I wanted to quickly test Newton’s method (and second order methods). Is there any easy way to do this in pytorch?

I don’t know any official method but, this should be a good start:

```
import torch
from torch.autograd import Variable
def my_function(x):
return (x**3 + 0.5)
def newton(func, guess, threshold = 1e-7):
guess = Variable(guess, requires_grad=True)
value = my_function(guess)
while abs(value.data.numpy()[0]) > threshold:
value = my_function(guess)
value.backward()
guess.data -= (value / guess.grad).data
guess.grad.data.zero_()
return guess.data
guess = torch.ones(1)
x = opt(my_function, guess)
print("x = %s, f(x) = %s" % (x.numpy()[0], my_function(x).numpy()[0]))
```

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Try `torch.optim.LBFGS`

. This performs a Newton update with approximate Hessian.

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