def test_converge_diverge_par(self):
prob = Problem(impl=impl)
prob.root = ConvergeDivergePar()
prob.root.ln_solver = PetscKSP()
prob.setup(check=False)
prob.run()
# Make sure value is fine.
assert_rel_error(self, prob['comp7.y1'], -102.7, 1e-6)
indep_list = ['p.x']
unknown_list = ['comp7.y1']
J = prob.calc_gradient(indep_list,
unknown_list,
mode='fwd',
return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
J = prob.calc_gradient(indep_list,
unknown_list,
mode='rev',
return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
J = prob.calc_gradient(indep_list,
unknown_list,
mode='fd',
return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
def test_converge_diverge_compfd(self):
prob = Problem(impl=impl)
prob.root = ConvergeDivergePar()
prob.root.ln_solver = PetscKSP()
# fd comp2 and comp5. each is under a par group
prob.root.par1.comp2.fd_options['force_fd'] = True
prob.root.par2.comp5.fd_options['force_fd'] = True
prob.setup(check=False)
prob.run()
# Make sure value is fine.
assert_rel_error(self, prob['comp7.y1'], -102.7, 1e-6)
indep_list = ['p.x']
unknown_list = ['comp7.y1']
J = prob.calc_gradient(indep_list, unknown_list, mode='fwd', return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
J = prob.calc_gradient(indep_list, unknown_list, mode='rev', return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)
def test_with_relevance_rev(self):
prob = Problem()
prob.root = ConvergeDivergePar()
prob.root.ln_solver = LinearGaussSeidel()
prob.driver.add_desvar('p.x')
prob.driver.add_objective('comp7.y1')
prob.root.ln_solver.options['mode'] = 'rev'
prob.root.ln_solver.options['single_voi_relevance_reduction'] = True
prob.setup(check=False)
prob.run()
data = prob.check_total_derivatives(out_stream=None)
for key, val in iteritems(data):
assert_rel_error(self, val['abs error'][1], 0.0, 1e-5)
assert_rel_error(self, val['rel error'][1], 0.0, 1e-5)
def test_converge_diverge_comp_cs(self):
prob = Problem(impl=impl)
prob.root = ConvergeDivergePar()
prob.root.ln_solver = PetscKSP()
# fd the whole model
prob.root.fd_options['force_fd'] = True
prob.root.fd_options['form'] = 'complex_step'
prob.setup(check=False)
prob.run()
# Make sure value is fine.
assert_rel_error(self, prob['comp7.y1'], -102.7, 1e-6)
indep_list = ['p.x']
unknown_list = ['comp7.y1']
J = prob.calc_gradient(indep_list, unknown_list, mode='fd', return_format='dict')
assert_rel_error(self, J['comp7.y1']['p.x'][0][0], -40.75, 1e-6)