def test_specify_solver(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.nonlinear_solver = om.NonlinearBlockGS()
model.linear_solver = om.LinearBlockJac()
prob.setup()
prob.run_model()
wrt = ['z']
of = ['obj']
J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')
assert_near_equal(J['obj', 'z'][0][0], 9.61001056, .00001)
assert_near_equal(J['obj', 'z'][0][1], 1.78448534, .00001)
def test_globaljac_err(self):
prob = om.Problem()
model = prob.model = om.Group(assembled_jac_type='dense')
model.add_subsystem('x_param', om.IndepVarComp('length', 3.0),
promotes=['length'])
model.add_subsystem('mycomp', TestExplCompSimpleDense(),
promotes=['length', 'width', 'area'])
model.linear_solver = om.LinearBlockJac(assemble_jac=True)
prob.setup()
with self.assertRaises(RuntimeError) as context:
prob.run_model()
self.assertEqual(str(context.exception),
"Linear solver 'LN: LNBJ' doesn't support assembled jacobians.")
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.nonlinear_solver = om.NonlinearBlockGS()
model.linear_solver = om.LinearBlockJac()
model.linear_solver.options['maxiter'] = 5
prob.setup()
prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))
prob.run_model()
wrt = ['z']
of = ['obj']
J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')
assert_near_equal(J['obj', 'z'][0][0], 9.60230118004, .00001)
assert_near_equal(J['obj', 'z'][0][1], 1.78022500547, .00001)