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## Automatic Differentiation

In this system we work in terms of functions; the derivative of a function is a function. The procedure for producing the derivative of a function is named "derivative", though we also use the single-letter symbol `D` to denote this operator.

 Note The differentation offered by SICMUtils uses "forward mode Automatic Differentation". We plan to implement reverse-mode AD at some point, but it doesn’t exist here yet.

We start with functions of a real variable to a real variable:

``((D cube) 5) ;;=> 75``

It is possible to compute the derivative of any composition of functions:

``````((D (+ (square sin) (square cos))) 3)
;;=> 0

(defn unity1 [x]
(+ (square (sin x))
(square (cos x))))

((D unity1) 4)
;;=> 0

(def unity2
(+ (compose square sin)
(compose square cos)))

((D unity2) 4)
;;=> 0``````

except that the computation of the value of the function may not require evaluating a conditional.

 Note This note about conditionals is currently true in SICMUtils, but we’re working on it. See this ticket for information on the plan to make generic comparisons in conditionals work in automatic differentiation.

These derivatives are not numerical approximations estimated by some limiting process. However, as usual, some of the procedures that are used to compute the derivative may be numerical approximations.

```((D sin) 3)    ;;=> -.9899924966004454
(cos 3)        ;;=> -.9899924966004454```
 Note If you do want a numerical derivative, see the docstring for the `D-numeric` function.

Of course, not all functions are simple compositions of univariate real-valued functions of real arguments. Some functions have multiple arguments, and some have structured values.

First we consider the case of multiple arguments. If a function maps several real arguments to a real value, then its derivative is a representation of the gradient of that function — we must be able to multiply the derivative by an incremental up tuple to get a linear approximation to an increment of the function, if we take a step described by the incremental up tuple. Thus the derivative must be a down tuple of partial derivatives. We will talk about computing partial derivatives later.

Let’s understand this in a simple case. Let `f(x,y) = x^3 y^5`:

``````(defn f [x y]
(* (expt x 3)
(expt y 5)))``````

Then `Df(x,y)` is a down tuple with components `[2 x^2 y^5, 5 x^3 y^4]`:

``(simplify ((D f) 2 3)) ;;=> (down 2916 3240)``

And the inner product with an incremental up tuple is the appropriate increment.

`(* ((D f) 2 3) (up 0.1 0.2)) ;;=> 939.6`

This is exactly the same as if we had a function of one up-tuple argument. Of course, we must supply an up-tuple to the derivative in this case:

``````(defn g [[x y]]
(* (expt x 3)
(expt y 5)))

(simplify ((D g) (up 2 3)))
;;=> (down 2916 3240)

(* ((D g) (up 2 3)) (up 0.1 0.2))
;;=> 939.6``````

Things get somewhat more complicated when we have functions with multiple structured arguments. Consider a function whose first argument is an up tuple and whose second argument is a number, which adds the cube of the number to the dot product of the up tuple with itself.

``````(defn h [v x]
(+ (cube x)
(square v)))``````

What is its derivative? Well, it had better be something that can multiply an increment in the arguments, to get an increment in the function. The increment in the first argument is an incremental up tuple. The increment in the second argument is a small number. Thus we need a down-tuple of two parts, a row of the values of the partial derivatives with respect to each component of the first argument and the value of the partial derivative with respect to the second argument. This is easier to see symbolically:

``````(simplify ((D h) (up 'a 'b) 'c))
;;=> (down (down (* 2 a) (* 2 b)) (* 3 (expt c 2)))``````

The idea generalizes.

### Partial Derivatives

Partial derivatives are just the components of the derivative of a function that takes multiple arguments or structured arguments or both. Thus, a partial derivative of a function is a composition of a component selector and the derivative of that function.

The procedure that makes a partial derivative operator given a selection chain is named `partial`.

 Note Clojure also has a `partial` function, that returns the partial application of some function `f` to whatever arguments you supply. In the `sicmutils.env` namespace this is aliased as `core-partial`.

For example:

``````(simplify (((partial 0) h) (up 'a 'b) 'c))
;;=> (down (* 2 a) (* 2 b))

(simplify (((partial 1) h) (up 'a 'b) 'c))
;;=> (* 3 (expt c 2))

(simplify (((partial 0 0) h) (up 'a 'b) 'c))
;;=> (* 2 a)

(simplify (((partial 0 1) h) (up 'a 'b) 'c))
;;=> (* 2 b)``````

This naming scheme is consistent, except for one special case. If a function takes exactly one up-tuple argument then one level of the hierarchy is eliminated, allowing one to naturally write:

``````(simplify ((D g) (up 'a 'b)))
;;=> (down (* 3 (expt a 2) (expt b 5))
(* 5 (expt a 3) (expt b 4)))

(simplify (((partial 0) g) (up 'a 'b)))
;;=> (* 3 (expt a 2) (expt b 5))

(simplify (((partial 1) g) (up 'a 'b)))
;;=> (* 5 (expt a 3) (expt b 4))``````

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