Automatic Differentiation

Automatic Differentiation roughly means that a numerical value is equipped with a derivative part, which is updated accordingly on every function application. Let the number $x_{0}$ be equipped with the derivative $x_{1}$: $\langle x_{0},x_{1}\rangle$. For example the sinus is defined as:

• $\sin \langle x_{0},x_{1}\rangle =\langle \sin x_{0},x_{1}\cdot \cos x_{0}\rangle$

You see, that's just estimating errors as in physics. However, it becomes more interesting for vector functions.

Implementations:

• ad
• fad
• rad
• Vector-space
• http://comonad.com/haskell/monoids/dist/doc/html/monoids/Data-Ring-Module-AutomaticDifferentiation.html

Power Series

You may count arithmetic with power series also as Automatic Differentiation, since this means just working with all derivatives simultaneously.

Implementation with Haskell 98 type classes: http://code.haskell.org/~thielema/htam/src/PowerSeries/Taylor.hs

With advanced type classes in Numeric Prelude: http://hackage.haskell.org/packages/archive/numeric-prelude/0.0.5/doc/html/MathObj-PowerSeries.html

See also

• Functional differentiation
• Chris Smith in Haskell-cafe on Hit a wall with the type system