def test_parab_FD(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add("par", ParallelGroup())
par.add("c1", Parab1D(root=2.0))
par.add("c2", Parab1D(root=3.0))
root.add("p1", ParamComp("x", val=0.0))
root.add("p2", ParamComp("x", val=0.0))
root.connect("p1.x", "par.c1.x")
root.connect("p2.x", "par.c2.x")
root.add("sumcomp", ExecComp("sum = x1+x2"))
root.connect("par.c1.y", "sumcomp.x1")
root.connect("par.c2.y", "sumcomp.x2")
driver = model.driver = pyOptSparseDriver()
driver.add_param("p1.x", low=-100, high=100)
driver.add_param("p2.x", low=-100, high=100)
driver.add_objective("sumcomp.sum")
root.fd_options["force_fd"] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model["p1.x"], 2.0, 1.0e-6)
assert_rel_error(self, model["p2.x"], 3.0, 1.0e-6)
def test_simple_paraboloid_scaled_objective_rev(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.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy', scaler=1 / 10.)
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'rev'
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_parab_FD_subbed_Pcomps(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add("par", ParallelGroup())
par.add("s1", MP_Point(root=2.0))
par.add("s2", MP_Point(root=3.0))
root.add("sumcomp", ExecComp("sum = x1+x2"))
root.connect("par.s1.c.y", "sumcomp.x1")
root.connect("par.s2.c.y", "sumcomp.x2")
driver = model.driver = pyOptSparseDriver()
driver.add_param("par.s1.p.x", low=-100, high=100)
driver.add_param("par.s2.p.x", low=-100, high=100)
driver.add_objective("sumcomp.sum")
root.fd_options["force_fd"] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model["par.s1.p.x"], 2.0, 1.0e-6)
if not MPI or self.comm.rank == 1:
assert_rel_error(self, model["par.s2.p.x"], 3.0, 1.0e-6)
def test_simple_paraboloid_equality_linear(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.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', equals=-15.0, linear=True)
if OPTIMIZER == 'SNOPT':
# there is currently a bug in SNOPT, it requires at least one
# nonlinear inequality constraint, so provide a 'fake' one
prob.driver.add_constraint('x', lower=-100.0)
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_equality_linear(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.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', equals=-15.0, linear=True)
if OPTIMIZER == 'SNOPT':
# there is currently a bug in SNOPT, it requires at least one
# nonlinear inequality constraint, so provide a 'fake' one
prob.driver.add_constraint('x', lower=-100.0)
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_parab_subbed_Pcomps(self):
model = Problem(impl=impl)
root = model.root = Group()
root.ln_solver = lin_solver()
par = root.add('par', ParallelGroup())
par.add('s1', MP_Point(root=2.0))
par.add('s2', MP_Point(root=3.0))
root.add('sumcomp', ExecComp('sum = x1+x2'))
root.connect('par.s1.c.y', 'sumcomp.x1')
root.connect('par.s2.c.y', 'sumcomp.x2')
driver = model.driver = pyOptSparseDriver()
driver.options['optimizer'] = OPTIMIZER
driver.options['print_results'] = False
driver.add_desvar('par.s1.p.x', lower=-100, upper=100)
driver.add_desvar('par.s2.p.x', lower=-100, upper=100)
driver.add_objective('sumcomp.sum')
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model['par.s1.p.x'], 2.0, 1.e-6)
if not MPI or self.comm.rank == 1:
assert_rel_error(self, model['par.s2.p.x'], 3.0, 1.e-6)
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_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_root_derivs_dict(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()
self.io.seek(0)
csv_reader = csv.DictReader(self.io)
rows = [row for row in csv_reader]
# execution
row = rows[0]
self.assertEqual(row['Derivatives'], '')
# derivatives
row = rows[1]
self.assertEqual(row['obj'], '')
J1 = eval(row['Derivatives'])[0]
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)
def test_parab_FD(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add('par', ParallelGroup())
par.add('c1', Parab1D(root=2.0))
par.add('c2', Parab1D(root=3.0))
root.add('p1', IndepVarComp('x', val=0.0))
root.add('p2', IndepVarComp('x', val=0.0))
root.connect('p1.x', 'par.c1.x')
root.connect('p2.x', 'par.c2.x')
root.add('sumcomp', ExecComp('sum = x1+x2'))
root.connect('par.c1.y', 'sumcomp.x1')
root.connect('par.c2.y', 'sumcomp.x2')
driver = model.driver = pyOptSparseDriver()
driver.options['optimizer'] = OPTIMIZER
driver.options['print_results'] = False
driver.add_desvar('p1.x', lower=-100, upper=100)
driver.add_desvar('p2.x', lower=-100, upper=100)
driver.add_objective('sumcomp.sum')
root.fd_options['force_fd'] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model['p1.x'], 2.0, 1.e-6)
assert_rel_error(self, model['p2.x'], 3.0, 1.e-6)
def test_simple_paraboloid_lower(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.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', lower=15.0)
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_inf_as_desvar_bounds(self):
# User may use np.inf as a bound. It is unneccessary, but the user
# may do it anyway, so make sure SLSQP doesn't blow up with it (bug
# reported by rfalck)
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.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-np.inf, upper=np.inf)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
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_pbo_desvar_slsqp(self):
if pyOptSparseDriver is None:
raise unittest.SkipTest("pyOptSparse not installed")
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', u'var_x', pass_by_obj=True))
root.add('p2', IndepVarComp('y', -4.0))
root.add('p', PassByObjParaboloid())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = pyOptSparseDriver()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.add_desvar('p1.x')
top.driver.add_desvar('p2.y')
top.driver.add_objective('p.f_xy')
try:
top.setup(check=False)
except Exception as err:
self.assertEqual(str(err), "Parameter 'p1.x' is a 'pass_by_obj' variable and "
"can't be used with a gradient based driver of type 'SLSQP'.")
