My Constraint Programming Blog

From JaCoP's news JaCoP version 2.4 is released:

Dear all,

We are happy to announce the release of a new version of our
Java-based solver JaCoP. This new version 2.4 has a number of new
features in addition to some bug fixes. The most important additions
in this version are:

1. The flatzinc interface that makes it possible to run minizinc
programs using JaCoP. The distribution contains number of different
minizinc examples.

2. Geometrical constraint, geost, based on pruning algorithms
originally proposed by Nicolas Beldiceanu et al. This constraint makes
it possible to define placement problems of non-convex objects in
k-dimensional space.

3. Knapsack constraint, which is based on the work published by Irit
Katriel et al. We extend the original work in number of ways, for example by
making it possible to use non-boolean quantity variables.

4. Set constraints defining typical operation on sets using set
interval variables.

This work would not be possible without help of several people. We
would like to thank Hakan Kjellerstrand for his help in testing
flatzinc parser as well as providing a number of examples in minizinc
format. We would also like to thank Meinolf Sellmann for his comments
on the initial implementation of knapsack constraint which have helped to
improve it further. Marc-Olivier Fleury has implemented most of the
functionality of the geost constraint. Robert Åkemalm has implemented the
first version of set constraint package. Wadeck Follonier has implemented
the first version of the Knapsack constraint. We would like to thank them
for their contributions.

As always feel free to send us ( radoslaw [dot] szymanek [at] gmail [dot] com )
feedback. We are always looking for cooperation to improve JaCoP. If you miss
some functionality of JaCoP we can help you to develop it so it can be done
efficiently and fast.


best regards,
Radoslaw Szymanek and Kris Kuchcinski

The latest version of JaCoP can be downloaded here.

For more information, see:

• JaCoP Library User’s Guide
• JaCoP API

Also see My JaCoP page.

MiniZinc/FlatZinc support

I have especially tested the FlatZinc solver (Fz2jacop) and it is fast. For example, here is the statistics for nonogram_create_automaton2.mzn with the P200 problem (I wrote about this some days ago here):

Model variables : 629
Model constraints : 50

Search CPU time : 640ms
Search nodes : 1040
Search decisions : 520
Wrong search decisions : 520
Search backtracks : 520
Max search depth : 22
Number solutions : 1

Total CPU time : 1010ms

Note: JaCoP uses a special optimized regular constraint.

The FlatZinc solver has the following options:

$ java JaCoP.fz.Fz2jacop --help
Usage: java Fz2jacop [] .fzn
Options:
    -h, --help
        Print this message.
    -a, --all-solutions
    -t , --time-out 
         - time in second.
    -s, --statistics
    -n , --num-solutions 
         - limit on solution number.

Great work!