driver_iteration_data['objectives'])
for c in expected_constraints:
self.assert_array_close(c, driver_iteration_data['constraints'])
def test_exponential_constrained_ieq(self):
prob = Problem()
root = prob.root = Group()
root.add("sc", ExponentialConstrained(), promotes=["x", "f", "h"])
root.add("desvars", IndepVarComp("x", np.array([1.0, 1.0])), promotes=["x"])
prob.driver = KonaOptimizer()
prob.driver.add_desvar("x")
# TODO report bug if this variable is promoted
prob.driver.add_objective("f")
prob.driver.add_constraint("h", lower=0)
prob.driver.options["algorithm"] = ConstrainedRSNK
# prob.driver.options['algorithm'] = Verifier
optns = {
"info_file": "kona_info.dat",
"max_iter": 30,
"primal_tol": 1e-5,
"constraint_tol": 1e-5,
"trust": {"init_radius": 1.0, "max_radius": 10.0, "min_radius": 1e-4},
"aug_lag": {"mu_init": 1.0, "mu_pow": 0.5, "mu_max": 1e5},
"reduced": {
"precond": None,
"product_fac": 0.001,
"lambda": 0.0,
"scale": 0.0,
"nu": 0.95,
"dynamic_tol": False,
},
"krylov": {"out_file": "kona_krylov.dat", "max_iter": 10, "rel_tol": 0.0095, "check_res": True},
"verify": {
"primal_vec": True,
"state_vec": True,
"dual_vec": True,
"gradients": True,
"pde_jac": True,
"cnstr_jac": True,
"red_grad": True,
"lin_solve": True,
"out_file": sys.stdout, # 'kona_verify.dat',
},
}
prob.driver.options["algorithm_options"] = optns
prob.setup(check=False)
prob.run()
# print(prob['x'])
assert_rel_error(self, prob["x"], np.zeros(2), 1e-5)
def test_record_derivs_lists(self):
prob = Problem()
prob.root = SellarDerivativesGrouped()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.options['disp'] = False
prob.driver.add_desvar('z',
lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
prob.driver.add_desvar('x', lower=0.0, 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.driver.add_recorder(self.recorder)
self.recorder.options['record_metadata'] = False
self.recorder.options['record_derivs'] = True
prob.setup(check=False)
prob.run()
prob.cleanup()
hdf = h5py.File(self.filename, 'r')
deriv_group = hdf['rank0:SLSQP|1']['Derivs']
self.assertEqual(deriv_group.attrs['success'], 1)
self.assertEqual(deriv_group.attrs['msg'], '')
J1 = deriv_group['Derivatives']
assert_rel_error(self, J1[0][0], 9.61001155, .00001)
assert_rel_error(self, J1[0][1], 1.78448534, .00001)
assert_rel_error(self, J1[0][2], 2.98061392, .00001)
assert_rel_error(self, J1[1][0], -9.61002285, .00001)
assert_rel_error(self, J1[1][1], -0.78449158, .00001)
assert_rel_error(self, J1[1][2], -0.98061433, .00001)
assert_rel_error(self, J1[2][0], 1.94989079, .00001)
assert_rel_error(self, J1[2][1], 1.0775421, .00001)
assert_rel_error(self, J1[2][2], 0.09692762, .00001)
hdf.close()
def test_record_derivs_lists(self):
prob = Problem()
prob.root = SellarDerivativesGrouped()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.options['disp'] = False
prob.driver.add_desvar('z', lower=np.array([-10.0, 0.0]),
upper=np.array([10.0, 10.0]))
prob.driver.add_desvar('x', lower=0.0, 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.driver.add_recorder(self.recorder)
self.recorder.options['record_metadata'] = False
self.recorder.options['record_derivs'] = True
prob.setup(check=False)
prob.run()
prob.cleanup()
hdf = h5py.File(self.filename, 'r')
deriv_group = hdf['rank0:SLSQP|1']['Derivs']
self.assertEqual(deriv_group.attrs['success'],1)
self.assertEqual(deriv_group.attrs['msg'],'')
J1 = deriv_group['Derivatives']
assert_rel_error(self, J1[0][0], 9.61001155, .00001)
assert_rel_error(self, J1[0][1], 1.78448534, .00001)
assert_rel_error(self, J1[0][2], 2.98061392, .00001)
assert_rel_error(self, J1[1][0], -9.61002285, .00001)
assert_rel_error(self, J1[1][1], -0.78449158, .00001)
assert_rel_error(self, J1[1][2], -0.98061433, .00001)
assert_rel_error(self, J1[2][0], 1.94989079, .00001)
assert_rel_error(self, J1[2][1], 1.0775421, .00001)
assert_rel_error(self, J1[2][2], 0.09692762, .00001)
hdf.close()
def test_mydriver(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
prob.driver.add_param('z', low=-100.0, high=100.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1')
prob.driver.add_constraint('con2')