else:
self.fail("Exception expected")
def test_raised_error_sensfunc(self):
# Component fails hard this time during gradient eval, so we expect
# pyoptsparse to raise.
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', ParaboloidAE(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
prob.root.comp.fail_hard = True
prob.root.comp.grad_fail_at = 2
prob.root.comp.eval_fail_at = 100
prob.setup(check=False)
with self.assertRaises(Exception) as err:
prob.run()
def test_parab_FD_subbed_Pcomps(self):
model = Problem(impl=impl)
root = model.root = Group()
par = root.add('par', ParallelGroup())
par.add('s1', MP_Point(root=2.0))
par.add('s2', MP_Point(root=3.0))
root.add('sumcomp', ExecComp('sum = x1+x2'))
root.connect('par.s1.c.y', 'sumcomp.x1')
root.connect('par.s2.c.y', 'sumcomp.x2')
driver = model.driver = pyOptSparseDriver()
driver.add_param('par.s1.p.x', low=-100, high=100)
driver.add_param('par.s2.p.x', low=-100, high=100)
driver.add_objective('sumcomp.sum')
root.fd_options['force_fd'] = True
model.setup(check=False)
model.run()
if not MPI or self.comm.rank == 0:
assert_rel_error(self, model['par.s1.p.x'], 2.0, 1.e-6)
if not MPI or self.comm.rank == 1:
assert_rel_error(self, model['par.s2.p.x'], 3.0, 1.e-6)
def test_simple_paraboloid_scaled_objective_rev(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.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy', scaler=1/10.)
prob.driver.add_constraint('c', lower=10.0, upper=11.0)
root.ln_solver.options['mode'] = 'rev'
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_driver_param_indices_force_fd(self):
""" Test driver param indices with pyOptSparse and force_fd=True
"""
prob = Problem()
prob.root = SellarStateConnection()
prob.root.fd_options['force_fd'] = True
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('z', lower=np.array([-10.0]),
upper=np.array([10.0]), indices=[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.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 setUp(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(impl=impl)
root = prob.root = Group()
#root.ln_solver = lin_solver()
root.ln_solver = LinearGaussSeidel()
par = root.add('par', ParallelGroup())
par.ln_solver = LinearGaussSeidel()
ser1 = par.add('ser1', Group())
ser1.ln_solver = LinearGaussSeidel()
ser1.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['x'])
ser1.add('comp', SimpleArrayComp(), promotes=['x', 'y'])
ser1.add('con',
ExecComp('c = y - 20.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['c', 'y'])
ser1.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['y', 'o'])
ser2 = par.add('ser2', Group())
ser2.ln_solver = LinearGaussSeidel()
ser2.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['x'])
ser2.add('comp', SimpleArrayComp(), promotes=['x', 'y'])
ser2.add('con',
ExecComp('c = y - 30.0',
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['c', 'y'])
ser2.add('obj',
ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['o', 'y'])
root.add('total', ExecComp('obj = x1 + x2'))
root.connect('par.ser1.o', 'total.x1')
root.connect('par.ser2.o', 'total.x2')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('par.ser1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('par.ser2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('total.obj')
prob.driver.add_constraint('par.ser1.c', equals=0.0)
prob.driver.add_constraint('par.ser2.c', equals=0.0)
self.prob = prob
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_multipoint_with_coloring(self):
size = 10
num_pts = self.N_PROCS
np.random.seed(11)
p = Problem()
p.driver = pyOptSparseDriver()
p.driver.options['optimizer'] = OPTIMIZER
p.driver.options['dynamic_simul_derivs'] = True
if OPTIMIZER == 'SNOPT':
p.driver.opt_settings['Major iterations limit'] = 100
p.driver.opt_settings['Major feasibility tolerance'] = 1.0E-6
p.driver.opt_settings['Major optimality tolerance'] = 1.0E-6
p.driver.opt_settings['iSumm'] = 6
model = p.model
for i in range(num_pts):
model.add_subsystem('indep%d' % i, IndepVarComp('x', val=np.ones(size)))
model.add_design_var('indep%d.x' % i)
par1 = model.add_subsystem('par1', ParallelGroup())
for i in range(num_pts):
mat = _get_mat(5, size)
par1.add_subsystem('comp%d' % i, ExecComp('y=A.dot(x)', A=mat, x=np.ones(size), y=np.ones(5)))
model.connect('indep%d.x' % i, 'par1.comp%d.x' % i)
par2 = model.add_subsystem('par2', ParallelGroup())
for i in range(num_pts):
mat = _get_mat(size, 5)
par2.add_subsystem('comp%d' % i, ExecComp('y=A.dot(x)', A=mat, x=np.ones(5), y=np.ones(size)))
model.connect('par1.comp%d.y' % i, 'par2.comp%d.x' % i)
par2.add_constraint('comp%d.y' % i, lower=-1.)
model.add_subsystem('normcomp%d' % i, ExecComp("y=sum(x*x)", x=np.ones(size)))
model.connect('par2.comp%d.y' % i, 'normcomp%d.x' % i)
model.add_subsystem('obj', ExecComp("y=" + '+'.join(['x%d' % i for i in range(num_pts)])))
for i in range(num_pts):
model.connect('normcomp%d.y' % i, 'obj.x%d' % i)
model.add_objective('obj.y')
p.setup()
p.run_driver()
J = p.compute_totals()
for i in range(num_pts):
vname = 'par2.comp%d.A' % i
if vname in model._var_abs_names['input']:
norm = np.linalg.norm(J['par2.comp%d.y'%i,'indep%d.x'%i] -
getattr(par2, 'comp%d'%i)._inputs['A'].dot(getattr(par1, 'comp%d'%i)._inputs['A']))
self.assertLess(norm, 1.e-7)
elif vname not in model._var_allprocs_abs_names['input']:
self.fail("Can't find variable par2.comp%d.A" % i)
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']['deriv']
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)
def test_multipoint_with_coloring(self):
size = 10
num_pts = self.N_PROCS
np.random.seed(11)
p = Problem()
p.driver = pyOptSparseDriver()
p.driver.options['optimizer'] = OPTIMIZER
p.driver.options['dynamic_simul_derivs'] = True
if OPTIMIZER == 'SNOPT':
p.driver.opt_settings['Major iterations limit'] = 100
p.driver.opt_settings['Major feasibility tolerance'] = 1.0E-6
p.driver.opt_settings['Major optimality tolerance'] = 1.0E-6
p.driver.opt_settings['iSumm'] = 6
model = p.model
for i in range(num_pts):
model.add_subsystem('indep%d' % i, IndepVarComp('x', val=np.ones(size)))
model.add_design_var('indep%d.x' % i)
par1 = model.add_subsystem('par1', ParallelGroup())
for i in range(num_pts):
mat = _get_mat(5, size)
par1.add_subsystem('comp%d' % i, ExecComp('y=A.dot(x)', A=mat, x=np.ones(size), y=np.ones(5)))
model.connect('indep%d.x' % i, 'par1.comp%d.x' % i)
par2 = model.add_subsystem('par2', ParallelGroup())
for i in range(num_pts):
mat = _get_mat(size, 5)
par2.add_subsystem('comp%d' % i, ExecComp('y=A.dot(x)', A=mat, x=np.ones(5), y=np.ones(size)))
model.connect('par1.comp%d.y' % i, 'par2.comp%d.x' % i)
par2.add_constraint('comp%d.y' % i, lower=-1.)
