ad: Automatic Differentiation
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.Forwardprovides basic forward-mode AD. It is good for computing simple derivatives.
Numeric.AD.Mode.Reverseuses benign side-effects to compute reverse-mode AD. It is good for computing gradients in one pass.
Numeric.AD.Mode.Sparsecomputes a sparse forward-mode AD tower. It is good for higher derivatives or large numbers of outputs.
Numeric.AD.Mode.Towercomputes a dense forward-mode AD tower useful for higher derivatives of single input functions.
Numeric.AD.Mode.Mixedcomputes using whichever mode or combination thereof is suitable to each individual combinator. This mode is the default, re-exported by
While not every mode can provide all operations, the following basic operations are supported, modified as appropriate by the suffixes below:
gradcomputes the gradient (partial derivatives) of a function at a point.
jacobiancomputes the Jacobian matrix of a function at a point.
diffcomputes the derivative of a function at a point.
ducomputes a directional derivative of a function at a point.
hessiancomputes the Hessian matrix (matrix of second partial derivatives) of a function at a point.
The following suffixes alter the meanings of the functions above as follows:
'-- also return the answer
Withlets the user supply a function to blend the input with the output
Fis a version of the base function lifted to return a
smeans the function returns all higher derivatives in a list or f-branching
Tmeans the result is transposed with respect to the traditional formulation.
0means that the resulting derivative list is padded with 0s at the end.
Changes since 1.1.3
Made primal calculations strict where possible.
Changes since 1.1.0
Introduced a much faster topological sort into the reverse mode AD implementation by Anthony Cowley. This fixes a space leak and a stack overflow problem on very large (>2000 variable) problem sets.
Made bound calculations in reverse mode more strict.
Changes since 1.0.0
Changed the way
Showwas derived to comply with changes in instance resolution in ghc >= 7.0 && <= 7.1
Changes since 0.45.0
Streamto use the external
Changes since 0.44.5
Added Halley's method
Changes since 0.40.0
Fixed bug fix for
'(/)' :: (Mode s, Fractional a) => AD s a
Regularized naming conventions
Id, probe, and lower methods via
Removed monadic combinators
Mixedmode jacobian calculations to only require a
Added unsafe variadic
|Versions [faq]||0.12, 0.13, 0.15, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.27, 0.28, 0.30.0, 0.31.0, 0.32.0, 0.33.0, 0.40, 0.40.1, 0.44.0, 0.44.1, 0.44.2, 0.44.3, 0.44.4, 0.45.0, 0.46.0, 0.46.1, 0.46.2, 0.47.0, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.0.6, 1.1.0, 220.127.116.11, 1.1.1, 1.1.3, 1.2.0, 18.104.22.168, 22.214.171.124, 1.3, 126.96.36.199, 1.3.1, 1.4, 1.5, 188.8.131.52, 184.108.40.206, 3.0, 3.0.1, 3.1.1, 3.1.2, 3.1.3, 3.1.4, 3.2, 3.2.1, 3.2.2, 220.127.116.11, 3.3.1, 18.104.22.168, 3.4, 4.0, 22.214.171.124, 4.1, 4.2, 126.96.36.199, 4.2.1, 188.8.131.52, 4.2.2, 4.2.3, 4.2.4, 4.3, 4.3.1, 4.3.2, 184.108.40.206, 4.3.3, 4.3.4, 4.3.5, 4.3.6, 4.4|
|Dependencies||array (>=0.2 && <0.4), base (==4.*), comonad (>=1.1.1 && <1.2), containers (>=0.2 && <0.5), data-reify (==0.6.*), free (==2.0.*), template-haskell (>=2.4 && <2.6) [details]|
|Copyright||(c) Edward Kmett 2010-2011, (c) Barak Pearlmutter and Jeffrey Mark Siskind 2008-2009|
|Source repo||head: git clone git://github.com/ekmett/ad.git|
|Uploaded||by EdwardKmett at 2011-08-05T08:29:40Z|
|Distributions||LTSHaskell:4.3.6, NixOS:4.4, Stackage:4.3.6|
|Downloads||68760 total (60 in the last 30 days)|
|Rating||2.5 (votes: 3) [estimated by Bayesian average]|
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