prob.setup(check=False)
prob.run()
obj = prob['obj']
self.assertLess(obj, 28.0)
def test_mydriver(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
prob.driver.add_desvar('z', low=-100.0, high=100.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.setup(check=False)
prob.run()
obj = prob['obj']
self.assertLess(obj, 28.0)
def test_mydriver(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
prob.driver.add_param("z", low=-100.0, high=100.0)
prob.driver.add_objective("obj")
prob.driver.add_constraint("con1")
prob.driver.add_constraint("con2")
prob.setup(check=False)
prob.run()
obj = prob["obj"]
self.assertLess(obj, 28.0)
def test_generate_numpydocstring(self):
prob = Problem()
prob.root = SellarStateConnection()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.add_desvar('z', low=np.array([-10.0]), high=np.array([10.0]),
indices=[0])
prob.driver.add_desvar('x', low=0.0, high=10.0)
prob.driver.add_objective('obj')
prob.driver.add_constraint('con1', upper=0.0)
prob.driver.add_constraint('con2', upper=0.0)
prob.driver.options['disp'] = False
test_string = prob.driver.generate_docstring()
original_string = ' """\n\n Options\n -------\n options[\'disp\'] : bool(False)\n Set to False to prevent printing of Scipy convergence messages\n options[\'maxiter\'] : int(200)\n Maximum number of iterations.\n options[\'optimizer\'] : str(\'SLSQP\')\n Name of optimizer to use\n options[\'tol\'] : float(1e-08)\n Tolerance for termination. For detailed control, use solver-specific options.\n\n """\n'
self.assertEqual(original_string, test_string)
def test_fan_out(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 1.0))
root.add('p2', ParamComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up non explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.add_param('p1.x', low=-50.0, high=50.0)
prob.driver.add_param('p2.x', low=-50.0, high=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', ctype='eq')
prob.driver.add_constraint('con2.c', ctype='eq')
prob.setup(check=False)
prob.run()
obj = prob['obj.o']
assert_rel_error(self, obj, 30.0, 1e-6)
# Verify that pyOpt has the correct wrt names
con1 = prob.driver.pyopt_solution.constraints['con1.c']
self.assertEqual(con1.wrt, ['p1.x'])
con2 = prob.driver.pyopt_solution.constraints['con2.c']
self.assertEqual(con2.wrt, ['p2.x'])
def test_fan_out(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 1.0))
root.add('p2', ParamComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up non explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = pyOptSparseDriver()
prob.driver.add_param('p1.x', low=-50.0, high=50.0)
prob.driver.add_param('p2.x', low=-50.0, high=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', ctype='eq')
prob.driver.add_constraint('con2.c', ctype='eq')
prob.setup(check=False)
prob.run()
obj = prob['obj.o']
assert_rel_error(self, obj, 30.0, 1e-6)
# Verify that pyOpt has the correct wrt names
con1 = prob.driver.pyopt_solution.constraints['con1.c']
self.assertEqual(con1.wrt, ['p1.x'])
con2 = prob.driver.pyopt_solution.constraints['con2.c']
self.assertEqual(con2.wrt, ['p2.x'])
def test_simple_array_comp2D_array_lo_hi(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', np.zeros((2, 2))), promotes=['*'])
root.add('comp', ArrayComp2D(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.zeros((2, 2)), y=np.zeros((2, 2))), promotes=['*'])
root.add('obj', ExecComp('o = y[0, 0]', y=np.zeros((2, 2))), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('x', low=-50.0*np.ones((2, 2)), high=50.0*np.ones((2, 2)))
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_array_comp(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', np.zeros([2])), promotes=['*'])
root.add('comp', SimpleArrayComp(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.array([0.0, 0.0]), y=np.array([0.0, 0.0])), promotes=['*'])
root.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', ctype='eq')
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_eq_ineq_error_messages(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1')
self.assertEqual(str(cm.exception), "Constraint 'con1' needs to define lower, upper, or equals.")
