def Momentum(loss_function: rosenbrock,
start: point,
step=0.1,
rho=0.7,
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param step:
:param rho: the influence of historical gradients
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
direction = -loss_function.g(start) / np.linalg.norm(
loss_function.g(start))
p = step * direction
x.append(x[k] + point(p[0], p[1]))
while True:
k += 1
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
direction = -gradient / np.linalg.norm(gradient)
# add the historical p
p = rho * p + step * direction
x.append(x[k] + point(p[0], p[1]))
return x
def Adam_HD(loss_function: rosenbrock,
start: point,
initial_step=0.1,
rho0=0.9,
rho1=0.99,
beta=10e-7,
epsilon=10e-2,
k_max=10000) -> list:
"""
Adaptive momentum
:param loss_function:
:param start:
:param initial_step:
:param rho0:
:param rho1:
:param epsilon:
:param k_max:
:return:
"""
x, k, step = [start], 0, initial_step
delta = np.array([10e-7] * len(start))
r = np.zeros(len(start))
direction = -loss_function.g(start) / np.linalg.norm(
loss_function.g(start))
uk_old = direction
r = rho1 * r + (1 - rho1) * direction**2
p = step * uk_old
x.append(x[k] + point(p[0], p[1]))
while True:
k += 1
gradient = loss_function.g(x[k])
# if meet the termination conditions then break
if k > k_max or np.linalg.norm(gradient) < epsilon: break
gradient = -gradient / np.linalg.norm(gradient)
# add the influence rate of historical to direction
direction = rho0 * direction + (1 - rho0) * gradient
uk_new = direction / (r + delta)**0.5
# find the new x
# add the influence rate of historical to r
r = rho1 * r + (1 - rho1) * gradient**2
# update the step
step = step + beta * np.dot(uk_new, uk_old)
p = step * uk_new
x.append(x[k] + point(p[0], p[1]))
uk_old = uk_new
return x
def Adadelta(loss_function: rosenbrock,
start: point,
rho=0.99,
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param rho:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
delta = np.array([10e-7] * len(start))
step = np.zeros(len(start))
r = np.zeros(len(start))
while True:
gradient = loss_function.g(x[k])
# if meet the termination conditions then break
if k > k_max or np.linalg.norm(gradient) < epsilon: break
gradient = -gradient / np.linalg.norm(gradient)
# find the new x
# add the influence rate of historical to r
r = rho * r + (1 - rho) * gradient**2
p = gradient * ((step + delta) / (r + delta))**0.5
x.append(x[k] + point(p[0], p[1]))
step = rho * step + (1 - rho) * p**2
k += 1
return x
def Adagrad(loss_function: rosenbrock,
start: point,
initial_step=0.1,
epsilon=10e-2,
k_max=10000) -> list:
"""
Adaptive Gradient
:param loss_function:
:param start:
:param initial_step:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
delte = np.array([10e-7] * len(start))
r = np.zeros(len(start))
while True:
gradient = loss_function.g(x[k])
# if meet the termination conditions then break
if k > k_max or np.linalg.norm(gradient) < epsilon: break
gradient = -gradient / np.linalg.norm(gradient)
# find the new x
r = r + gradient**2
p = initial_step * gradient / (r + delte)**0.5
x.append(x[k] + point(p[0], p[1]))
k += 1
return x
def plain_gradient_descent(loss_function: rosenbrock,
start: point,
step=0.1,
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param step:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
while True:
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
direction = -gradient / np.linalg.norm(gradient)
p = step * direction
x.append(x[k] + point(p[0], p[1]))
k += 1
return x
def cyclic_coordinate_method(loss_function: rosenbrock, start: point, method='golden_search', epsilon=10e-1,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param method:
:param epsilon:
:param k_max:
:return:
"""
x, M, k = [start], len(start), 0
while True:
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
direction = [0] * M
direction[np.mod(k, M)] = 1
if method == 'golden_search':
step = golden_search(loss_function, x[k], direction)
elif method == 'fibonacci_search':
step = fibonacci_search(loss_function, x[k], direction)
elif method == 'dichotomous_search':
step = dichotomous_search(loss_function, x[k], direction)
else:
return x
x.append(x[k] + point(direction[0] * step, direction[1] * step))
k += 1
return x
def DFP(loss_function: rosenbrock,
start: point,
method='golden_search',
epsilon=10e-2,
k_max=10000) -> list:
"""
Davidon Fletcher Powell, quasi_newton_method
:param loss_function:
:param start:
:param step:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
D = np.identity(len(start)) # Identity matrix
while True:
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
gradient = gradient / np.linalg.norm(gradient)
direction = -np.matmul(D, gradient)
if method == 'golden_search':
step = golden_search(loss_function, x[k], direction)
elif method == 'fibonacci_search':
step = fibonacci_search(loss_function, x[k], direction)
elif method == 'dichotomous_search':
step = dichotomous_search(loss_function, x[k], direction)
else:
return x
p = step * direction