model.add_subsystem('normcomp%d' % i, ExecComp("y=sum(x*x)", x=np.ones(size)))
model.connect('par2.comp%d.y' % i, 'normcomp%d.x' % i)
model.add_subsystem('obj', ExecComp("y=" + '+'.join(['x%d' % i for i in range(num_pts)])))
for i in range(num_pts):
model.connect('normcomp%d.y' % i, 'obj.x%d' % i)
model.add_objective('obj.y')
p.setup()
p.run_driver()
J = p.compute_totals()
for i in range(num_pts):
vname = 'par2.comp%d.A' % i
if vname in model._var_abs_names['input']:
norm = np.linalg.norm(J['par2.comp%d.y'%i,'indep%d.x'%i] -
getattr(par2, 'comp%d'%i)._inputs['A'].dot(getattr(par1, 'comp%d'%i)._inputs['A']))
self.assertLess(norm, 1.e-7)
elif vname not in model._var_allprocs_abs_names['input']:
self.fail("Can't find variable par2.comp%d.A" % i)
def setUp(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")
self.concurrent_setUp(prefix='par_mpi_opt-')
prob = Problem(impl=impl)
root = prob.root = Group()
#root.ln_solver = lin_solver()
root.ln_solver = LinearGaussSeidel()
par = root.add('par', ParallelGroup())
par.ln_solver = LinearGaussSeidel()
ser1 = par.add('ser1', Group())
ser1.ln_solver = LinearGaussSeidel()
ser1.add('p1', IndepVarComp('x', np.zeros([2])))
ser1.add('comp', SimpleArrayComp())
ser1.add('con', ExecComp('c = y - 20.0', c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])))
ser1.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])))
ser2 = par.add('ser2', Group())
ser2.ln_solver = LinearGaussSeidel()
ser2.add('p1', IndepVarComp('x', np.zeros([2])))
ser2.add('comp', SimpleArrayComp())
ser2.add('con', ExecComp('c = y - 30.0', c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])))
ser2.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])))
root.add('total', ExecComp('obj = x1 + x2'))
ser1.connect('p1.x', 'comp.x')
ser1.connect('comp.y', 'con.y')
ser1.connect('comp.y', 'obj.y')
root.connect('par.ser1.obj.o', 'total.x1')
ser2.connect('p1.x', 'comp.x')
ser2.connect('comp.y', 'con.y')
ser2.connect('comp.y', 'obj.y')
root.connect('par.ser2.obj.o', 'total.x2')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('par.ser1.p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('par.ser2.p1.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('total.obj')
prob.driver.add_constraint('par.ser1.con.c', equals=0.0)
prob.driver.add_constraint('par.ser2.con.c', equals=0.0)
self.prob = prob
def magneplane_brachistochrone(solver='SLSQP', num_seg=3, seg_ncn=3):
prob = Problem()
traj = prob.add_traj(Trajectory("traj0"))
if solver == 'SNOPT':
if pyOptSparseDriver is None:
raise ValueError('Requested SNOPT but pyoptsparse is not available')
driver=pyOptSparseDriver()
driver.options['optimizer'] = solver
driver.opt_settings['Major iterations limit'] = 1000
driver.opt_settings['iSumm'] = 6
driver.opt_settings['Major step limit'] = 0.5
driver.opt_settings["Major feasibility tolerance"] = 1.0E-6
driver.opt_settings["Major optimality tolerance"] = 1.0E-6
driver.opt_settings["Minor feasibility tolerance"] = 1.0E-4
driver.opt_settings['Verify level'] = 3
else:
driver=ScipyOptimizer()
driver.options['tol'] = 1.0E-6
driver.options['disp'] = True
driver.options['maxiter'] = 500
prob.driver = driver
dynamic_controls = [ {'name':'g','units':'m/s**2'},
{'name':'T','units':'N'},
{'name':'D','units':'N'},
{'name':'mass','units':'kg'},
{'name':'psi','units':'rad'},
{'name':'theta', 'units':'rad'},
{'name':'phi','units':'rad'} ]
phase0 = CollocationPhase(name='phase0',rhs_class=MagneplaneRHS,num_seg=num_seg,seg_ncn=seg_ncn,rel_lengths="equal",
dynamic_controls=dynamic_controls,static_controls=None)
traj.add_phase(phase0)
phase0.set_state_options('x', lower=0,upper=10,ic_val=0,ic_fix=True,fc_val=10,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('y', lower=0,upper=0,ic_val=0,ic_fix=True,fc_val=0,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('z', lower=-10,upper=0,ic_val=-10,ic_fix=True,fc_val=-5,fc_fix=True,defect_scaler=0.1)
phase0.set_state_options('v', lower=0, upper=np.inf,ic_val=0.0,ic_fix=True,fc_val=10.0,fc_fix=False,defect_scaler=0.1)
phase0.set_dynamic_control_options(name='psi', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='theta', val=phase0.node_space(-.46,-.46),opt=True,lower=-1.57,upper=1.57,scaler=1.0)
phase0.set_dynamic_control_options(name='phi', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='g', val=phase0.node_space(9.80665, 9.80665), opt=False)
phase0.set_dynamic_control_options(name='T', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='D', val=phase0.node_space(0.0, 0.0), opt=False)
phase0.set_dynamic_control_options(name='mass', val=phase0.node_space(1000.0, 1000.0), opt=False)
phase0.set_time_options(t0_val=0,t0_lower=0,t0_upper=0,tp_val=2.0,tp_lower=0.5,tp_upper=10.0)
traj.add_objective(name="t",phase="phase0",place="end",scaler=1.0)
return prob
def test_reading_system_metadata(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")
self.setup_sellar_grouped_scaled_model()
self.prob.driver = pyOptSparseDriver()
self.prob.driver.options['optimizer'] = OPTIMIZER
self.prob.model.options.declare("test1", 1)
self.prob.model.mda.d1.options.declare("test2", "2")
self.prob.model.pz.options.declare("test3", True)
self.prob.model.pz.recording_options['options_excludes'] = ['*']
if OPTIMIZER == 'SLSQP':
self.prob.driver.opt_settings['ACC'] = 1e-9
self.prob.model.recording_options['record_inputs'] = True
self.prob.model.recording_options['record_outputs'] = True
self.prob.model.recording_options['record_residuals'] = True
self.prob.model.recording_options['record_metadata'] = True
self.prob.model.add_recorder(self.recorder)
pz = self.prob.model.pz # IndepVarComp which is an ExplicitComponent
