def golden_search(loss_function: rosenbrock,
start: point,
direction: list,
epsilon=0.1) -> float:
"""
derivative-free to search the longest step
:param loss_function:
:param start:
:param direction:
:param epsilon:
:return:
"""
a, b = advance_retreat_method(loss_function, start, direction)
# find the minimum
golden_num = (math.sqrt(5) - 1) / 2
p, q = a + (1 - golden_num) * (b - a), a + golden_num * (b - a)
while abs(a - b) > epsilon:
f_p = loss_function.f(start +
point(direction[0] * p, direction[1] * p))
f_q = loss_function.f(start +
point(direction[0] * q, direction[1] * q))
if f_p < f_q:
b, q = q, p
p = a + (1 - golden_num) * (b - a)
else:
a, p = p, q
q = a + golden_num * (b - a)
return (a + b) / 2
def dichotomous_search(loss_function: rosenbrock,
start: point,
direction: list,
epsilon=0.1) -> float:
"""
derivative-free to search the longest step
:param loss_function:
:param start:
:param direction:
:param epsilon:
:return:
"""
a, b = advance_retreat_method(loss_function, start, direction)
# find the minimum
e = epsilon / 3
p, q = (a + b) / 2 - e, (a + b) / 2 + e
while abs(a - b) > epsilon:
f_p = loss_function.f(start +
point(direction[0] * p, direction[1] * p))
f_q = loss_function.f(start +
point(direction[0] * q, direction[1] * q))
if f_p < f_q:
b = q
else:
a = p
p, q = (a + b) / 2 - e, (a + b) / 2 + e
return (a + b) / 2
def armijo_goldstein_search(loss_function: rosenbrock,
start: point,
direction: list,
rho=0.1) -> float:
"""
:param loss_function:
:param start:
:param direction:
:param rho: meet condition 0<rho<0.5
:return:
"""
a, b = 0, 100
alpha = b * random.uniform(0.5, 1)
# find the alpha
f1 = loss_function.f(start)
gradient = loss_function.g(start)
gradient_f1 = np.dot(gradient.T, np.array(direction))
while True:
f2 = loss_function.f(start +
point(direction[0] * alpha, direction[1] * alpha))
# print(f2 - f1, alpha)
# print(rho * alpha * gradient_f1, (1 - rho) * alpha * gradient_f1)
# the armijo goldstein rule
# if alpha < 1: return 0.1
if f2 - f1 <= rho * alpha * gradient_f1:
if f2 - f1 >= (1 - rho) * alpha * gradient_f1:
print(alpha)
return alpha
else:
a, b = alpha, b
if b < alpha:
alpha = (a + b) / 2
else:
alpha = 2 * alpha
else:
a, b = a, alpha
alpha = (a + b) / 2
def fibonacci_search(loss_function: rosenbrock,
start: point,
direction: list,
epsilon=1) -> float:
"""
derivative-free to search the longest step
:param loss_function:
:param start:
:param direction:
:param epsilon:
:return:
"""
a, b = advance_retreat_method(loss_function, start, direction)
# build the Fibonacci series
F, d = [1.0, 2.0], (b - a) / epsilon
while F[-1] < d:
F.append(F[-1] + F[-2])
# find the minimum
N = len(F) - 1
p, q = a + (1 - F[N - 1] / F[N]) * (b - a), a + F[N - 1] / F[N] * (b - a)
while abs(a - b) > epsilon and N > 0:
N = N - 1
f_p = loss_function.f(start +
point(direction[0] * p, direction[1] * p))
f_q = loss_function.f(start +
point(direction[0] * q, direction[1] * q))
if f_p < f_q:
b, q = q, p
p = a + (1 - F[N - 1] / F[N]) * (b - a)
else:
a, p = p, q
q = a + F[N - 1] / F[N] * (b - a)
return (a + b) / 2
def advance_retreat_method(loss_function: rosenbrock,
start: point,
direction: list,
step=0,
delta=0.1) -> tuple:
"""
find the initial section of step
:param loss_function:
:param start:
:param direction:
:param step:
:param delta:
:return:
"""
alpha0, point0 = step, start
alpha1 = alpha0 + delta
point1 = point0 + point(direction[0] * delta, direction[1] * delta)
if loss_function.f(point0) < loss_function.f(point1):
while True:
delta *= 2
alpha2 = alpha0 - delta
point2 = point0 - point(direction[0] * delta, direction[1] * delta)
if loss_function.f(point2) < loss_function.f(point0):
alpha1, alpha0 = alpha0, alpha2
point1, point0 = point0, point2
else:
return alpha2, alpha1
else:
while True:
delta *= 2
alpha2 = alpha1 + delta
point2 = point1 + point(direction[0] * delta, direction[1] * delta)
if loss_function.f(point2) < loss_function.f(point1):
alpha0, alpha1 = alpha1, alpha2
point0, point1 = point1, point2
else:
return alpha0, alpha2
