# Automated Differentiation Task

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An Automated Differentiation Task is a function differentiation task that is an automated task.

**Context:**- It can be solved by an Automated Differentiation System.

**Counter-Example(s):**- to support Time-Marching Simulation of Electronic Circuits [1].

**Counter-Example(s):****See:**Mathematics, Computer Algebra, Derivative, Chain Rule, Symbolic Differentiation, Numerical Differentiation, Round-Off Error, Discretization, Gradient Descent, Theano Library.

## References

### 2019

- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Automatic_differentiation Retrieved:2019-12-16.
- … Automatic differentiation is distinct from symbolic differentiation and numerical differentiation (the method of finite differences). Symbolic differentiation can lead to inefficient code and faces the difficulty of converting a computer program into a single expression, while numerical differentiation can introduce round-off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where complexity and errors increase. Finally, both classical methods are slow at computing partial derivatives of a function with respect to
*many*inputs, as is needed for gradient-based optimization algorithms. Automatic differentiation solves all of these problems, at the expense of introducing more software dependencies.

- … Automatic differentiation is distinct from symbolic differentiation and numerical differentiation (the method of finite differences). Symbolic differentiation can lead to inefficient code and faces the difficulty of converting a computer program into a single expression, while numerical differentiation can introduce round-off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where complexity and errors increase. Finally, both classical methods are slow at computing partial derivatives of a function with respect to