@hackage ADPfusion0.6.0.0

Efficient, high-level dynamic programming.

  • Installation

  • Tested Compilers

  • Dependencies (20)

  • Dependents (2)

    @hackage/FormalGrammars, @hackage/GrammarProducts
  • Package Flags

       (off by default)

      Enable bounds checking and various other debug operations at the cost of a significant performance penalty.

       (off by default)

      Enable debug output, which spams the screen full of index information

       (off by default)

      Enable dumping intermediate / core files

       (off by default)

      Dump HTML for the core generated by GHC during compilation

       (off by default)

      build the examples

       (off by default)

      build the spec-ctor test case

       (off by default)

      build additional tests

       (off by default)

      performance test for backtracking structures

       (off by default)

      use llvm

Build Status


generalized Algebraic Dynamic Programming Homepage

Ideas implemented here are described in a couple of papers:

  1. Christian Hoener zu Siederdissen
    Sneaking Around ConcatMap: Efficient Combinators for Dynamic Programming
    2012, Proceedings of the 17th ACM SIGPLAN international conference on Functional programming
    paper preprint
  2. Andrew Farmer, Christian Höner zu Siederdissen, and Andy Gill.
    The HERMIT in the stream: fusing stream fusion’s concatMap
    2014, Proceedings of the ACM SIGPLAN 2014 workshop on Partial evaluation and program manipulation.
  3. Christian Höner zu Siederdissen, Ivo L. Hofacker, and Peter F. Stadler.
    Product Grammars for Alignment and Folding
    2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics. 99
  4. Christian Höner zu Siederdissen, Sonja J. Prohaska, and Peter F. Stadler
    Algebraic Dynamic Programming over General Data Structures
    2015, BMC Bioinformatics
  5. Maik Riechert, Christian Höner zu Siederdissen, and Peter F. Stadler
    Algebraic dynamic programming for multiple context-free languages
    2016, Theoretical Computer Science


ADPfusion combines stream-fusion (using the stream interface provided by the vector library) and type-level programming to provide highly efficient dynamic programming combinators.

From the programmers' viewpoint, ADPfusion behaves very much like the original ADP implementation http://bibiserv.techfak.uni-bielefeld.de/adp/ developed by Robert Giegerich and colleagues, though both combinator semantics and backtracking are different.

The library internals, however, are designed not only to speed up ADP by a large margin (which this library does), but also to provide further runtime improvements by allowing the programmer to switch over to other kinds of data structures with better time and space behaviour. Most importantly, dynamic programming tables can be strict, removing indirections present in lazy, boxed tables.

As an example, even rather complex ADP code tends to be completely optimized to loops that use only unboxed variables (Int# and others, indexIntArray# and others).

Completely novel (compared to ADP), is the idea of allowing efficient monadic combinators. This facilitates writing code that performs backtracking, or samples structures stochastically, among others things.


Follow the gADP examples.

Implementors Notes (if you want to extend ADPfusion)

These have been moved to HACKING.md.


Christian Hoener zu Siederdissen
Leipzig University, Leipzig, Germany