pz.add_recorder(self.recorder)
mda = self.prob.model.mda # Group
d1 = mda.d1
d1.add_recorder(self.recorder)
self.prob.setup(check=False, mode='rev')
self.prob.run_driver()
self.prob.cleanup()
cr = CaseReader(self.filename)
self.assertEqual(
sorted(cr.system_metadata.keys()),
sorted(['root', 'mda.d1', 'pz'])
)
self.assertEqual(cr.system_metadata['root']['component_options']['test1'], 1)
self.assertEqual(cr.system_metadata['mda.d1']['component_options']['test2'], "2")
self.assertFalse('test3' in cr.system_metadata['pz']['component_options'])
assert_rel_error(
self, cr.system_metadata['pz']['scaling_factors']['output']['nonlinear']['phys'][0][1], [2.0, 2.0], 1.0e-3)
def setUp(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(impl=impl)
root = prob.root = Group()
#root.ln_solver = lin_solver() # this works too (PetscKSP)
root.ln_solver = LinearGaussSeidel()
par = root.add('par', ParallelGroup())
par.ln_solver = LinearGaussSeidel()
ser1 = par.add('ser1', Group())
ser1.ln_solver = LinearGaussSeidel()
ser1.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['*'])
ser1.add('comp', SimpleArrayComp(), promotes=['*'])
ser1.add('con', ExecComp4Test('c = y - 20.0', # lin_delay=.1,
c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])),
promotes=['c', 'y'])
ser1.add('obj', ExecComp4Test('o = y[0]', # lin_delay=.1,
y=np.array([0.0, 0.0])),
promotes=['y', 'o'])
ser2 = par.add('ser2', Group())
ser2.ln_solver = LinearGaussSeidel()
ser2.add('p1', IndepVarComp('x', np.zeros([2])), promotes=['*'])
ser2.add('comp', SimpleArrayComp(), promotes=['*'])
ser2.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])),
promotes=['y', 'o'])
root.add('con', ExecComp('c = y - 30.0', c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])))
root.add('total', ExecComp('obj = x1 + x2'))
root.connect('par.ser1.o', 'total.x1')
root.connect('par.ser2.o', 'total.x2')
root.connect('par.ser2.y', 'con.y')
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.add_desvar('par.ser1.x', low=-50.0, high=50.0)
prob.driver.add_desvar('par.ser2.x', low=-50.0, high=50.0)
prob.driver.add_objective('total.obj')
prob.driver.add_constraint('par.ser1.c', equals=0.0)
prob.driver.add_constraint('con.c', equals=0.0)
self.prob = prob
def test_reading_driver_cases(self):
""" Tests that the reader returns params correctly. """
prob = SellarProblem(SellarDerivativesGrouped)
driver = prob.driver = pyOptSparseDriver(optimizer='SLSQP')
driver.options['print_results'] = False
driver.opt_settings['ACC'] = 1e-9
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.add_recorder(self.recorder)
prob.setup()
prob.run_driver()
prob.cleanup()
cr = CaseReader(self.filename)
# Test to see if we got the correct number of cases
self.assertEqual(cr.driver_cases.num_cases, 7)
self.assertEqual(cr.system_cases.num_cases, 0)
self.assertEqual(cr.solver_cases.num_cases, 0)
# Test to see if the access by case keys works:
seventh_slsqp_iteration_case = cr.driver_cases.get_case(
'rank0:SLSQP|5')
np.testing.assert_almost_equal(
seventh_slsqp_iteration_case.outputs['z'],
[1.97846296, -2.21388305e-13],
decimal=2,
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('pz.z'))
# Test values from one case, the last case
last_case = cr.driver_cases.get_case(-1)
np.testing.assert_almost_equal(last_case.outputs['z'],
prob['z'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('pz.z'))
np.testing.assert_almost_equal(last_case.outputs['x'], [-0.00309521],
decimal=2,
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('px.x'))
# Test to see if the case keys (iteration coords) come back correctly
case_keys = cr.driver_cases.list_cases()
print(case_keys)
for i, iter_coord in enumerate(case_keys):
self.assertEqual(iter_coord, 'rank0:SLSQP|{}'.format(i))
def setUp(self):
if SKIP:
raise unittest.SkipTest('Could not import pyOptSparseDriver. Is pyoptsparse installed?')
prob = Problem(impl=impl)
root = prob.root = Group()
#root.ln_solver = lin_solver()
root.ln_solver = LinearGaussSeidel()
par = root.add('par', ParallelGroup())
par.ln_solver = LinearGaussSeidel()
ser1 = par.add('ser1', Group())
ser1.ln_solver = LinearGaussSeidel()
ser1.add('p1', IndepVarComp('x', np.zeros([2])))
ser1.add('comp', SimpleArrayComp())
ser1.add('con', ExecComp('c = y - 20.0', c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])))
ser1.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])))
ser2 = par.add('ser2', Group())
ser2.ln_solver = LinearGaussSeidel()
ser2.add('p1', IndepVarComp('x', np.zeros([2])))
ser2.add('comp', SimpleArrayComp())
ser2.add('con', ExecComp('c = y - 30.0', c=np.array([0.0, 0.0]),
y=np.array([0.0, 0.0])))
ser2.add('obj', ExecComp('o = y[0]', y=np.array([0.0, 0.0])))
root.add('total', ExecComp('obj = x1 + x2'))
ser1.connect('p1.x', 'comp.x')
ser1.connect('comp.y', 'con.y')
ser1.connect('comp.y', 'obj.y')
root.connect('par.ser1.obj.o', 'total.x1')
ser2.connect('p1.x', 'comp.x')
ser2.connect('comp.y', 'con.y')
ser2.connect('comp.y', 'obj.y')
root.connect('par.ser2.obj.o', 'total.x2')
prob.driver = pyOptSparseDriver()
prob.driver.add_desvar('par.ser1.p1.x', low=-50.0, high=50.0)
prob.driver.add_desvar('par.ser2.p1.x', low=-50.0, high=50.0)
prob.driver.add_objective('total.obj')
prob.driver.add_constraint('par.ser1.con.c', equals=0.0)
prob.driver.add_constraint('par.ser2.con.c', equals=0.0)
self.prob = prob
def test_root_derivs_dict(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()
db = SqliteDict(self.filename, self.tablename_derivs, flag="r")
J1 = db["rank0:SLSQP|1"]["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, 0.00001)
def test_fan_out(self):
