https://github.com/paulk-asert/groovy-constraint-programming

Three common programming paradigms or styles are the imperative (OO & procedural), functional, and logic (or constraint) styles. This repo shows examples of the latter using the Apache Groovy programming language and other (mostly JVM) languages which support this style of programming (often using libraries).
https://github.com/paulk-asert/groovy-constraint-programming

constraint-programming groovy jvm-languages linear-programming programming-language

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Three common programming paradigms or styles are the imperative (OO & procedural), functional, and logic (or constraint) styles. This repo shows examples of the latter using the Apache Groovy programming language and other (mostly JVM) languages which support this style of programming (often using libraries).

• Host: GitHub
• URL: https://github.com/paulk-asert/groovy-constraint-programming
• Owner: paulk-asert
• License: apache-2.0
• Created: 2019-06-02T01:26:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-03-13T22:24:48.000Z (8 months ago)
• Last Synced: 2024-04-16T07:06:02.698Z (7 months ago)
• Topics: constraint-programming, groovy, jvm-languages, linear-programming, programming-language
• Language: Groovy
• Homepage: https://speakerdeck.com/paulk/groovy-constraint-programming
• Size: 27.4 MB
• Stars: 6
• Watchers: 4
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Groovy Constraint Programming

This repo contains examples from an Introduction to Constraint Programming talk:

https://speakerdeck.com/paulk/groovy-constraint-programming

You have 4 options to run the examples:

* [running locally](docs/RunningLocal.md) on the command-line or in an IDE

* [running via gitpod](docs/RunningGitpod.md)

* [running as a Jupyter/BeakerX](docs/RunningBeakerX.md) notebook

* [running in the Groovy Console or Groovy Web Console](docs/RunningConsole.md)

---

The [Pythagorean subproject](subprojects/Pythagorean/)

is an introductory example comparing an imperative style solution (using a brute-force approach), and

a constraint-programming solution (using Choco[1]).

![Pythagorean](docs/images/Pythagorean.png)

Run Choco solution via Jupyter/BeakerX:

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/paulk-asert/groovy-constraint-programming/master?filepath=subprojects%2FPythagorean%2Fsrc%2Fmain%2Fnotebook%2FPythagorean.ipynb)

Command-line arguments for Gradle to run the script:

`:Pythagorean:run`

---

The [McNuggets subproject](subprojects/McNuggets/)

illustrates solving a fairly simple Frobenius numbers puzzle

using Choco[1] and Ojalgo[2].

![McNuggets](docs/images/McNuggets.png)

Command-line arguments for Gradle to see the task names for the available solutions:

```

:McNuggets:tasks --group="Application"

```

Then pick one of those tasks to run, e.g. to run the Ojalgo solution:

```

:McNuggets:runOjalgo

```

---

## Cryptarithmetic puzzles

This example solves a classic cryptarithmetic puzzle using constraint programming.

The Groovy solution is contrasted with brute-force approaches and solutions in other JVM languages.

![Constraint programming](docs/images/cp_screenshot.png)

See the [SendMoreMoney subproject](subprojects/SendMoreMoney/) for all the details.

Constraint-programming libraries covered:

[Choco][]

Programming languages covered:

[Clojure][],

[Groovy][],

[Frege][],

[Java][],

[Nashorn][],

[JRuby][],

[Jython][],

[Kotlin][],

[Luaj][],

[Scala][],

[tuprolog][].

---

## Diet optimization

This example solves an optimization/linear programming problem.

Numerous technologies and approaches are used to solve the problem.

![Linear programming](docs/images/lp_screenshot.png)

See the [Diet subproject](subprojects/Diet/) for all the details.

Technologies illustrated:

[Apache Commons Math][],

[Hipparchus][],

[Choco][] (with and without ibex integration),

[JaCoP][] (using scalar-product/weighted-sum and knapsack algorithms),

[Ojalgo][],

[OptaPlanner][],

[OrTools][],

[SAS/OR][].

---

## Genetic algorithms

This example uses genetic algorithms to explore the

[infinite monkey theorem](https://en.wikipedia.org/wiki/Infinite_monkey_theorem).

![Chimpanzee at keyboard](docs/images/Chimpanzee.png)

See the [Monkeys subproject](subprojects/Monkeys/) for all the details.

Technologies illustrated:

[Apache Commons Math][],

[Jenetics][].

---

## Technology summary

Libraries used:

[Choco][],

[Hipparchus][],

[Apache Commons Math][],

[JaCoP][],

[OptaPlanner][],

[OrTools][],

[Jenetics][],

[SAS/OR][] (commercial product).

[Apache Commons Math]: https://commons.apache.org/proper/commons-math/

[Hipparchus]: https://hipparchus.org/

[Choco]: http://www.choco-solver.org/

[Ojalgo]: https://www.ojalgo.org/

[JaCoP]: https://github.com/radsz/jacop

[OptaPlanner]: https://www.optaplanner.org/

[OrTools]: https://developers.google.com/optimization "Google OR Tools"

[SAS/OR]: https://www.sas.com/en_us/software/or.html

[Jenetics]: https://jenetics.io/

[Clojure]: https://clojure.org/

[Groovy]: https://groovy-lang.org/

[Frege]: https://github.com/Frege/frege "JVM Haskell"

[Java]: https://www.java.com/

[Nashorn]: https://docs.oracle.com/javase/10/nashorn/ "JavaScript for JVM up to JDK"

[JRuby]: https://www.jruby.org/ "Ruby for the JVM"

[Jython]: https://www.jython.org/ "Python for the JVM"

[Kotlin]: https://kotlinlang.org/

[Luaj]: https://github.com/luaj/luaj "LUA for the JVM"

[Scala]: https://www.scala-lang.org/

[tuprolog]: http://apice.unibo.it/xwiki/bin/view/Tuprolog/ "A prolog for the JVM"

---