# Automatic Differentiation

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+ | * [http://hackage.haskell.org/cgi-bin/hackage-scripts/package/ad ad] |
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* [http://hackage.haskell.org/cgi-bin/hackage-scripts/package/fad fad] |
* [http://hackage.haskell.org/cgi-bin/hackage-scripts/package/fad fad] |
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+ | * [http://hackage.haskell.org/cgi-bin/hackage-scripts/package/rad rad] |
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* [[Vector-space]] |
* [[Vector-space]] |
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* http://comonad.com/haskell/monoids/dist/doc/html/monoids/Data-Ring-Module-AutomaticDifferentiation.html |
* http://comonad.com/haskell/monoids/dist/doc/html/monoids/Data-Ring-Module-AutomaticDifferentiation.html |

## Revision as of 21:26, 12 May 2011

**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}: .
For example the sinus is defined as:

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

## 1 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

## 2 See also

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