# This tests sparse-response specification.
# This is a slightly modified FanOut
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 1.0))
model.add_subsystem('p2', IndepVarComp('x', 1.0))
model.add_subsystem('comp1', ExecComp('y = 3.0*x'))
model.add_subsystem('comp2', ExecComp('y = 5.0*x'))
model.add_subsystem('obj', ExecComp('o = i1 + i2'))
model.add_subsystem('con1', ExecComp('c = 15.0 - x'))
model.add_subsystem('con2', ExecComp('c = 15.0 - x'))
# hook up explicitly
model.connect('p1.x', 'comp1.x')
model.connect('p2.x', 'comp2.x')
model.connect('comp1.y', 'obj.i1')
model.connect('comp2.y', 'obj.i2')
model.connect('comp1.y', 'con1.x')
model.connect('comp2.y', 'con2.x')
prob.set_solver_print(level=0)
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
model.add_design_var('p1.x', lower=-50.0, upper=50.0)
model.add_design_var('p2.x', lower=-50.0, upper=50.0)
model.add_objective('obj.o')
model.add_constraint('con1.c', equals=0.0)
model.add_constraint('con2.c', equals=0.0)
prob.setup(check=False)
prob.run_driver()
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_prob_split_comm(self):
colors = [0, 0, 1, 1]
comm = MPI.COMM_WORLD.Split(colors[MPI.COMM_WORLD.rank])
# split the size 4 comm into 2 size 2 comms
self.assertEqual(comm.size, 2)
prob = Problem(comm=comm)
model = prob.model
p1 = model.add_subsystem('p1', IndepVarComp('x', 99.0))
p1.add_design_var('x', lower=-50.0, upper=50.0)
par = model.add_subsystem('par', ParallelGroup())
c1 = par.add_subsystem('C1', ExecComp('y = x*x'))
c2 = par.add_subsystem('C2', ExecComp('y = x*x'))
model.add_subsystem('obj', ExecComp('o = a + b + 2.'))
model.connect('p1.x', ['par.C1.x', 'par.C2.x'])
model.connect('par.C1.y', 'obj.a')
model.connect('par.C2.y', 'obj.b')
model.add_objective('obj.o')
prob.set_solver_print(level=0)
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.setup()
prob.run_model()
failed = prob.run_driver()
all_failed = comm.allgather(failed)
if any(all_failed):
all_msgs = comm.allgather(str(
prob.driver.pyopt_solution.optInform))
for i, tup in enumerate(zip(all_failed, all_msgs)):
failed, msg = tup
if failed:
self.fail("Optimization failed on rank %d: %s" % (i, msg))
objs = comm.allgather(prob['obj.o'])
for i, obj in enumerate(objs):
assert_near_equal(obj, 2.0, 1e-6)
def test_analysis_error_sensfunc(self):
# Component raises an analysis error during some linearize calls, and
# pyopt attempts to recover.
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
comp = model.add_subsystem('comp', ParaboloidAE(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
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)
comp.grad_fail_at = 2
comp.eval_fail_at = 100
prob.setup(check=False)
prob.run_driver()
# SLSQP does a bad job recovering from gradient failures
if OPTIMIZER == 'SLSQP':
tol = 1e-2
else:
tol = 1e-6
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, tol)
assert_rel_error(self, prob['y'], -7.833334, tol)
# Normally it takes 9 iterations, but takes 13 here because of the
# gradfunc failures. (note SLSQP just doesn't do well)
if OPTIMIZER == 'SNOPT':
self.assertEqual(prob.driver.iter_count, 15)
def setup(self):
self.model = Problem(impl=impl)
self.model.root = MPPT_MDP()
# add SNOPT driver
self.model.driver = pyOptSparseDriver()
self.model.driver.options['optimizer'] = "SNOPT"
self.model.driver.opt_settings = {'Major optimality tolerance': 1e-3,
'Major feasibility tolerance': 1.0e-5,
'Iterations limit': 500000000,
"New basis file": 10}
self.model.driver.add_objective("perf.result")
self.model.driver.add_desvar("pt0.param.CP_Isetpt", lower=0., upper=0.4)
self.model.driver.add_desvar("pt1.param.CP_Isetpt", lower=0., upper=0.4)
self.model.setup()
def test_sparsity_fd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('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 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.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.root.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
# Verify that the appropriate sparsity pattern is applied
dv_dict = {'p1.x': 1.0, 'p2.x': 1.0}
prob.driver._problem = prob
sens_dict, fail = prob.driver._gradfunc(dv_dict, {})
self.assertTrue('p2.x' not in sens_dict['con1.c'])
self.assertTrue('p1.x' in sens_dict['con1.c'])
self.assertTrue('p2.x' in sens_dict['con2.c'])
self.assertTrue('p1.x' not in sens_dict['con2.c'])
self.assertTrue('p1.x' in sens_dict['obj.o'])
self.assertTrue('p2.x' in sens_dict['obj.o'])
def test_root_derivs_dict(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
self.recorder.options['record_unknowns'] = True
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()
sout = open(self.filename)
lines = sout.readlines()
self.assertEqual(lines[14].rstrip(), 'Derivatives:')
self.assertTrue(' con1 wrt x:' in lines[15])
self.assertTrue(' con1 wrt z:' in lines[16])
self.assertTrue(' con2 wrt x:' in lines[17])
