ad-4.2.1.1: Automatic Differentiation

Copyright(c) Edward Kmett 2010-2014
LicenseBSD3
Maintainerekmett@gmail.com
Stabilityexperimental
PortabilityGHC only
Safe HaskellNone
LanguageHaskell2010

Numeric.AD.Rank1.Forward

Contents

Description

Forward mode automatic differentiation

Synopsis

Documentation

data Forward a Source

Forward mode AD

Instances

(Num a, Bounded a) => Bounded (Forward a) 
(Num a, Enum a) => Enum (Forward a) 
(Num a, Eq a) => Eq (Forward a) 
Floating a => Floating (Forward a) 
Fractional a => Fractional (Forward a) 
Data a => Data (Forward a) 
Num a => Num (Forward a) 
(Num a, Ord a) => Ord (Forward a) 
Real a => Real (Forward a) 
RealFloat a => RealFloat (Forward a) 
RealFrac a => RealFrac (Forward a) 
Show a => Show (Forward a) 
Erf a => Erf (Forward a) 
InvErf a => InvErf (Forward a) 
Num a => Mode (Forward a) 
Num a => Jacobian (Forward a) 
Typeable (* -> *) Forward 
type Scalar (Forward a) = a 
type D (Forward a) = Id a 

auto :: Mode t => Scalar t -> t Source

Embed a constant

Gradient

grad :: (Traversable f, Num a) => (f (Forward a) -> Forward a) -> f a -> f a Source

Compute the gradient of a function using forward mode AD.

Note, this performs O(n) worse than grad for n inputs, in exchange for better space utilization.

grad' :: (Traversable f, Num a) => (f (Forward a) -> Forward a) -> f a -> (a, f a) Source

Compute the gradient and answer to a function using forward mode AD.

Note, this performs O(n) worse than grad' for n inputs, in exchange for better space utilization.

gradWith :: (Traversable f, Num a) => (a -> a -> b) -> (f (Forward a) -> Forward a) -> f a -> f b Source

Compute the gradient of a function using forward mode AD and combine the result with the input using a user-specified function.

Note, this performs O(n) worse than gradWith for n inputs, in exchange for better space utilization.

gradWith' :: (Traversable f, Num a) => (a -> a -> b) -> (f (Forward a) -> Forward a) -> f a -> (a, f b) Source

Compute the gradient of a function using forward mode AD and the answer, and combine the result with the input using a user-specified function.

Note, this performs O(n) worse than gradWith' for n inputs, in exchange for better space utilization.

>>> gradWith' (,) sum [0..4]
(10,[(0,1),(1,1),(2,1),(3,1),(4,1)])

Jacobian

jacobian :: (Traversable f, Traversable g, Num a) => (f (Forward a) -> g (Forward a)) -> f a -> g (f a) Source

Compute the Jacobian using Forward mode AD. This must transpose the result, so jacobianT is faster and allows more result types.

>>> jacobian (\[x,y] -> [y,x,x+y,x*y,exp x * sin y]) [pi,1]
[[0.0,1.0],[1.0,0.0],[1.0,1.0],[1.0,3.141592653589793],[19.472221418841606,12.502969588876512]]

jacobian' :: (Traversable f, Traversable g, Num a) => (f (Forward a) -> g (Forward a)) -> f a -> g (a, f a) Source

Compute the Jacobian using Forward mode AD along with the actual answer.

jacobianWith :: (Traversable f, Traversable g, Num a) => (a -> a -> b) -> (f (Forward a) -> g (Forward a)) -> f a -> g (f b) Source

Compute the Jacobian using Forward mode AD and combine the output with the input. This must transpose the result, so jacobianWithT is faster, and allows more result types.

jacobianWith' :: (Traversable f, Traversable g, Num a) => (a -> a -> b) -> (f (Forward a) -> g (Forward a)) -> f a -> g (a, f b) Source

Compute the Jacobian using Forward mode AD combined with the input using a user specified function, along with the actual answer.

Transposed Jacobian

jacobianT :: (Traversable f, Functor g, Num a) => (f (Forward a) -> g (Forward a)) -> f a -> f (g a) Source

A fast, simple, transposed Jacobian computed with forward-mode AD.

jacobianWithT :: (Traversable f, Functor g, Num a) => (a -> a -> b) -> (f (Forward a) -> g (Forward a)) -> f a -> f (g b) Source

A fast, simple, transposed Jacobian computed with Forward mode AD that combines the output with the input.

Hessian Product

hessianProduct :: (Traversable f, Num a) => (f (On (Forward (Forward a))) -> On (Forward (Forward a))) -> f (a, a) -> f a Source

Compute the product of a vector with the Hessian using forward-on-forward-mode AD.

hessianProduct' :: (Traversable f, Num a) => (f (On (Forward (Forward a))) -> On (Forward (Forward a))) -> f (a, a) -> f (a, a) Source

Compute the gradient and hessian product using forward-on-forward-mode AD.

Derivatives

diff :: Num a => (Forward a -> Forward a) -> a -> a Source

The diff function calculates the first derivative of a scalar-to-scalar function by forward-mode AD

>>> diff sin 0
1.0

diff' :: Num a => (Forward a -> Forward a) -> a -> (a, a) Source

The diff' function calculates the result and first derivative of scalar-to-scalar function by Forward mode AD

diff' sin == sin &&& cos
diff' f = f &&& d f

>>> diff' sin 0
(0.0,1.0)

>>> diff' exp 0
(1.0,1.0)

diffF :: (Functor f, Num a) => (Forward a -> f (Forward a)) -> a -> f a Source

The diffF function calculates the first derivatives of scalar-to-nonscalar function by Forward mode AD

>>> diffF (\a -> [sin a, cos a]) 0
[1.0,-0.0]

diffF' :: (Functor f, Num a) => (Forward a -> f (Forward a)) -> a -> f (a, a) Source

The diffF' function calculates the result and first derivatives of a scalar-to-non-scalar function by Forward mode AD

>>> diffF' (\a -> [sin a, cos a]) 0
[(0.0,1.0),(1.0,-0.0)]

Directional Derivatives

du :: (Functor f, Num a) => (f (Forward a) -> Forward a) -> f (a, a) -> a Source

Compute the directional derivative of a function given a zipped up Functor of the input values and their derivatives

du' :: (Functor f, Num a) => (f (Forward a) -> Forward a) -> f (a, a) -> (a, a) Source

Compute the answer and directional derivative of a function given a zipped up Functor of the input values and their derivatives

duF :: (Functor f, Functor g, Num a) => (f (Forward a) -> g (Forward a)) -> f (a, a) -> g a Source

Compute a vector of directional derivatives for a function given a zipped up Functor of the input values and their derivatives.

duF' :: (Functor f, Functor g, Num a) => (f (Forward a) -> g (Forward a)) -> f (a, a) -> g (a, a) Source

Compute a vector of answers and directional derivatives for a function given a zipped up Functor of the input values and their derivatives.