def test_opt_cylinder(self):
# this is just here to make sure that the cylinder optimization works normally so
# we know if the opt of the nested cylinder fails it's not due to some cylinder
# model issue.
prob = Problem(root=CylinderGroup())
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['disp'] = False
prob.driver.add_desvar("indep.r", lower=0.0, upper=1.e99)
prob.driver.add_desvar("indep.h", lower=0.0, upper=1.e99)
prob.driver.add_objective("cylinder.area")
prob.driver.add_constraint("cylinder.volume", equals=1.5)
prob.setup(check=False)
prob.run()
for name, opt in cylinder_opts:
self.assertAlmostEqual(prob[name], opt,
places=4,
msg="%s should be %s, but got %s" %
(name, opt, prob[name]))
self.assertAlmostEqual(prob['cylinder.volume'], 1.5,
places=4,
msg="volume should be 1.5, but got %s" %
prob['cylinder.volume'])
def test_simple_paraboloid_scaled_objective_rev(self):
prob = Problem()
model = prob.model = Group()
prob.set_solver_print(level=0)
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=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = 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', ref=10.)
model.add_constraint('c', lower=10.0, upper=11.0)
prob.setup(check=False, mode='rev')
prob.run_driver()
# Minimum should be at (7.166667, -7.833334)
assert_rel_error(self, prob['x'] - prob['y'], 11.0, 1e-6)
def test_unconstrainted(self):
from openmdao.api import Problem, ScipyOptimizer, IndepVarComp
# We'll use the component that was defined in the last tutorial
from openmdao.test_suite.components.paraboloid import Paraboloid
# build the model
prob = Problem()
indeps = prob.model.add_subsystem('indeps', IndepVarComp())
indeps.add_output('x', 3.0)
indeps.add_output('y', -4.0)
prob.model.add_subsystem('paraboloid', Paraboloid())
prob.model.connect('indeps.x', 'paraboloid.x')
prob.model.connect('indeps.y', 'paraboloid.y')
# setup the optimization
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'COBYLA'
prob.model.add_design_var('indeps.x', lower=-50, upper=50)
prob.model.add_design_var('indeps.y', lower=-50, upper=50)
prob.model.add_objective('paraboloid.f_xy')
prob.setup()
prob.run_driver()
# minimum value
assert_rel_error(self, prob['paraboloid.f_xy'], -27.33333, 1e-6)
# location of the minimum
assert_rel_error(self, prob['indeps.x'], 6.6667, 1e-4)
assert_rel_error(self, prob['indeps.y'], -7.33333, 1e-4)
def test_root_derivs_array(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()
sout = open(self.filename)
lines = sout.readlines()
self.assertEqual(lines[14].rstrip(), 'Derivatives:')
self.assertTrue('9.61' in lines[15])
self.assertTrue('0.784' in lines[16])
self.assertTrue('1.077' in lines[17])
def test_simple_paraboloid_equality(self):
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=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = 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', equals=-15.0)
prob.setup(check=False)
prob.run_driver()
