def test_hierarchy_iprint3(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NonlinearBlockJac()
sub1.nonlinear_solver = om.NonlinearBlockJac()
sub2.nonlinear_solver = om.NonlinearBlockJac()
g1.nonlinear_solver = om.NonlinearBlockJac()
g2.nonlinear_solver = om.NonlinearBlockJac()
prob.set_solver_print(level=2)
prob.setup()
output = run_model(prob)
def test_hierarchy_iprint3(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('pz', om.IndepVarComp('z', np.array([5.0, 2.0])))
sub1 = model.add_subsystem('sub1', om.Group())
sub2 = sub1.add_subsystem('sub2', om.Group())
g1 = sub2.add_subsystem('g1', SubSellar())
g2 = model.add_subsystem('g2', SubSellar())
model.connect('pz.z', 'sub1.sub2.g1.z')
model.connect('sub1.sub2.g1.y2', 'g2.x')
model.connect('g2.y2', 'sub1.sub2.g1.x')
model.nonlinear_solver = om.NonlinearBlockJac()
sub1.nonlinear_solver = om.NonlinearBlockJac()
sub2.nonlinear_solver = om.NonlinearBlockJac()
g1.nonlinear_solver = om.NonlinearBlockJac()
g2.nonlinear_solver = om.NonlinearBlockJac()
prob.set_solver_print(level=2)
prob.setup()
output = run_model(prob)
# Check that certain things show up in our outputs
self.assertGreaterEqual(output.count('sub1'), 2)
self.assertGreaterEqual(output.count('sub1.sub2'), 2)
self.assertGreaterEqual(output.count('sub1.sub2.g1'), 2)
self.assertGreaterEqual(output.count('g2'), 2)
self.assertGreaterEqual(output.count('NL: NLBJ'), 2)
def test_reraise_analylsis_error(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('p1', om.IndepVarComp('x', 0.5))
model.add_subsystem('p2', om.IndepVarComp('x', 3.0))
sub = model.add_subsystem('sub', om.ParallelGroup())
sub.add_subsystem('c1', AEComp())
sub.add_subsystem('c2', AEComp())
sub.nonlinear_solver = om.NonlinearBlockJac()
model.add_subsystem('obj', om.ExecComp(['val = x1 + x2']))
model.connect('p1.x', 'sub.c1.x')
model.connect('p2.x', 'sub.c2.x')
model.connect('sub.c1.y', 'obj.x1')
model.connect('sub.c2.y', 'obj.x2')
prob.driver = AEDriver()
prob.setup()
handled = prob.run_driver()
self.assertTrue(handled)
def test_feature_rtol(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives
prob = om.Problem()
model = prob.model
model.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])
model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]), x=0.0),
promotes=['obj', 'x', 'z', 'y1', 'y2'])
model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])
model.linear_solver = om.LinearBlockGS()
nlbgs = model.nonlinear_solver = om.NonlinearBlockJac()
nlbgs.options['rtol'] = 1e-3
prob.setup()
prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))
prob.run_model()
assert_near_equal(prob.get_val('y1'), 25.5891491526, .00001)
assert_near_equal(prob.get_val('y2'), 12.0569142166, .00001)
def test_feature_maxiter(self):
import numpy as np
import openmdao.api as om
from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives
prob = om.Problem()
model = prob.model
model.add_subsystem('px', om.IndepVarComp('x', 1.0), promotes=['x'])
model.add_subsystem('pz',
om.IndepVarComp('z', np.array([5.0, 2.0])),
promotes=['z'])
model.add_subsystem('d1',
SellarDis1withDerivatives(),
promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2',
SellarDis2withDerivatives(),
promotes=['z', 'y1', 'y2'])
model.add_subsystem('obj_cmp',
om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]),
x=0.0),
promotes=['obj', 'x', 'z', 'y1', 'y2'])
model.add_subsystem('con_cmp1',
om.ExecComp('con1 = 3.16 - y1'),
promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2',
om.ExecComp('con2 = y2 - 24.0'),
promotes=['con2', 'y2'])
model.linear_solver = om.LinearBlockGS()
nlbgs = model.nonlinear_solver = om.NonlinearBlockJac()
nlbgs.options['maxiter'] = 4
prob.setup()
prob.run_model()
assert_rel_error(self, prob['y1'], 25.5723813937, .00001)
assert_rel_error(self, prob['y2'], 12.0542542372, .00001)
def test_feature_maxiter(self):
prob = om.Problem()
model = prob.model
model.add_subsystem('d1',
SellarDis1withDerivatives(),
promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2',
SellarDis2withDerivatives(),
promotes=['z', 'y1', 'y2'])
model.add_subsystem('obj_cmp',
om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
z=np.array([0.0, 0.0]),
x=0.0),
promotes=['obj', 'x', 'z', 'y1', 'y2'])
model.add_subsystem('con_cmp1',
om.ExecComp('con1 = 3.16 - y1'),
promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2',
om.ExecComp('con2 = y2 - 24.0'),
promotes=['con2', 'y2'])
model.linear_solver = om.LinearBlockGS()
nlbgs = model.nonlinear_solver = om.NonlinearBlockJac()
nlbgs.options['maxiter'] = 4
prob.setup()
prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))
prob.run_model()
assert_near_equal(prob['y1'], 25.5723813937, .00001)
assert_near_equal(prob['y2'], 12.0542542372, .00001)
prob.model.linear_solver = om.DirectSolver()
elif solver_flag == 'nlbgs':
# The nonlinear block Gauss-Seidel solver is an iterative solvver
# Requires no linear solver and works even without derivatives
prob.model.nonlinear_solver = om.NonlinearBlockGS(iprint=2)
prob.model.nonlinear_solver.options['maxiter'] = 400
prob.model.nonlinear_solver.options['atol'] = 1e-8
prob.model.nonlinear_solver.options['rtol'] = 1e-8
# The Aitken relaxation method improves robustness at cost of some speed
prob.model.nonlinear_solver.options['use_aitken'] = False
prob.model.nonlinear_solver.options['use_apply_nonlinear'] = True
elif solver_flag == 'nlbjac':
# We don't usually recommend using nonlinear block Jacobi as it converges slower
prob.model.nonlinear_solver = om.NonlinearBlockJac(iprint=2)
prob.model.nonlinear_solver.options['maxiter'] = 400
prob.model.nonlinear_solver.options['atol'] = 1e-8
prob.model.nonlinear_solver.options['rtol'] = 1e-8
else:
raise ValueError("bad solver selection!")
prob.setup()
### If using the Newton solver you should generally check your partial derivatives
### before you run the model. It won't converge if you made a mistake.
# prob.check_partials(compact_print=True)
prob.run_model()
### If you want to list all inputs and outputs, uncomment the following