self.assertTrue(' con2 wrt z:' in lines[18])
self.assertTrue(' obj wrt x:' in lines[19])
self.assertTrue(' obj wrt z:' in lines[20])
self.assertTrue('1.784' in lines[20])
def test_sparsity_fd(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('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 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.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
prob.root.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
# Verify that the appropriate sparsity pattern is applied
dv_dict = {'p1.x': 1.0, 'p2.x': 1.0}
prob.driver._problem = prob
sens_dict, fail = prob.driver._gradfunc(dv_dict, {})
self.assertTrue('p2.x' not in sens_dict['con1.c'])
self.assertTrue('p1.x' in sens_dict['con1.c'])
self.assertTrue('p2.x' in sens_dict['con2.c'])
self.assertTrue('p1.x' not in sens_dict['con2.c'])
self.assertTrue('p1.x' in sens_dict['obj.o'])
self.assertTrue('p2.x' in sens_dict['obj.o'])
def test_totals_of_wrt_indices(self, method):
prob = Problem(coloring_dir=self.tempdir)
model = prob.model = CounterGroup()
prob.driver = pyOptSparseDriver(optimizer='SLSQP')
prob.driver.declare_coloring()
mask = np.array([[1, 0, 0, 1, 1], [0, 1, 0, 1, 1], [0, 1, 0, 1, 1],
[1, 0, 0, 0, 0], [0, 1, 1, 0, 0]])
isplit = 2
sparsity = setup_sparsity(mask)
indeps, conns = setup_indeps(isplit, mask.shape[1], 'indeps', 'comp')
model.add_subsystem('indeps', indeps)
comp = model.add_subsystem(
'comp',
SparseCompExplicit(sparsity, method, isplit=isplit, osplit=2))
model.connect('indeps.x0', 'comp.x0')
model.connect('indeps.x1', 'comp.x1')
model.comp.add_objective('y0', index=1)
model.comp.add_constraint('y1', lower=[1., 2.])
model.add_design_var('indeps.x0',
indices=[0, 2],
lower=np.ones(2),
upper=np.ones(2) + .1)
model.add_design_var('indeps.x1',
lower=np.ones(2),
upper=np.ones(2) + .1)
model.approx_totals(method=method)
prob.setup(check=False, mode='fwd')
prob.set_solver_print(level=0)
prob.run_driver() # need this to trigger the dynamic coloring
prob.driver._total_jac = None
start_nruns = model._nruns
derivs = prob.driver._compute_totals() # colored
self.assertEqual(model._nruns - start_nruns, 2)
cols = [0, 2, 3, 4]
rows = [1, 3, 4]
_check_total_matrix(model, derivs, sparsity[rows, :][:, cols], method)
def test_report_generation_basic_pyoptsparse(self):
# Just to try a different driver
setup_default_reports()
prob = self.setup_and_run_simple_problem(driver=pyOptSparseDriver(
optimizer='SLSQP'))
# get the path to the problem subdirectory
problem_reports_dir = pathlib.Path(_reports_dir).joinpath(prob._name)
path = pathlib.Path(problem_reports_dir).joinpath(self.n2_filename)
self.assertTrue(path.is_file(),
f'The N2 report file, {str(path)} was not found')
path = pathlib.Path(problem_reports_dir).joinpath(
self.scaling_filename)
self.assertTrue(
path.is_file(),
f'The scaling report file, {str(path)}, was not found')
def test_reading_driver_recording_with_system_vars(self):
self.setup_sellar_grouped_model()
self.prob.driver = pyOptSparseDriver()
self.prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
self.prob.driver.opt_settings['ACC'] = 1e-9
self.prob.driver.add_recorder(self.recorder)
driver = self.prob.driver
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = [
'mda.d2.y2',
]
self.prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
self.prob.driver.opt_settings['ACC'] = 1e-9
self.prob.setup(check=False)
self.prob.run_driver()
self.prob.cleanup()
cr = CaseReader(self.filename)
# Test values from one case, the last case
last_case = cr.driver_cases.get_case(-1)
np.testing.assert_almost_equal(last_case.desvars['z'],
self.prob['pz.z'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('pz.z'))
np.testing.assert_almost_equal(last_case.desvars['x'],
self.prob['px.x'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('px.x'))
np.testing.assert_almost_equal(last_case.sysincludes['y2'],
self.prob['mda.d2.y2'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('mda.d2.y2'))
def test_simple_totals(self, method):
prob = Problem(coloring_dir=self.tempdir)
model = prob.model = CounterGroup()
prob.driver = pyOptSparseDriver(optimizer='SLSQP')
prob.driver.declare_coloring()
mask = np.array([[1, 0, 0, 1, 1], [0, 1, 0, 1, 1], [0, 1, 0, 1, 1],
[1, 0, 0, 0, 0], [0, 1, 1, 0, 0]])
isplit = 2
sparsity = setup_sparsity(mask)
indeps, conns = setup_indeps(isplit, mask.shape[1], 'indeps', 'comp')
model.add_subsystem('indeps', indeps)
comp = model.add_subsystem(
'comp',
SparseCompExplicit(sparsity, method, isplit=isplit, osplit=2))
model.connect('indeps.x0', 'comp.x0')
model.connect('indeps.x1', 'comp.x1')
model.declare_coloring('*',
method=method,
step=1e-6 if method == 'fd' else None)
model.comp.add_objective(
'y0', index=0
) # pyoptsparse SLSQP requires a scalar objective, so pick index 0
model.comp.add_constraint('y1', lower=[1., 2.])