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', lower=0.0, upper=1.1, equals=2.2)
self.assertEqual(str(cm.exception), "Constraint 'con1' cannot be both equality and inequality.")
# Don't try this at home, kids
prob.driver.supports['two_sided_constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', lower=0.0, upper=1.1)
self.assertEqual(str(cm.exception), "Driver does not support 2-sided constraint 'con1'.")
# Don't try this at home, kids
prob.driver.supports['equality_constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', equals=0.0)
self.assertEqual(str(cm.exception), "Driver does not support equality constraint 'con1'.")
# Don't try this at home, kids
prob.driver.supports['inequality_constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', upper=0.0)
self.assertEqual(str(cm.exception), "Driver does not support inequality constraint 'con1'.")
def test_eq_ineq_error_messages(self):
prob = Problem()
root = prob.root = SellarDerivatives()
prob.driver = MySimpleDriver()
# Don't try this at home, kids
prob.driver.supports['Equality Constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', ctype='eq')
self.assertEqual(str(cm.exception), "Driver does not support equality constraint 'con1'.")
# Don't try this at home, kids
prob.driver.supports['Inequality Constraints'] = False
with self.assertRaises(RuntimeError) as cm:
prob.driver.add_constraint('con1', ctype='ineq')
self.assertEqual(str(cm.exception), "Driver does not support inequality constraint 'con1'.")
def test_Sellar_state_SLSQP(self):
prob = Problem()
prob.root = SellarStateConnection()
prob.driver = ScipyOptimizer()
prob.driver.options["optimizer"] = "SLSQP"
prob.driver.options["tol"] = 1.0e-8
prob.driver.add_param("z", low=np.array([-10.0, 0.0]), high=np.array([10.0, 10.0]))
prob.driver.add_param("x", low=0.0, high=10.0)
prob.driver.add_objective("obj")
prob.driver.add_constraint("con1")
prob.driver.add_constraint("con2")
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob["z"][0], 1.9776, 1e-3)
assert_rel_error(self, prob["z"][1], 0.0, 1e-3)
assert_rel_error(self, prob["x"], 0.0, 1e-3)
def benchmark_mppt(self):
model = Problem(impl=impl)
model.root = MPPT_MDP()
# add optimizer
model.driver = pyOptSparseDriver()
model.driver.options['optimizer'] = "SNOPT"
model.driver.opt_settings = {
'Major optimality tolerance': 1e-3,
'Major feasibility tolerance': 1.0e-5,
'Iterations limit': 500000000,
'New basis file': 10
}
model.driver.add_objective("perf.result")
model.driver.add_desvar("pt0.param.CP_Isetpt", lower=0., upper=0.4)
model.driver.add_desvar("pt1.param.CP_Isetpt", lower=0., upper=0.4)
model.setup(check=False)
model.run()
assert_rel_error(self, model["perf.result"], -9.4308562238E+03, 1e-6)
def test_simple_paraboloid_double_sided_high(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 50.0), promotes=['*'])
root.add('p2', IndepVarComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = x - y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('x', low=-50.0, high=50.0)
prob.driver.add_desvar('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_simple_array_comp2D(self):
prob = Problem()
root = prob.root = Group()
root.add("p1", ParamComp("x", np.zeros((2, 2))), promotes=["*"])
root.add("comp", ArrayComp2D(), promotes=["*"])
root.add("con", ExecComp("c = y - 20.0", c=np.zeros((2, 2)), y=np.zeros((2, 2))), promotes=["*"])
root.add("obj", ExecComp("o = y[0, 0]", y=np.zeros((2, 2))), promotes=["*"])
prob.driver = ScipyOptimizer()
prob.driver.options["optimizer"] = "SLSQP"
prob.driver.add_param("x", low=-50.0, high=50.0)
prob.driver.add_objective("o")
prob.driver.add_constraint("c", ctype="eq")
prob.setup(check=False)
prob.run()
obj = prob["o"]
