def newton_method(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
hessian_f = Autodiff(grad_f, be=be, next_error=None)
hessian_f = hessian_f.get_grad_op_tree([x_init])[0]
while True:
x_new[:] = x_init - grad_f / hessian_f
# if conv_vec_test(x_init, x_new, be) < epsilon:
if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new
def newton_method(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
hessian_f = Autodiff(grad_f, be=be, next_error=None)
hessian_f = hessian_f.get_grad_op_tree([x_init])[0]
while True:
x_new[:] = x_init - grad_f / hessian_f
# if conv_vec_test(x_init, x_new, be) < epsilon:
if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new
def fletcher_reeves(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
while True:
alpha, _ = None # implement the line search
def fletcher_reeves(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
while True:
alpha, _ = None # implement the line search