name: ad
version: 1.0.1
x-revision: 1
license: BSD3
license-File: LICENSE
copyright: (c) Edward Kmett 2010-2011,
(c) Barak Pearlmutter and Jeffrey Mark Siskind 2008-2009
author: Edward Kmett
maintainer: ekmett@gmail.com
stability: Experimental
category: Math
homepage: http://github.com/ekmett/ad
synopsis: Automatic Differentiation
description:
Forward-, reverse- and mixed- mode automatic differentiation combinators with a common API.
.
Type-level \"branding\" is used to both prevent the end user from confusing infinitesimals
and to limit unsafe access to the implementation details of each Mode.
.
Each mode has a separate module full of combinators.
.
* @Numeric.AD.Mode.Forward@ provides basic forward-mode AD. It is good for computing simple derivatives.
.
* @Numeric.AD.Mode.Reverse@ uses benign side-effects to compute reverse-mode AD. It is good for computing gradients in one pass.
.
* @Numeric.AD.Mode.Sparse@ computes a sparse forward-mode AD tower. It is good for higher derivatives or large numbers of outputs.
.
* @Numeric.AD.Mode.Tower@ computes a dense forward-mode AD tower useful for higher derivatives of single input functions.
.
* @Numeric.AD.Mode.Mixed@ computes using whichever mode or combination thereof is suitable to each individual combinator. This mode is the default, re-exported by @Numeric.AD@
.
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While not every mode can provide all operations, 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.
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* 'du' computes a directional derivative of a function at a point.
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* 'hessian' computes the Hessian matrix (matrix of second partial derivatives) of a function at a point.
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The following suffixes alter the meanings of the functions above as follows:
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* @\'@ -- also return the answer
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* @With@ lets the user supply a function to blend the input with the output
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* @F@ is a version of the base function lifted to return a 'Traversable' (or 'Functor') result
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* @s@ means the function returns all higher derivatives in a list or f-branching 'Stream'
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* @T@ means the result is transposed with respect to the traditional formulation.
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* @0@ means that the resulting derivative list is padded with 0s at the end.
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Changes since 0.45.0
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* Converted 'Stream' to use the external 'comonad' package
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Changes since 0.44.5
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* Added Halley's method
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Changes since 0.40.0
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* Fixed bug fix for @'(/)' :: (Mode s, Fractional a) => AD s a@
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* Improved documentation
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* Regularized naming conventions
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* Exposed 'Id', probe, and lower methods via @Numeric.AD.Types@
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* Removed monadic combinators
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* Retuned the 'Mixed' mode jacobian calculations to only require a 'Functor'-based result.
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* Added unsafe variadic 'vgrad', 'vgrad'', and 'vgrads' combinators
build-type: Simple
build-depends:
base >= 4 && < 5,
data-reify >= 0.6 && < 0.7,
containers >= 0.2 && < 0.5,
template-haskell >= 2.4 && < 2.5,
array >= 0.2 && < 0.4,
comonad >= 1.0 && < 1.1,
streams >= 0.6 && < 0.7
exposed-modules:
Numeric.AD
Numeric.AD.Classes
Numeric.AD.Types
Numeric.AD.Newton
Numeric.AD.Halley
Numeric.AD.Internal.Classes
Numeric.AD.Internal.Combinators
Numeric.AD.Internal.Forward
Numeric.AD.Internal.Tower
Numeric.AD.Internal.Reverse
Numeric.AD.Internal.Sparse
Numeric.AD.Internal.Dense
Numeric.AD.Internal.Composition
Numeric.AD.Mode.Directed
Numeric.AD.Mode.Forward
Numeric.AD.Mode.Mixed
Numeric.AD.Mode.Reverse
Numeric.AD.Mode.Tower
Numeric.AD.Mode.Sparse
other-modules:
Numeric.AD.Internal.Types
Numeric.AD.Internal.Tensors
Numeric.AD.Internal.Identity
Extra-Source-Files: TODO
GHC-Options: -Wall -fspec-constr -fdicts-cheap -O2