The JaCoP constraint programming solver received the silver medal (second place) in the MIniZinc solver challenge in the fix search category this year. This year, the MiniZinc challenge was organized by Data61 and CSIRO and had 16 entrants (solvers). For more information please check http://www.minizinc.org/challenge2017/results2017.html
The JaCoP constraint programming solver received the silver medal (second place) in the MIniZinc solver challenge in the fix search category this year. This year, the MiniZinc challenge was organized by Data61 and CSIRO and had 15 entrants (solvers). In the competition each solver was given 20 different problems, each having 5 sets of input data, all together 100 different instances to solve. For more information please check www.minizinc.org/challenge2016/results2016.html.
We have deployed JaCoP 4.4 on Maven Central.
You can find the artefact by browsing this page - http://search.maven.org/#artifactdetails%7Corg.jacop%7Cjacop%7C4.4.0%7Cjar
Core Developers Team
We would like to thank YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit Java Profiler innovative and intelligent tools for profiling Java and .NET applications. It will help us to make JaCoP even faster ;).
Core Developer team
We have just released JaCoP 4.1. Feel free to clone JaCoP repository - https://github.com/radsz/jacop and contribute. In this release we introduce floating-point variables (FloatVar) and constraints on these variables. The methods used in floating point domain of JaCoP are based on floating point intervals and consistency methods build around them. We provide a set of basic arithmetic constraints as well as square root, absolute value, trigonometric constraints (sin, cos,tan, asin, acos, atan), exponential constraint and natural logarithm. Moreover element constraint and equation between integer and float variables makes it possible to build models that mix different JaCoP variables. We have also included experimental implementation of several features that is not consider as final and can change in future.
The floating point domain is fully integrated with JaCoP solver. The same search methods can be used as well as floating point variables can be used as cost functions for minimization. We provide also specialized optimization methods that are specially developed for floating point variables.
The released version provides also flatzinc interpreter for floating point variables. Minizinc models containing float variables can be compiled using standard mzn2fzn compiler and used by JaCoP solver.
This release fixes also few bugs.
JaCoP Core Developer Team
We decided to move the source code for the newest version of JaCoP (4.0.0.RC1) to Github. We are happy users of Git and Github is an obvious choice. Feel free to clone JaCoP repository - https://github.com/radsz/jacop and contribute. The changes within the newest JaCoP 4.0.0.RC1 are published here.
JaCoP core developer team
We are happy to announce that Victor Zverovich from AMPL has created a driver that allows to use JaCoP from AMPL. As most AMPL drivers, the jacop driver is open source. This driver is available from the AMPL repository on GitHub: https://github.com/vitaut/ampl/tree/master/solvers/jacop .
Feel free to check this driver and contribute in any manner possible. It is said to be fairly complete and implementing all of the AMPL constraint programming extensions and a set of options.
We would like to thank Victor and AMPL for their work.
JaCoP core developer team
We have just released JaCoP 3.2. This is a release that fixes few bugs as well as provides an interface from Scala to JaCoP. Examples using Scala are provided in ExamplesScala package.
Core Developer Team
We are working hard in our free time. There are multiple aspects that are being currently on our agenda. First, SAT solver that cooperates internally with JaCoP so constraints can send explanation to SAT solver. Second, we are polishing a stochastic subpackage within a JaCoP so it possible to model problems with stochastic variables specified only by their distribution and without user control of their value. SAT solver is the result of the project done by Simon Cruanes, and the first version of Stochastic subpackage was the result of the work of another student Sreenivas Kartik Buddha. We would like to thank them for their help in developing JaCoP.
We forgot to mention that JaCoP was again awarded a silver price in the fixed category among the elligible solvers for the awards in the Minizinc Challenge 2011.
We would like to thank Peter Stuckey and his team for all their work in running an interesting competition. It is gratifying to see that our focus on global constraints and modeling expressiveness is paying off. We hope that for the next year competition we will have even more problems with global constraints like network flow constraint, knapsack constraint, and geost constraint which have not been used yet in this year solver competition.
If anybody would like to have access to the upcoming version of JaCoP containing SAT and Stochastic package please contact me for details at ( radoslaw dot szymanek at gmail dot com ).
Radoslaw Szymanek and Krzysztof Kuchcinski
Scala programming language gets more and more acceptance in the community. It compiles to Java byte code and is executed using JVM. This makes it possible to use JaCoP directly in Scala by importing its packages. However, since Scala offers a nice way to define domain specific languages (DSL), we have developed an experimental implementation of the DSL for JaCoP solver.
The implementation (available on Sourceforge) is still experimental and we will be happy to get comments and proposals for improvements. We hope that this work will make it easier for Scala community to use JaCoP solver/CP technology.
Core Developer Team