PyCSP3

# PyCSP3

PyCSP3 is a Python library that allows us to write models of combinatorial constrained problems in a declarative manner. Currently, with PyCSP3, you can write models of constraint satisfaction and optimization problems. More specifically, you can build models for:

• CSP (Constraint Satisfaction Problem)
• COP (Constraint Optimization Problem)

In this website, you will find all that you need to know about Constraint Programming (CP) and PyCSP3, with more than 60 Jupyter Notebooks:

You can also read this guide.

As an illustration, below, you can find two simple examples of PyCSP3 models:

#### AllInterval Problem


from pycsp3 import *

n = data or 8

# x[i] is the ith note of the series
x = VarArray(size=n, dom=range(n))

satisfy(
# notes must occur once, and so form a permutation
AllDifferent(x),

# intervals between neighbouring notes must form a permutation
AllDifferent(abs(x[i] - x[i + 1]) for i in range(n - 1)),

# tag(symmetry-breaking)
x < x[n - 1]
)



#### Golomb Ruler Problem


from pycsp3 import *

n = data or 10

# x[i] is the position of the ith tick
x = VarArray(size=n, dom=range(n * n))

satisfy(
# all distances are different
AllDifferent(abs(x[i] - x[j]) for i, j in combinations(range(n), 2)),

# tag(symmetry-breaking)
[x == 0, Increasing(x, strict=True)]
)

minimize(
# minimizing the position of the rightmost tick
x[-1]
)


### Last News

November 10, 2022
PyCSP3 2.1, with new versions of embedded solvers (ACE and Choco) and additional constraints (Precedence, BinPacking, Knapsack, MinimumArg, MaximumArg). See other extensions in Changelog Chapter of the guide.
November 10, 2022
Specifications of XCSP3, Version 3.1
August 3, 2022
2022 XCSP3 Competition, held with FLoC 2022 (Olympic Games); see the results.
December 15, 2021
PyCSP3 2.0 , Version 2 of the Python library PyCSP3.
December 15, 2021
ACE 2.0 , Version 2 of the generic constraint solver ACE (written in Java).
January 16, 2021
Specifications of XCSP3, Version 3.0.7

## Requirements

• For building and compiling models, you need:
• Python 3.6 (at least) to be installed
• Python package PyCSP3 to be installed, for example from PyPi: pip install pycsp3
• For running the embedded solvers (ACE and Choco), you need Java 11 to be installed

Importantly, there is a complete separation between the modeling and solving phases: you write a model, you compile it (while providing some data) in order to generate an XCSP3 instance (file), and you solve that problem instance by means of a constraint solver. You can also directly pilot the solving procedure in PyCSP3 possibly conducting an incremental solving strategy.

In a nutshell, the main ingredients of the complete tool chain we propose for handling combinatorial constrained problems are:

• PyCSP3: a Python library for modelling constrained problems, which is described in this document (or equivalently, JvCSP3, a Java-based API)
• XCSP3: an intermediate format used to represent problem instances while preserving structure of models

A shown in the figure below, the user who wishes to solve a combinatorial constrained problem has to:

• write a model using the Python library PyCSP3 (i.e., write a Python file), or the Java modeling API JvCSP3 (i.e., write a Java file)
• provide a data file (in JSON format) for a specific problem instance to be solved
• compile both files (model and data) so as to generate an XCSP3 instance (file)
• solve the XCSP3 file (problem instance under format XCSP3) by using a constraint solver as, e.g., ACE, Choco, OscaR or PicatSAT 