x.append(x[k] + point(p[0], p[1]))
# update the D
yk = np.mat(
loss_function.g(x[k + 1]) /
np.linalg.norm(loss_function.g(x[k + 1])) - gradient).T
pk = np.mat(p).T
Dk = np.mat(D)
D = D + np.array((pk * pk.T) / (pk.T * yk) - (Dk * yk * yk.T * Dk) /
(yk.T * Dk * yk))
k += 1
return x
def BFGS(loss_function: rosenbrock,
start: point,
method='golden_search',
epsilon=10e-2,
k_max=10000) -> list:
"""
Broyden Fletcher Goldfarb Shanno
:param loss_function:
:param start:
:param method:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
B = np.identity(len(start)) # Identity matrix
while True:
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
gradient = gradient / np.linalg.norm(gradient)
direction = -np.matmul(np.linalg.inv(B), gradient)
if method == 'golden_search':
step = golden_search(loss_function, x[k], direction)
elif method == 'fibonacci_search':
step = fibonacci_search(loss_function, x[k], direction)
elif method == 'dichotomous_search':
step = dichotomous_search(loss_function, x[k], direction)
else:
return x
p = step * direction
x.append(x[k] + point(p[0], p[1]))
# update the B
yk = np.mat(
loss_function.g(x[k + 1]) /
np.linalg.norm(loss_function.g(x[k + 1])) - gradient).T
pk = np.mat(p).T
Bk = np.mat(B)
B = B + np.array((yk * yk.T) / (yk.T * pk) - (Bk * pk * pk.T * Bk) /
(pk.T * Bk * pk))
k += 1
return x
def Nesterov_momentum_HD(loss_function: rosenbrock,
start: point,
initial_step=0.1,
rho=0.7,
mu=0.2,
beta=0.001,
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param step:
:param rho: the influence of historical gradients
:param beta: ahead rate
:param epsilon:
:param k_max:
:return:
"""
x, k, step = [start], 0, initial_step
direction_old = -loss_function.g(start) / np.linalg.norm(
loss_function.g(start))
p = step * direction_old
x.append(x[k] + point(p[0], p[1]))
while True:
k += 1
# if meet the termination conditions then break
if k > k_max or np.linalg.norm(loss_function.g(x[k])) < epsilon: break
# find the new x
# ahead p * beta
gradient = loss_function.g(x[k] + point(p[0] * mu, p[1] * mu))
direction_new = -gradient / np.linalg.norm(gradient)
# update the step
step = step + beta * np.dot(direction_new, direction_old)
# add the historical p
p = rho * p + step * direction_new
x.append(x[k] + point(p[0], p[1]))
direction_old = direction_new
return x
def plain_gradient_descent_HD(loss_function: rosenbrock,
start: point,
initial_step=0.1,
beta=0.001,
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param step:
:param epsilon:
:param k_max:
:return:
"""
x, k, step = [start], 0, initial_step
direction_old = -loss_function.g(start) / np.linalg.norm(
loss_function.g(start))
p = step * direction_old
x.append(x[k] + point(p[0], p[1]))
while True:
k += 1
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
direction_new = -gradient / np.linalg.norm(gradient)
# update the step
step = step + beta * np.dot(direction_new, direction_old)
p = step * direction_new
x.append(x[k] + point(p[0], p[1]))
direction_old = direction_new
return x
def conjugate_gradient(loss_function: rosenbrock,
start: point,
method='golden_search',
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param method:
:param epsilon:
:param k_max:
:return:
"""
x, direction, k = [
start
], -1 * loss_function.g(start) / np.linalg.norm(loss_function.g(start)), 0
while True:
# if meet the termination conditions then break
gradient_old = loss_function.g(x[k]) / np.linalg.norm(
loss_function.g(x[k]))
if np.linalg.norm(loss_function.g(x[k])) < epsilon or k > k_max: break
# find the new x
if method == 'golden_search':
step = golden_search(loss_function, x[k], direction)
elif method == 'fibonacci_search':
step = fibonacci_search(loss_function, x[k], direction)
elif method == 'dichotomous_search':
step = dichotomous_search(loss_function, x[k], direction)
else:
return x
x.append(x[k] + point(direction[0] * step, direction[1] * step))
# update the direction
gradient_new = loss_function.g(x[k + 1]) / np.linalg.norm(
loss_function.g(x[k + 1]))
alpha = np.dot(gradient_new, gradient_new) / np.dot(
gradient_old, gradient_old)
direction = -gradient_new + alpha * direction
k += 1
return x
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 Newton_method(loss_function: rosenbrock,
start: point,
method='golden_search',
epsilon=10e-2,
k_max=10000) -> list:
"""
:param loss_function:
:param start:
:param step:
:param epsilon:
:param k_max:
:return:
"""
x, k = [start], 0
while True:
# if meet the termination conditions then break
gradient = loss_function.g(x[k])
if k > k_max or np.linalg.norm(gradient) < epsilon: break
# find the new x
inverse = np.linalg.inv(loss_function.H(x[k]))
direction = -np.matmul(inverse, gradient)
if method == 'golden_search':
step = golden_search(loss_function, x[k], direction)
elif method == 'fibonacci_search':
step = fibonacci_search(loss_function, x[k], direction)
elif method == 'dichotomous_search':
step = dichotomous_search(loss_function, x[k], direction)
else:
return x
p = step * direction
x.append(x[k] + point(p[0], p[1]))
k += 1
return x