name: ad version: license: BSD3 license-File: LICENSE copyright:
(c) Edward Kmett 2010-2011, (c) Barak Pearlmutter and Jeffrey
Mark Siskind 2008-2009 author: Edward Kmett maintainer:
stability: Experimental category: Math homepage: build-type:
Simple cabal-version: >= 1.6 extra-source-files: TODO 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
outp