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)