# Minimum should be at (7.166667, -7.833334)
# (Note, loose tol because of appveyor py3.4 machine.)
assert_rel_error(self, prob['x'], 7.16667, 1e-4)
assert_rel_error(self, prob['y'], -7.833334, 1e-4)
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.options['disp'] = 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 test_feature_basic(self):
from openmdao.api import Problem, Group, IndepVarComp, ScipyOptimizer
from openmdao.test_suite.components.paraboloid import Paraboloid
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=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
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')
prob.setup()
prob.run_driver()
assert_rel_error(self, prob['x'], 6.66666667, 1e-6)
assert_rel_error(self, prob['y'], -7.3333333, 1e-6)
def test_sellar_state_connection(self):
top = Problem()
top.root = SellarStateConnection()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-8
top.driver.options['disp'] = False
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.setup(check=False)
top.run()
assert_rel_error(self, top['z'][0], 1.977639, 1e-5)
assert_rel_error(self, top['z'][1], 0.0, 1e-5)
assert_rel_error(self, top['x'], 0.0, 1e-5)
assert_rel_error(self, top['obj'], 3.1833940, 1e-5)
def test_sellar_sand_architecture(self):
top = Problem()
top.root = SellarSAND()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-12
top.driver.options['disp'] = False
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_desvar('y1', lower=-10.0, upper=10.0)
top.driver.add_desvar('y2', lower=-10.0, 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.driver.add_constraint('resid1', equals=0.0)
top.driver.add_constraint('resid2', equals=0.0)
top.setup(check=False)
top.run()
assert_rel_error(self, top['z'][0], 1.9776, 1e-3)
assert_rel_error(self, top['z'][1], 0.0000, 1e-3)
assert_rel_error(self, top['x'], 0.0000, 1e-3)
assert_rel_error(self, top['d1.y1'], 3.1600, 1e-3)
assert_rel_error(self, top['d1.y2'], 3.7553, 1e-3)
assert_rel_error(self, top['obj'], 3.1834, 1e-3)
def test_beam_tutorial(self):
top = Problem()
top.root = BeamTutorial()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-8
top.driver.options['maxiter'] = 10000 #maximum number of solver iterations
top.driver.options['disp'] = False
#room length and width bounds
top.driver.add_desvar('ivc_rlength.room_length', lower=5.0*12.0, upper=50.0*12.0) #domain: 1in <= length <= 50ft
top.driver.add_desvar('ivc_rwidth.room_width', lower=5.0*12.0, upper=30.0*12.0) #domain: 1in <= width <= 30ft
top.driver.add_objective('d_neg_area.neg_room_area') #minimize negative area (or maximize area)
top.driver.add_constraint('d_len_minus_wid.length_minus_width', lower=0.0) #room_length >= room_width
top.driver.add_constraint('d_deflection.deflection', lower=720.0) #deflection >= 720
top.driver.add_constraint('d_bending.bending_stress_ratio', upper=0.5) #bending < 0.5
top.driver.add_constraint('d_shear.shear_stress_ratio', upper=1.0/3.0) #shear < 1/3
top.setup(check=False)
top.run()
assert_rel_error(self, -top['d_neg_area.neg_room_area'], 51655.257618, .01)
assert_rel_error(self, top['ivc_rwidth.room_width'], 227.277956, .01)
assert_rel_error(self,top['ivc_rlength.room_length'], 227.277904, .01)
assert_rel_error(self,top['d_deflection.deflection'], 720, .01)
assert_rel_error(self,top['d_bending.bending_stress_ratio'], 0.148863, .001)
assert_rel_error(self,top['d_shear.shear_stress_ratio'], 0.007985, .0001)
def test_paraboloid_optimize_unconstrained(self):
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', 3.0))
root.add('p2', IndepVarComp('y', -4.0))
root.add('p', ParaboloidOptUnCon())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['disp'] = False
top.driver.add_desvar('p1.x', lower=-50, upper=50)
top.driver.add_desvar('p2.y', lower=-50, upper=50)
top.driver.add_objective('p.f_xy')
top.setup(check=False)
top.run()
assert_rel_error(self, top['p.x'], 6.666667, 1e-6)
assert_rel_error(self, top['p.y'], -7.333333, 1e-6)
def test_scipy_optimizer_simple_paraboloid_unconstrained(self):
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=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer(optimizer='SLSQP', tol=1e-9, disp=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')
prob.setup(check=False)
failed = prob.run_driver()
self.assertFalse(
failed,
"Optimization failed, result =\n" + str(prob.driver.result))
assert_rel_error(self, prob['x'], 6.66666667, 1e-6)
assert_rel_error(self, prob['y'], -7.3333333, 1e-6)
def main():
print("Bayesopt OpenMDAO Optimizer example")
top = Problem()
root = top.root = Group()
