CPMpy: Constraint Programming and Modeling in Python

CPMpy is a Constraint Programming and Modeling library in Python, based on numpy, with direct solver access.

Constraint Programming is a methodology for solving combinatorial optimisation problems like assignment problems or covering, packing and scheduling problems. Problems that require searching over discrete decision variables.

CPMpy allows to model search problems in a high-level manner, by defining decision variables and constraints and an objective over them (similar to MiniZinc and Essence’). You can freely use numpy functions and indexing while doing so. This model is then automatically translated to state-of-the-art solver like or-tools, which then compute the optimal answer.

Source code and bug reports at https://github.com/CPMpy/cpmpy

Getting started:


Problem: I get the following error:

"IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"

Solution: Indexing an array with a variable is not allowed by standard numpy arrays, but it is allowed by cpmpy-numpy arrays. First convert your numpy array to a cpmpy-numpy array with the cpm_array() wrapper:

1# x is a variable
2X = intvar(0,3)
4# Transforming a given numpy-array **m** into a CPMpy array
5m = cpm_array(m)
7# apply constraint
8m[X] == 8


This library is delivered under the MIT License, (see [LICENSE](https://github.com/tias/cppy/blob/master/LICENSE)).