Editing Automatic differentiation (section)

== Difference from other differentiation methods ==
[[Image:AutomaticDifferentiationNutshell.png|right|thumb|300px|Figure 1: How automatic differentiation relates to symbolic differentiation]]
Automatic differentiation is distinct from [[symbolic differentiation]] and [[numerical differentiation]]. 
Symbolic differentiation faces the difficulty of converting a computer program into a single [[mathematical expression]] and can lead to inefficient code. Numerical differentiation (the method of finite differences) can introduce [[round-off error]]s in the [[discretization]] process and cancellation. Both of these classical methods have problems with calculating higher derivatives, where complexity and errors increase. Finally, both of these classical methods are slow at computing partial derivatives of a function with respect to ''many'' inputs, as is needed for [[gradient descent|gradient]]-based [[Optimization (mathematics)|optimization]] algorithms. Automatic differentiation solves all of these problems.