# Why do we use gradient descent in the backpropagation algorithm?

The common approach for training neural networks, as far as i know, is the backpropagation algortihm, which uses gradient descent to reduce the error.

(i) why should one use a fixed learning rate / simulated annealing over, let's say, Armijo rule?

(ii) is there a good reason to use gradient descent in this case, or did that just grow historically? Especially, is gradient descent in this case favorable over algorithms like Newton's (Quasi-Newton, globalized Newton)?

-