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_paraboloid_unconstrained_SLSQP(self):
prob = Problem()
root = prob.root = Group()
root.add("p1", ParamComp("x", 50.0), promotes=["*"])
root.add("p2", ParamComp("y", 50.0), promotes=["*"])
root.add("comp", Paraboloid(), promotes=["*"])
prob.driver = ScipyOptimizer()
prob.driver.options["optimizer"] = "SLSQP"
prob.driver.add_param("x", low=-50.0, high=50.0)
prob.driver.add_param("y", low=-50.0, high=50.0)
prob.driver.add_objective("f_xy")
prob.setup(check=False)
prob.run()
# Optimal solution (minimum): x = 6.6667; y = -7.3333
assert_rel_error(self, prob["x"], 6.666667, 1e-6)
assert_rel_error(self, prob["y"], -7.333333, 1e-6)
def test_simple_array_comp2D(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', np.zeros((2, 2))), promotes=['*'])
root.add('comp', ArrayComp2D(), promotes=['*'])
root.add('con', ExecComp('c = y - 20.0', c=np.zeros((2, 2)), y=np.zeros((2, 2))), promotes=['*'])
root.add('obj', ExecComp('o = y[0, 0]', y=np.zeros((2, 2))), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', ctype='eq')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_paraboloid_unconstrained_SLSQP(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
# Optimal solution (minimum): x = 6.6667; y = -7.3333
assert_rel_error(self, prob['x'], 6.666667, 1e-6)
assert_rel_error(self, prob['y'], -7.333333, 1e-6)
def test_simple_paraboloid_unconstrained_SLSQP_bounds(self):
# Make sure we don't go past high/low when set.
prob = Problem()
root = prob.root = Group()
root.add("p1", ParamComp("x", 50.0), promotes=["*"])
root.add("p2", ParamComp("y", 50.0), promotes=["*"])
root.add("comp", Paraboloid(), promotes=["*"])
root.add("obj_comp", ExecComp("obj = -f_xy"), promotes=["*"])
prob.driver = ScipyOptimizer()
prob.driver.options["optimizer"] = "SLSQP"
prob.driver.add_param("x", low=-50.0, high=50.0)
prob.driver.add_param("y", low=-50.0, high=50.0)
prob.driver.add_objective("obj")
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob["x"], 50.0, 1e-6)
assert_rel_error(self, prob["y"], 50.0, 1e-6)
def test_simple_paraboloid(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = 15.0 - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c')
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('con', ExecComp('c = 15.0 - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c')
prob.setup(check=False)
prob.run()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, 1e-6)
assert_rel_error(self, prob['y'], -7.833334, 1e-6)
def test_simple_paraboloid_unconstrained_SLSQP(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('f_xy')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
# Optimal solution (minimum): x = 6.6667; y = -7.3333
assert_rel_error(self, prob['x'], 6.666667, 1e-6)
assert_rel_error(self, prob['y'], -7.333333, 1e-6)
def test_fan_out(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 1.0))
root.add('p2', ParamComp('x', 1.0))
root.add('comp1', ExecComp('y = 3.0*x'))
root.add('comp2', ExecComp('y = 5.0*x'))
root.add('obj', ExecComp('o = i1 + i2'))
root.add('con1', ExecComp('c = 15.0 - x'))
root.add('con2', ExecComp('c = 15.0 - x'))
# hook up non explicitly
root.connect('p1.x', 'comp1.x')
root.connect('p2.x', 'comp2.x')
root.connect('comp1.y', 'obj.i1')
root.connect('comp2.y', 'obj.i2')
root.connect('comp1.y', 'con1.x')
root.connect('comp2.y', 'con2.x')
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('p1.x', low=-50.0, high=50.0)
prob.driver.add_param('p2.x', low=-50.0, high=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', ctype='eq')
prob.driver.add_constraint('con2.c', ctype='eq')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
obj = prob['obj.o']
assert_rel_error(self, obj, 30.0, 1e-6)
def test_simple_paraboloid_unconstrained_SLSQP_bounds(self):
