def test_gutmann_branin_noisy_with_init(self): """Check solution of noisy braning with Gutmann, solver.""" bb = ti.TestNoisyBlackBox('branin', 0.1, 0.01) optimum = bb._function.optimum_value for seed in self.rand_seeds: print() print('Solving branin with random seed ' + '{:d}'.format(seed)) settings = RbfSettings(algorithm='Gutmann', global_search_method='solver', target_objval=optimum, eps_opt=self.eps_opt, max_iterations=200, max_evaluations=300, fast_objfun_rel_error=0.1, fast_objfun_abs_error=0.01, rand_seed=seed) init_node_pos = [[0, 0], [-2, 2], [5, 10]] init_node_val = [bb._function.evaluate(x) for x in init_node_pos] alg = ra.OptAlgorithm(settings, bb, init_node_pos, init_node_val) res = alg.optimize() msg = ('Could not solve noisy branin with init and ' + 'Gutmann\'s algorithm') target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) > settings.eps_zero else self.eps_opt) self.assertLessEqual(res[0], target, msg=msg)
def test_gutmann_st_miqp3_noisy(self): """Check solution of noisy st_miqp3 with Gutmann, genetic.""" bb = ti.TestNoisyBlackBox('st_miqp3', 0.1, 0.01) optimum = bb._function.optimum_value for seed in self.rand_seeds: print() print('Solving st_miqp3 with random seed ' + '{:d}'.format(seed)) settings = RbfSettings(algorithm='Gutmann', global_search_method='genetic', target_objval=optimum, eps_opt=self.eps_opt, max_iterations=200, max_evaluations=300, fast_objfun_rel_error=0.1, fast_objfun_abs_error=0.01, rand_seed=seed) alg = ra.OptAlgorithm(settings, bb) res = alg.optimize() msg = 'Could not solve st_miqp3 with Gutmann\'s algorithm' target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) > settings.eps_zero else self.eps_opt) self.assertLessEqual(res[0], target, msg=msg)
def test_msrsm_parallel_prob03_noisy(self): """Check solution of noisy prob03 with MSRSM, sampling.""" bb = ti.TestNoisyBlackBox('prob03', 0.1, 0.01) optimum = bb._function.optimum_value for seed in self.rand_seeds: print() print('Solving prob03 with random seed ' + '{:d}'.format(seed)) settings = RbfSettings(algorithm='MSRSM', global_search_method='sampling', target_objval=optimum, eps_opt=self.eps_opt, max_iterations=200, max_evaluations=300, num_cpus=4, fast_objfun_rel_error=0.1, fast_objfun_abs_error=0.01, rand_seed=seed) alg = ra.OptAlgorithm(settings, bb) res = alg.optimize() msg = 'Could not solve prob03 with MSRSM algorithm' target = optimum + (abs(optimum) * self.eps_opt if abs(optimum) > settings.eps_zero else self.eps_opt) self.assertLessEqual(res[0], target, msg=msg)