Portability  GHC only 

Stability  experimental 
Maintainer  ekmett@gmail.com 
 Gradients (Reverse Mode)
 Higher Order Gradients (SparseonReverse)
 Jacobians (Sparse or Reverse)
 Higher Order Jacobian (SparseonReverse)
 Transposed Jacobians (Forward Mode)
 Hessian (SparseOnReverse)
 Hessian Tensors (Sparse or SparseOnReverse)
 Hessian Tensors (Sparse)
 Hessian Vector Products (ForwardOnReverse)
 Derivatives (Forward Mode)
 Derivatives (Tower)
 Directional Derivatives (Forward Mode)
 Directional Derivatives (Tower)
 Taylor Series (Tower)
 Maclaurin Series (Tower)
 Unsafe Variadic Grad
 Exposed Types
MixedMode Automatic Differentiation.
Each combinator exported from this module chooses an appropriate AD mode. The following basic operations are supported, modified as appropriate by the suffixes below:

grad
computes the gradient (partial derivatives) of a function at a point 
jacobian
computes the Jacobian matrix of a function at a point 
diff
computes the derivative of a function at a point 
du
computes a directional derivative of a function at a point 
hessian
compute the Hessian matrix (matrix of second partial derivatives) of a function at a point
The suffixes have the following meanings:

'
 also return the answer 
With
lets the user supply a function to blend the input with the output 
F
is a version of the base function lifted to return aTraversable
(orFunctor
) result 
s
means the function returns all higher derivatives in a list or fbranchingStream