model.add_design_var('indeps.x0',
lower=np.ones(3),
upper=np.ones(3) + .1)
model.add_design_var('indeps.x1',
lower=np.ones(2),
upper=np.ones(2) + .1)
model.approx_totals(method=method)
prob.setup(check=False, mode='fwd')
prob.set_solver_print(level=0)
prob.run_driver() # need this to trigger the dynamic coloring
prob.driver._total_jac = None
start_nruns = model._nruns
derivs = prob.compute_totals()
_check_total_matrix(model, derivs, sparsity[[0, 3, 4], :], method)
nruns = model._nruns - start_nruns
self.assertEqual(nruns, 3)
def test_reading_system_metadata(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")
self.setup_sellar_grouped_scaled_model()
self.prob.driver = pyOptSparseDriver()
self.prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
self.prob.driver.opt_settings['ACC'] = 1e-9
self.recorder.options['record_inputs'] = True
self.recorder.options['record_outputs'] = True
self.recorder.options['record_residuals'] = True
self.recorder.options['record_metadata'] = True
self.prob.model.add_recorder(self.recorder)
pz = self.prob.model.get_subsystem(
'pz') # IndepVarComp which is an ExplicitComponent
pz.add_recorder(self.recorder)
mda = self.prob.model.get_subsystem('mda') # Group
d1 = mda.get_subsystem('d1')
d1.add_recorder(self.recorder)
self.prob.setup(check=False, mode='rev')
self.prob.run_driver()
self.prob.cleanup()
cr = CaseReader(self.filename)
self.assertEqual(sorted(cr.system_metadata.keys()),
sorted(['root', 'mda.d1', 'pz']))
assert_rel_error(
self,
cr.system_metadata['pz']['output']['nonlinear']['phys'][0][1],
[2.0, 2.0], 1.0e-3)
def test_root_derivs_dict(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
self.recorder.options['record_unknowns'] = True
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()
sout = open(self.filename)
lines = sout.readlines()
self.assertEqual(lines[12].rstrip(), 'Derivatives:')
self.assertTrue(' con1 wrt x:' in lines[13])
self.assertTrue(' con1 wrt z:' in lines[14])
self.assertTrue(' con2 wrt x:' in lines[15])
self.assertTrue(' con2 wrt z:' in lines[16])
self.assertTrue(' obj wrt x:' in lines[17])
self.assertTrue(' obj wrt z:' in lines[18])
self.assertTrue('1.784' in lines[18])
def test_dido(self):
prob = Problem(root=Group(), impl=impl, driver=pyOptSparseDriver())
# Total horizontal space of area to be enclosed.
x = 100.0
# Number of segments used to enclose area.
n = 50
# Horizonal size of each segment
dx = x / n
prob.root.add(name="ys_ivc", system=IndepVarComp("ys", val=np.zeros(n), units="m"), promotes=["ys"])
prob.root.add(name="rec_group", system=RectangleGroup(n, dx))
prob.root.add(name="total_area_comp", system=Summer(n), promotes=["total_area"])
prob.root.add(name="perimeter_comp", system=PerimeterComp(n, dx), promotes=["ys", "total_perimeter"])
for i in range(n):
prob.root.connect("ys", "rec_group.section_{0}.y".format(i), src_indices=[i])
prob.root.connect("rec_group.section_{0}.area".format(i), "total_area_comp.area_{0}".format(i))
idxs = range(n)[1:-1]
prob.driver.options["optimizer"] = OPTIMIZER
prob.driver.options["print_results"] = False
prob.driver.add_desvar("ys", lower=np.zeros(n - 2), indices=idxs)
prob.driver.add_constraint("total_perimeter", upper=150)
prob.driver.add_objective("total_area", scaler=-1.0e-3)
prob.setup(check=False)
prob.run()
data = prob.check_total_derivatives(out_stream=None)
for key, val in data.items():
assert_rel_error(self, val["abs error"][0], 0.0, 1e-5)
assert_rel_error(self, val["abs error"][1], 0.0, 1e-5)
assert_rel_error(self, val["abs error"][2], 0.0, 1e-5)
assert_rel_error(self, val["rel error"][0], 0.0, 1e-5)
assert_rel_error(self, val["rel error"][1], 0.0, 1e-5)
assert_rel_error(self, val["rel error"][2], 0.0, 1e-5)
assert_rel_error(self, 3574.94, prob["total_area"], 0.1)
def test_root_derivs_dict(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
self.recorder.options["record_unknowns"] = True
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()
sout = open(self.filename)
lines = sout.readlines()
self.assertEqual(lines[12].rstrip(), "Derivatives:")
self.assertTrue(" con1 wrt x:" in lines[13])
self.assertTrue(" con1 wrt z:" in lines[14])
self.assertTrue(" con2 wrt x:" in lines[15])
self.assertTrue(" con2 wrt z:" in lines[16])
self.assertTrue(" obj wrt x:" in lines[17])
self.assertTrue(" obj wrt z:" in lines[18])
self.assertTrue("1.784" in lines[18])
def test_analysis_error_sensfunc(self):
# Component raises an analysis error during some linearize calls, and
# pyopt attempts to recover.
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', ParaboloidAE(), promotes=['*'])
root.add('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=50.0)
prob.driver.add_objective('f_xy')
prob.driver.add_constraint('c', upper=-15.0)
prob.root.comp.grad_fail_at = 2
prob.root.comp.eval_fail_at = 100
prob.setup(check=False)
prob.run()
# SLSQP does a bad job recovering from gradient failures
if OPTIMIZER == 'SLSQP':
tol = 1e-2
else:
tol = 1e-6
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'], 7.16667, tol)
assert_rel_error(self, prob['y'], -7.833334, tol)
# Normally it takes 9 iterations, but takes 12 here because of the
# gradfunc failures. (note SLSQP just doesn't do well)
if OPTIMIZER == 'SNOPT':
self.assertEqual(prob.driver.iter_count, 12)
def test_fan_out(self):
prob = Problem()
root = prob.root = Group()
root.add('p1', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('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 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.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
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', IndepVarComp('x', 1.0))
root.add('p2', IndepVarComp('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 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.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.x', lower=-50.0, upper=50.0)
prob.driver.add_objective('obj.o')
prob.driver.add_constraint('con1.c', equals=0.0)
prob.driver.add_constraint('con2.c', equals=0.0)
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_basic(self):
prob = Problem()
model = prob.model = SellarDerivativesGrouped()
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = "SLSQP"
model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
model.add_design_var('x', lower=0.0, upper=10.0)
model.add_objective('obj')
model.add_constraint('con1', upper=0.0)
model.add_constraint('con2', upper=0.0)
prob.set_solver_print(level=0)
prob.setup(check=False, mode='rev')
prob.run_driver()
assert_rel_error(self, prob['z'][0], 1.9776, 1e-3)
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_analysis_error_objfunc(self):
