Subject: ANNOUNCE: fad 1.0 -- Forward Automatic Differentiation library Newsgroups: gmane.comp.lang.haskell.cafe Date: Friday 3rd April 2009 02:28:52 UTC (over 7 years ago) I'm pleased to announce the initial release of the Haskell fad library, developed by Barak A. Pearlmutter and Jeffrey Mark Siskind. Fad provides Forward Automatic Differentiation (AD) for functions polymorphic over instances of 'Num'. There have been many Haskell implementations of forward AD, with varying levels of completeness, published in papers and blog posts[1], but alarmingly few of these have made it into hackage -- to date Conal Elliot's vector-spaces[2] package is the only one I am aware of. Fad is an attempt to make as comprehensive and usable a forward AD package as is possible in Haskell. However, correctness is given priority over ease of use, and this is in my opinion the defining quality of fad. Specifically, Fad leverages Haskell's expressive type system to tackle the problem of _perturbation confusion_, brought to light in Pearlmutter and Siskind's 2005 paper "Perturbation Confusion and Referential Transparency"[3]. Fad prevents perturbation confusion by employing type-level "branding" as proposed by myself in a 2007 post to haskell-cafe[4]. To the best of our knowledge all other forward AD implementations in Haskell are susceptible to perturbation confusion. As this library has been in the works for quite some time it is worth noting that it hasn't benefited from Conal's ground-breaking work[5] in the area. Once we wrap our heads around his beautiful constructs perhaps we'll be able to borrow some tricks from him. As mentioned already, fad was developed primarily by Barak A. Pearlmutter and Jeffrey Mark Siskind. My own contribution has been providing Haskell infrastructure support and wrapping up loose ends in order to get the library into a releasable state. Many thanks to Barak and Jeffrey for permitting me to release fad under the BSD license. Fad resides on GitHub[6] and hackage[7] and is only a "cabal install fad" away! What follows is Fad's README, refer to the haddocks for detailed documentation. Thanks, Bjorn Buckwalter [1] http://www.haskell.org/haskellwiki/Functional_differentiation [2] http://www.haskell.org/haskellwiki/Vector-space [3]: http://www.bcl.hamilton.ie/~qobi/nesting/papers/ifl2005.pdf [4]: http://thread.gmane.org/gmane.comp.lang.haskell.cafe/22308/ [5]: http://conal.net/papers/beautiful-differentiation/ [6] http://github.com/bjornbm/fad/ [7] http://hackage.haskell.org/cgi-bin/hackage-scripts/package/fad ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Copyright : 2008-2009, Barak A. Pearlmutter and Jeffrey Mark Siskind License : BSD3 Maintainer : [email protected] Stability : experimental Portability: GHC only? Forward Automatic Differentiation via overloading to perform nonstandard interpretation that replaces original numeric type with corresponding generalized dual number type. Each invocation of the differentiation function introduces a distinct perturbation, which requires a distinct dual number type. In order to prevent these from being confused, tagging, called branding in the Haskell community, is used. This seems to prevent perturbation confusion, although it would be nice to have an actual proof of this. The technique does require adding invocations of lift at appropriate places when nesting is present. For more information on perturbation confusion and the solution employed in this library see: <http://www.bcl.hamilton.ie/~barak/papers/ifl2005.pdf> <http://thread.gmane.org/gmane.comp.lang.haskell.cafe/22308/> Installation ============ To install: cabal install Or: runhaskell Setup.lhs configure runhaskell Setup.lhs build runhaskell Setup.lhs install Examples ======== Define an example function 'f': > import Numeric.FAD > f x = 6 - 5 * x + x ^ 2 -- Our example function Basic usage of the differentiation operator: > y = f 2 -- f(2) = 0 > y' = diff f 2 -- First derivative f'(2) = -1 > y'' = diff (diff f) 2 -- Second derivative f''(2) = 2 List of derivatives: > ys = take 3 $ diffs f 2 -- [0, -1, 2] Example optimization method; find a zero using Newton's method: > y_newton1 = zeroNewton f 0 -- converges to first zero at 2.0. > y_newton2 = zeroNewton f 10 -- converges to second zero at 3.0. Credits ======= Authors: Copyright 2008, Barak A. Pearlmutter |
|||