def test_two_parameter_maximization(self):
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0,
pgtol=1.0e-11)
fom = lambda params: np.sin(np.pi * params[0]) * np.sin(np.pi * params[
1])
jac = lambda params: np.pi * np.array([
np.cos(np.pi * params[0]) * np.sin(np.pi * params[1]),
np.sin(np.pi * params[0]) * np.cos(np.pi * params[1])
])
start = np.array([1.0e-5, 1.0 - 1e-5])
bounds = np.array([(self.machine_eps, 1.0 - self.machine_eps),
(self.machine_eps, 1.0 - self.machine_eps)])
def plot_fun():
pass
optimizer.initialize(start_params=start,
callable_fom=fom,
callable_jac=jac,
bounds=bounds,
plotting_function=plot_fun)
results = optimizer.run()
self.assertTrue(results.success)
self.assertAlmostEqual(results.x.size, 2)
self.assertAlmostEqual(results.x[0], 0.5, 11)
self.assertAlmostEqual(results.x[1], 0.5, 11)
self.assertAlmostEqual(results.fun, 1.0, 11)
self.assertLessEqual(results.nit, 3)
self.assertLessEqual(results.nfev, 15)
def test_single_parameter_maximization_with_scaling(self):
span = 1.0e-9
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0 / span,
pgtol=1.0e-11)
fom = lambda param: -1.0 / np.sin(np.pi / span * param[0])
jac = lambda param: np.pi * np.cos(np.pi / span * param[0]) / np.power(
np.sin(np.pi / span * param[0]), 2) / span
start = np.array(1.0e-6) * span
bounds = np.array([(self.machine_eps, 1.0 - self.machine_eps)]) * span
def plot_fun():
pass
optimizer.initialize(start_params=start,
callable_fom=fom,
callable_jac=jac,
bounds=bounds,
plotting_function=plot_fun)
results = optimizer.run()
self.assertTrue(results.success)
self.assertAlmostEqual(results.x.size, 1)
self.assertAlmostEqual(results.x[0], 0.5 * span, 11)
self.assertAlmostEqual(results.fun, -1.0, 11)
self.assertLessEqual(results.nit, 31)
self.assertLessEqual(results.nfev, 46)