# Make sure we don't go past high/low when set.
prob = Problem()
root = prob.root = Group()
root.add('p1', ParamComp('x', 50.0), promotes=['*'])
root.add('p2', ParamComp('y', 50.0), promotes=['*'])
root.add('comp', Paraboloid(), promotes=['*'])
root.add('obj_comp', ExecComp('obj = -f_xy'), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_param('x', low=-50.0, high=50.0)
prob.driver.add_param('y', low=-50.0, high=50.0)
prob.driver.add_objective('obj')
prob.driver.options['disp'] = False
prob.setup(check=False)
prob.run()
assert_rel_error(self, prob['x'], 50.0, 1e-6)
assert_rel_error(self, prob['y'], 50.0, 1e-6)
m = 300
npts = 6
restart = False
# These numbers are for quick testing
#n = 150
#m = 6
#npts = 2
# Instantiate
model = Problem(impl=impl)
root = model.root = CADRE_MDP_Group(n=n, m=m, npts=npts)
# add SNOPT driver
model.driver = pyOptSparseDriver()
model.driver.options['optimizer'] = "SNOPT"
model.driver.opt_settings = {'Major optimality tolerance': 1e-3,
'Major feasibility tolerance': 1.0e-5,
'Iterations limit': 500000000,
"New basis file": 10}
# Restart File
if restart is True and os.path.exists("fort.10"):
model.driver.opt_settings["Old basis file"] = 10
# Add parameters and constraints to each CADRE instance.
names = ['pt%s' % i for i in range(npts)]
for i, name in enumerate(names):
# add parameters to driver
if __name__ == '__main__':
# Setup and run the model.
from openmdao.core.problem import Problem
from openmdao.drivers.scipy_optimizer import ScipyOptimizer
from openmdao.api import pyOptSparseDriver
from openmdao.api import SqliteRecorder
top = Problem()
top.root = SellarDerivatives()
# top.driver = ScipyOptimizer()
# top.driver.options['optimizer'] = 'SLSQP'
# top.driver.options['tol'] = 1.0e-8
top.driver = pyOptSparseDriver()
top.driver.options['optimizer'] = 'SNOPT'
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=0.0, upper=10.0)
top.driver.add_objective('obj')
top.driver.add_constraint('con1', upper=0.0)
top.driver.add_constraint('con2', upper=0.0)
rec = SqliteRecorder('sellar_snopt.db')
top.driver.add_recorder(rec)
rec.options['record_derivs'] = True
top.setup()
root = top.root = Group()
root.add('simprop', IndepVarComp('simtime', val="100"))
root.add('p1', IndepVarComp('rho', val=1.0))
root.add('p2', IndepVarComp('jturbine', val=1.0))
root.add('p3', IndepVarComp('ratio', val=1.0))
root.add(
'modelica',
OMModelWrapper(
'WindPowerPlants.Examples.GenericPlantRayleigh',
'/Users/adam/repo/WindPowerPlants/WindPowerPlants/package.mo'))
root.add('tl_peakPowerOutput', TakeLast())
root.add('tl_integratedEnergy', TakeLast())
top.driver = UniformDriver(5000)
top.driver.add_desvar('p1.rho', low=1.0, high=1.5)
top.driver.add_desvar('p2.jturbine',
low=13000000 * 0.7,
high=130000000 * 1.3)
top.driver.add_desvar('p3.ratio', low=80.0, high=120.0)
top.driver.add_objective('tl_peakPowerOutput.output')
top.driver.add_objective('tl_integratedEnergy.output')
root.connect('simprop.simtime', 'modelica.stopTime')
root.connect('p1.rho', 'modelica.rho')
root.connect('p2.jturbine', 'modelica.jturbine')
root.connect('p3.ratio', 'modelica.ratio')
root.connect('modelica.peakPowerOutput', 'tl_peakPowerOutput.input')
root.connect('modelica.integratedEnergy', 'tl_integratedEnergy.input')
if __name__ == "__main__":
top = Problem()
root = top.root = Group()
root.add('p1', ParamComp('x', 3.0))
root.add('p2', ParamComp('y', -4.0))
root.add('p', Paraboloid())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_param('p1.x', low=-50, high=50)
top.driver.add_param('p2.y', low=-50, high=50)
top.driver.add_objective('p.f_xy')
top.setup()
top.run()
print('\n')
print('Minimum of %f found at (%f, %f)' %
(top['p.f_xy'], top['p.x'], top['p.y']))
# Expected Output
# Minimum of -27.333333 found at (6.666667, -7.333333)