# Component raises an analysis error during some runs, and pyopt
# attempts to recover.
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', ParaboloidAE(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
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)
prob.run_driver()
# 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)
# Normally it takes 9 iterations, but takes 13 here because of the
# analysis failures. (note SLSQP takes 5 instead of 4)
if OPTIMIZER == 'SLSQP':
self.assertEqual(prob.driver.iter_count, 7)
else:
self.assertEqual(prob.driver.iter_count, 15)
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_run(self):
nProblems = 4
top = Problem(impl=impl)
top.root = SellarDerivativesSuperGroup(nProblems=nProblems)
top.driver = ScipyOptimizer()
top.driver = pyOptSparseDriver()
if OPTIMIZER == 'SNOPT':
top.driver.options['optimizer'] = 'SNOPT'
top.driver.opt_settings['Verify level'] = 0
top.driver.opt_settings['Print file'] = 'SNOPT_print_petsctest.out'
top.driver.opt_settings[
'Summary file'] = 'SNOPT_summary_petsctest.out'
top.driver.opt_settings['Major iterations limit'] = 1000
else:
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=0.0, 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.root.ln_solver.options['single_voi_relevance_reduction'] = True
top.setup(check=False)
# Setting initial values for design variables
top['x'] = 1.0
top['z'] = np.array([5.0, 2.0])
top.run()
if top.root.comm.rank == 0:
assert_rel_error(self, top['z'][0], 1.977639, 1.0e-6)
assert_rel_error(self, top['z'][1], 0.0, 1.0e-6)
assert_rel_error(self, top['x'], 0.0, 1.0e-6)
def test_simple_paraboloid_upper_indices(self):
prob = Problem()
model = prob.model = Group()
size = 3
model.add_subsystem('p1', IndepVarComp('x', np.array([50.0]*size)))
model.add_subsystem('p2', IndepVarComp('y', np.array([50.0]*size)))
model.add_subsystem('comp', ExecComp('f_xy = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0',
x=np.zeros(size), y=np.zeros(size),
f_xy=np.zeros(size)))
model.add_subsystem('con', ExecComp('c = - x + y',
c=np.zeros(size), x=np.zeros(size),
y=np.zeros(size)))
model.connect('p1.x', 'comp.x')
model.connect('p2.y', 'comp.y')
model.connect('p1.x', 'con.x')
model.connect('p2.y', 'con.y')
prob.set_solver_print(level=0)
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SLSQP':
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
model.add_design_var('p1.x', indices=[1], lower=-50.0, upper=50.0)
model.add_design_var('p2.y', indices=[1], lower=-50.0, upper=50.0)
model.add_objective('comp.f_xy', index=1)
model.add_constraint('con.c', indices=[1], upper=-15.0)
prob.setup(check=False)
prob.run_driver()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['p1.x'], np.array([50., 7.16667, 50.]), 1e-6)
assert_rel_error(self, prob['p2.y'], np.array([50., -7.833334, 50.]), 1e-6)
def test_raised_error_sensfunc(self):
# Component fails hard this time during gradient eval, so we expect
# pyoptsparse to raise.
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1', IndepVarComp('x', 50.0), promotes=['*'])
model.add_subsystem('p2', IndepVarComp('y', 50.0), promotes=['*'])
comp = model.add_subsystem('comp', ParaboloidAE(), promotes=['*'])
model.add_subsystem('con', ExecComp('c = - x + y'), promotes=['*'])
prob.driver = pyOptSparseDriver()
# SNOPT has a weird cleanup problem when this fails, so we use SLSQP. For the
# regular failure, it doesn't matter which opt we choose since they all fail through.
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.opt_settings['ACC'] = 1e-9
prob.driver.options['print_results'] = False
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)
comp.fail_hard = True
comp.grad_fail_at = 2
comp.eval_fail_at = 100
prob.setup(check=False)
with self.assertRaises(Exception) as err:
prob.run_driver()
# pyopt's failure message differs by platform and is not informative anyway
del prob
def test_reading_driver_recording_with_system_vars(self):
prob = SellarProblem(SellarDerivativesGrouped)
driver = prob.driver = pyOptSparseDriver(optimizer='SLSQP')
driver.options['print_results'] = False
driver.opt_settings['ACC'] = 1e-9
driver.recording_options['record_desvars'] = True
driver.recording_options['record_responses'] = True
driver.recording_options['record_objectives'] = True
driver.recording_options['record_constraints'] = True
driver.recording_options['includes'] = [
'mda.d2.y2',
]
driver.add_recorder(self.recorder)
prob.setup()
prob.run_driver()
prob.cleanup()
cr = CaseReader(self.filename)
# Test values from one case, the last case
last_case = cr.driver_cases.get_case(-1)
np.testing.assert_almost_equal(last_case.outputs['z'],
prob['pz.z'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('pz.z'))
np.testing.assert_almost_equal(last_case.outputs['x'],
prob['px.x'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('px.x'))
np.testing.assert_almost_equal(last_case.outputs['y2'],
prob['mda.d2.y2'],
err_msg='Case reader gives '
'incorrect Parameter value'
' for {0}'.format('mda.d2.y2'))
def test_pbo_desvar_nsga2(self):
if pyOptSparseDriver is None:
raise unittest.SkipTest("pyOptSparse not installed")
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', u'var_x', pass_by_obj=True))
root.add('p2', IndepVarComp('y', -4.0))
root.add('p', PassByObjParaboloid())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = pyOptSparseDriver()
top.driver.options['optimizer'] = 'NSGA2'
top.driver.add_desvar('p1.x')
top.driver.add_desvar('p2.y')
top.driver.add_objective('p.f_xy')
top.setup(check=False)
def test_sellar_mdf(self):
prob = Problem()
model = prob.model = SellarDerivativesGrouped()
prob.driver = pyOptSparseDriver()
prob.driver.options['optimizer'] = OPTIMIZER
if OPTIMIZER == 'SNOPT':
prob.driver.opt_settings['Verify level'] = 3
prob.driver.options['print_results'] = False
model.add_design_var('z', lower=np.array([-10.0, 0.0]), upper=np.array([10.0, 10.0]))
model.add_design_var('x', lower=0.0, upper=10.0)
model.add_objective('obj')
model.add_constraint('con1', upper=0.0)
model.add_constraint('con2', upper=0.0)
prob.setup(check=False, mode='rev')
prob.run_driver()
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_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 = 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', 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_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.options['optimizer'] = OPTIMIZER
prob.driver.options['print_results'] = False
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('y', lower=-50.0, upper=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)