# Initial value of x and y set in the IndepVarComp.
root.add('p1', IndepVarComp('x', 13.0))
root.add('p2', IndepVarComp('y', -14.0))
root.add('p', Paraboloid())
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'COBYLA'
top.driver.add_desvar('p1.x', lower=-50, upper=50)
top.driver.add_desvar('p2.y', lower=-50, upper=50)
top.driver.add_objective('p.f_xy')
top.setup()
# You can also specify initial values post-setup
top['p1.x'] = 3.0
top['p2.y'] = -4.0
top.run()
print('\n')
print('Minimum of %f found at (%f, %f)' % (top['p.f_xy'], top['p.x'], top['p.y']))
def cobyla_optimize(iteration_count, dimensions):
top = Problem()
root = top.root = Group()
root.add('p', RosenbrockMultiDim(dimensions))
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'COBYLA'
top.driver.options['maxiter'] = iteration_count
top.driver.add_objective('p.f')
for i in range(dimensions):
componentName = 'p{0}'.format(i)
variableName = 'x{0}'.format(i)
portName = '{0}.{1}'.format(componentName, variableName)
root.add(componentName, IndepVarComp(variableName, 0.0))
root.connect(portName, 'p.{0}'.format(variableName))
top.driver.add_desvar(portName, lower=-50, upper=50)
top.setup()
top.run()
result_x = []
for i in range(dimensions):
result_x.append(top['p.x{0}'.format(i)])
print('\n')
print('Minimum of {0} found at {1}'.format(top['p.f'], result_x))
return top['p.f']
def test_beam_tutorial_viewtree(self):
top = Problem()
top.root = BeamTutorial()
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['tol'] = 1.0e-8
top.driver.options['maxiter'] = 10000 #maximum number of solver iterations
top.driver.options['disp'] = False
#room length and width bounds
top.driver.add_desvar('ivc_rlength.room_length', lower=5.0*12.0, upper=50.0*12.0) #domain: 1in <= length <= 50ft
top.driver.add_desvar('ivc_rwidth.room_width', lower=5.0*12.0, upper=30.0*12.0) #domain: 1in <= width <= 30ft
top.driver.add_objective('d_neg_area.neg_room_area') #minimize negative area (or maximize area)
top.driver.add_constraint('d_len_minus_wid.length_minus_width', lower=0.0) #room_length >= room_width
top.driver.add_constraint('d_deflection.deflection', lower=720.0) #deflection >= 720
top.driver.add_constraint('d_bending.bending_stress_ratio', upper=0.5) #bending < 0.5
top.driver.add_constraint('d_shear.shear_stress_ratio', upper=1.0/3.0) #shear < 1/3
top.setup(check=False)
from openmdao.api import view_tree
view_tree(top, show_browser=False)
import os.path
self.assertTrue(os.path.isfile('partition_tree_n2.html'))
os.remove('partition_tree_n2.html')
def test_paraboloid_optimize_constrained_explicit_infinite_bounds(self):
top = Problem()
root = top.root = Group()
root.add('p1', IndepVarComp('x', 3.0))
root.add('p2', IndepVarComp('y', -4.0))
root.add('p', ParaboloidOptCon())
# Constraint Equation
root.add('con', ExecComp('c = x-y'))
root.connect('p1.x', 'p.x')
root.connect('p2.y', 'p.y')
root.connect('p.x', 'con.x')
root.connect('p.y', 'con.y')
top.driver = ScipyOptimizer()
top.driver.options['optimizer'] = 'SLSQP'
top.driver.options['disp'] = False
top.driver.add_desvar('p1.x', lower=-inf_bound, upper=50)
top.driver.add_desvar('p2.y', lower=-50, upper=inf_bound)
top.driver.add_objective('p.f_xy')
top.driver.add_constraint('con.c', lower=15.0, upper=inf_bound)
top.setup(check=False)
top.run()
assert_rel_error(self, top['p.x'], 7.166667, 1e-6)
assert_rel_error(self, top['p.y'], -7.833333, 1e-6)
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',
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.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_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', 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.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_unsupported_equality(self):
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=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'COBYLA'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = 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', equals=-15.0)
prob.setup(check=False)
with self.assertRaises(Exception) as raises_cm:
prob.run_driver()
exception = raises_cm.exception
msg = "Constraints of type 'eq' not handled by COBYLA."