T
means the result is transposed with respect to the traditional formulation. 
0
means that the resulting derivative list is padded with 0s at the end.
 grad :: (Traversable f, Num a) => FU f a > f a > f a
 grad' :: (Traversable f, Num a) => FU f a > f a > (a, f a)
 gradWith :: (Traversable f, Num a) => (a > a > b) > FU f a > f a > f b
 gradWith' :: (Traversable f, Num a) => (a > a > b) > FU f a > f a > (a, f b)
 grads :: (Traversable f, Num a) => FU f a > f a > Stream f a
 jacobian :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (f a)
 jacobian' :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (a, f a)
 jacobianWith :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > g (f b)
 jacobianWith' :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > g (a, f b)
 jacobians :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (Stream f a)
 jacobianT :: (Traversable f, Functor g, Num a) => FF f g a > f a > f (g a)
 jacobianWithT :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > f (g b)
 hessian :: (Traversable f, Num a) => FU f a > f a > f (f a)
 hessian' :: (Traversable f, Num a) => FU f a > f a > (a, f (a, f a))
 hessianF :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (f (f a))
 hessianF' :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (a, f (a, f a))
 hessianProduct :: (Traversable f, Num a) => FU f a > f (a, a) > f a
 hessianProduct' :: (Traversable f, Num a) => FU f a > f (a, a) > f (a, a)
 diff :: Num a => UU a > a > a
 diffF :: (Functor f, Num a) => UF f a > a > f a
 diff' :: Num a => UU a > a > (a, a)
 diffF' :: (Functor f, Num a) => UF f a > a > f (a, a)
 diffs :: Num a => UU a > a > [a]
 diffsF :: (Functor f, Num a) => UF f a > a > f [a]
 diffs0 :: Num a => UU a > a > [a]
 diffs0F :: (Functor f, Num a) => UF f a > a > f [a]
 du :: (Functor f, Num a) => FU f a > f (a, a) > a
 du' :: (Functor f, Num a) => FU f a > f (a, a) > (a, a)
 duF :: (Functor f, Functor g, Num a) => FF f g a > f (a, a) > g a
 duF' :: (Functor f, Functor g, Num a) => FF f g a > f (a, a) > g (a, a)
 dus :: (Functor f, Num a) => FU f a > f [a] > [a]
 dus0 :: (Functor f, Num a) => FU f a > f [a] > [a]
 dusF :: (Functor f, Functor g, Num a) => FF f g a > f [a] > g [a]
 dus0F :: (Functor f, Functor g, Num a) => FF f g a > f [a] > g [a]
 taylor :: Fractional a => UU a > a > a > [a]
 taylor0 :: Fractional a => UU a > a > a > [a]
 maclaurin :: Fractional a => UU a > a > [a]
 maclaurin0 :: Fractional a => UU a > a > [a]
 vgrad :: Grad i o o' a => i > o
 vgrad' :: Grad i o o' a => i > o'
 vgrads :: Grads i o a => i > o
 module Numeric.AD.Types
 class Lifted t => Mode t where
 class Num a => Grad i o o' a  i > a o o', o > a i o', o' > a i o
 class Num a => Grads i o a  i > a o, o > a i
Gradients (Reverse Mode)
grad :: (Traversable f, Num a) => FU f a > f a > f aSource
grad' :: (Traversable f, Num a) => FU f a > f a > (a, f a)Source
gradWith :: (Traversable f, Num a) => (a > a > b) > FU f a > f a > f bSource
function calculates the gradient of a nonscalartoscalar function grad
g ff
with reversemode AD in a single pass.
The gradient is combined elementwise with the argument using the function g
.
grad == gradWith (\_ dx > dx) id == gradWith const
gradWith' :: (Traversable f, Num a) => (a > a > b) > FU f a > f a > (a, f b)Source
Higher Order Gradients (SparseonReverse)
Jacobians (Sparse or Reverse)
jacobian :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (f a)Source
Calculate the Jacobian of a nonscalartononscalar function, automatically choosing between forward and reverse mode AD based on the number of inputs and outputs.
If you know the relative number of inputs and outputs, consider Numeric.AD.Reverse.jacobian
or Nuneric.AD.Sparse.jacobian
.
jacobian' :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (a, f a)Source
Calculate both the answer and Jacobian of a nonscalartononscalar function, automatically choosing between forward and reverse mode AD based on the relative, based on the number of inputs
If you know the relative number of inputs and outputs, consider Numeric.AD.Reverse.jacobian'
or Nuneric.AD.Sparse.jacobian'
.
jacobianWith :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > g (f b)Source
calculates the Jacobian of a nonscalartononscalar function, automatically choosing between forward and reverse mode AD based on the number of inputs and outputs.
jacobianWith
g f
The resulting Jacobian matrix is then recombined elementwise with the input using g
.
If you know the relative number of inputs and outputs, consider Numeric.AD.Reverse.jacobianWith
or Nuneric.AD.Sparse.jacobianWith
.
jacobianWith' :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > g (a, f b)Source
calculates the answer and Jacobian of a nonscalartononscalar function, automatically choosing between sparse and reverse mode AD based on the number of inputs and outputs.
jacobianWith'
g f
The resulting Jacobian matrix is then recombined elementwise with the input using g
.
If you know the relative number of inputs and outputs, consider Numeric.AD.Reverse.jacobianWith'
or Nuneric.AD.Sparse.jacobianWith'
.
Higher Order Jacobian (SparseonReverse)
Transposed Jacobians (Forward Mode)
jacobianT :: (Traversable f, Functor g, Num a) => FF f g a > f a > f (g a)Source
A fast, simple transposed Jacobian computed with forwardmode AD.
jacobianWithT :: (Traversable f, Functor g, Num a) => (a > a > b) > FF f g a > f a > f (g b)Source
A fast, simple transposed Jacobian computed with forwardmode AD.
Hessian (SparseOnReverse)
hessian :: (Traversable f, Num a) => FU f a > f a > f (f a)Source
Compute the Hessian via the Jacobian of the gradient. gradient is computed in reverse mode and then the Jacobian is computed in sparse (forward) mode.
hessian' :: (Traversable f, Num a) => FU f a > f a > (a, f (a, f a))Source
Hessian Tensors (Sparse or SparseOnReverse)
hessianF :: (Traversable f, Functor g, Num a) => FF f g a > f a > g (f (f a))Source
Compute the order 3 Hessian tensor on a nonscalartononscalar function using Sparse or SparseonReverse
Hessian Tensors (Sparse)
Hessian Vector Products (ForwardOnReverse)
hessianProduct :: (Traversable f, Num a) => FU f a > f (a, a) > f aSource
computes the product of the hessian hessianProduct
f wvH
of a nonscalartoscalar function f
at w =
with a vector fst
$ wvv = snd $ wv
using "Pearlmutter's method" from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.6143, which states:
H v = (d/dr) grad_w (w + r v)  r = 0
Or in other words, we take the directional derivative of the gradient. The gradient is calculated in reverse mode, then the directional derivative is calculated in forward mode.
hessianProduct' :: (Traversable f, Num a) => FU f a > f (a, a) > f (a, a)Source
computes both the gradient of a nonscalartoscalar hessianProduct'
f wvf
at w =
and the product of the hessian fst
$ wvH
at w
with a vector v = snd $ wv
using "Pearlmutter's method". The outputs are returned wrapped in the same functor.
H v = (d/dr) grad_w (w + r v)  r = 0
Or in other words, we return the gradient and the directional derivative of the gradient. The gradient is calculated in reverse mode, then the directional derivative is calculated in forward mode.
Derivatives (Forward Mode)
diff' :: Num a => UU a > a > (a, a)Source
The d'UU
function calculates the result and first derivative of scalartoscalar function by Forward
AD
d' sin == sin &&& cos d' f = f &&& d f
Derivatives (Tower)
Directional Derivatives (Forward Mode)
Directional Derivatives (Tower)
Taylor Series (Tower)
taylor :: Fractional a => UU a > a > a > [a]Source
taylor0 :: Fractional a => UU a > a > a > [a]Source
Maclaurin Series (Tower)
maclaurin :: Fractional a => UU a > a > [a]Source
maclaurin0 :: Fractional a => UU a > a > [a]Source
Unsafe Variadic Grad
Exposed Types
module Numeric.AD.Types
class Lifted t => Mode t whereSource
lift :: Num a => a > t aSource
Embed a constant
(<+>) :: Num a => t a > t a > t aSource
Vector sum
(*^) :: Num a => a > t a > t aSource
Scalarvector multiplication
(^*) :: Num a => t a > a > t aSource
Vectorscalar multiplication
(^/) :: Fractional a => t a > a > t aSource
Scalar division
'zero' = 'lift' 0