def test_history(self):
x0 = -np.array([1.3, 2.7])
opt = LBFGS(self.oracle,
x0,
memory_size=10,
line_search_options={
'method': 'Constant',
'c': 1.0
},
tolerance=1e-6)
opt.run(10)
x_min = opt.hist['x_star']
func_steps = [
25.635000000000005, 22.99, -9.3476294733722725,
-9.4641732176886055, -9.5
]
grad_norm_steps = [
11.629703349613008, 11.4, 0.55751193505619512, 0.26830541958992876,
0.0
]
time_steps = [0.0] * 5 # Dummy values
x_steps = [
np.array([-1.3, -2.7]),
np.array([1.0, 8.7]),
np.array([0.45349973, 3.05512941]),
np.array([0.73294321, 3.01292737]),
np.array([0.99999642, 2.99998814])
]
true_history = dict(grad_norm=grad_norm_steps,
time=time_steps,
x=x_steps,
func=func_steps)
check_equal_histories(opt.hist, true_history)
def test_quality(self):
opt = LBFGS(self.oracle, self.x0, tolerance=1e-5)
opt.run(5)
x_min = opt.hist['x_star']
# x_min, message, _ = LBFGS(self.oracle, self.x0, tolerance=1e-5)
f_min = self.oracle.func(x_min)
g_k_norm_sqr = norm(self.A.dot(x_min) - self.b, 2)**2
g_0_norm_sqr = norm(self.A.dot(self.x0) - self.b, 2)**2
self.assertLessEqual(g_k_norm_sqr, 1e-5 * g_0_norm_sqr)
self.assertLessEqual(abs(f_min - self.f_star), 1e-5 * g_0_norm_sqr)
def test_line_search_options(self):
"""Check if argument `line_search_options` is supported."""
LBFGS(self.oracle,
self.x0,
line_search_options={
'method': 'Wolfe',
'c1': 1e-4,
'c2': 0.9
})
def test_memory_size(self):
"""Check if argument `memory_size` is supported."""
LBFGS(self.oracle, self.x0, memory_size=1)
def test_max_iter(self):
"""Check if argument `max_iter` is supported."""
opt = LBFGS(self.oracle, self.x0)
opt.run(max_iter=0)
def test_tolerance(self):
"""Check if argument `tolerance` is supported."""
LBFGS(self.oracle, self.x0, tolerance=1e-5)
def test_default(self):
"""Check if everything works correctly with default parameters."""
opt = LBFGS(self.oracle, self.x0)
opt.run()