self.assertEqual(exception.args[0], msg)
def test_simple_array_comp2D(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 = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('x', lower=-50.0, upper=50.0)
prob.driver.add_objective('o')
prob.driver.add_constraint('c', equals=0.0)
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_driver_records_unknown_types_metadata(self):
prob = Problem()
root = prob.root = Group()
# Need an optimization problem to test to make sure
# the is_desvar, is_con, is_obj metadata is being
# recorded for the Unknowns
root.add('p1', IndepVarComp('x', 50.0))
root.add('p2', IndepVarComp('y', 50.0))
root.add('comp', Paraboloid())
root.connect('p1.x', 'comp.x')
root.connect('p2.y', 'comp.y')
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('p1.x', lower=-50.0, upper=50.0)
prob.driver.add_desvar('p2.y', lower=-50.0, upper=50.0)
prob.driver.add_objective('comp.f_xy')
prob.driver.options['disp'] = False
prob.driver.add_recorder(self.recorder)
self.recorder.options['record_metadata'] = True
prob.setup(check=False)
prob.cleanup() # close recorders
expected_params = list(iteritems(prob.root.params))
expected_unknowns = list(iteritems(prob.root.unknowns))
expected_resids = list(iteritems(prob.root.resids))
self.assertMetadataRecorded(
(expected_params, expected_unknowns, expected_resids))
def test_Sellar_SLSQP(self):
prob = Problem()
prob.root = SellarDerivatives()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
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.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_paraboloid_unconstrained(self):
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=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = 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')
prob.setup(check=False)
prob.run_driver()
assert_rel_error(self, prob['x'], 6.66666667, 1e-6)
assert_rel_error(self, prob['y'], -7.3333333, 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',
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] = 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_simple_array_comp2D_dbl_sided_con_array(self):
prob = Problem()
model = prob.model = Group()
model.add_subsystem('p1',
IndepVarComp('widths', np.zeros((2, 2))),
promotes=['*'])
model.add_subsystem('comp', TestExplCompArrayDense(), promotes=['*'])
model.add_subsystem('obj',
ExecComp('o = areas[0, 0]', areas=np.zeros(
(2, 2))),
promotes=['*'])
prob.set_solver_print(level=0)
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = False
model.add_design_var('widths', lower=-50.0, upper=50.0)
model.add_objective('o')
model.add_constraint('areas', lower=20.0, upper=20.0)
prob.setup(check=False)
prob.run_driver()
obj = prob['o']
assert_rel_error(self, obj, 20.0, 1e-6)
def test_simple_paraboloid_scaled_constraint_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 = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1.0e-8
prob.driver.options['disp'] = 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, scaler=1 / 10.)
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(self):
import numpy as np
from openmdao.api import Problem, ScipyOptimizer
from openmdao.test_suite.test_examples.beam_optimization.beam_group import BeamGroup
E = 1.
L = 1.
b = 0.1
volume = 0.01
num_elements = 50
prob = Problem(model=BeamGroup(E=E, L=L, b=b, volume=volume, num_elements=num_elements))
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
prob.driver.options['disp'] = True
prob.setup()
prob.run_driver()
print(prob['inputs_comp.h'])
def test_feature_run_driver(self):
import numpy as np
from openmdao.api import Problem, NonlinearBlockGS, ScipyOptimizer
from openmdao.test_suite.components.sellar import SellarDerivatives
prob = Problem()
model = prob.model = SellarDerivatives()
model.nonlinear_solver = NonlinearBlockGS()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.options['tol'] = 1e-9
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()
prob.run_driver()
assert_rel_error(self, prob['x'], 0.0, 1e-5)
assert_rel_error(self, prob['y1'], 3.160000, 1e-2)
assert_rel_error(self, prob['y2'], 3.755278, 1e-2)
assert_rel_error(self, prob['z'], [1.977639, 0.000000], 1e-2)
assert_rel_error(self, prob['obj'], 3.18339395, 1e-2)
def test_sellar_opt(self):
from openmdao.api import Problem, ScipyOptimizer, ExecComp, IndepVarComp, DirectSolver
from openmdao.test_suite.components.sellar_feature import SellarMDA
prob = Problem()
prob.model = SellarMDA()
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
# prob.driver.options['maxiter'] = 100
prob.driver.options['tol'] = 1e-8
prob.model.add_design_var('x', lower=0, upper=10)
prob.model.add_design_var('z', lower=0, upper=10)
prob.model.add_objective('obj')
prob.model.add_constraint('con1', upper=0)
prob.model.add_constraint('con2', upper=0)
prob.setup()
prob.set_solver_print(level=0)
# Ask OpenMDAO to finite-difference across the model to compute the gradients for the optimizer
prob.model.approx_totals()
prob.run_driver()
print('minimum found at')
assert_rel_error(self, prob['x'][0], 0., 1e-5)
assert_rel_error(self, prob['z'], [1.977639, 0.], 1e-5)
print('minumum objective')
assert_rel_error(self, prob['obj'][0], 3.18339395045, 1e-5)
def test_pbo_desvar_slsqp_scipy(self):
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 = ScipyOptimizer()
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")
