NUCS 2.2.0

A Numpy and Numba based Python library for solving Constraint Satisfaction Problems over finite domains

TLDR

NuCS is a Python library for solving Constraint Satisfaction and Optimization Problems. Because it is 100% written in Python, NuCS is easy to install and allows to model complex problems in a few lines of code. The NuCS solver is also very fast because it is powered by Numpy and Numba.

Installation

pip install nucs

Documentation

Check out NUCS documentation.

With NuCS, in a few seconds you can ...

Find all 14200 solutions to the 12-queens problem

NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.queens -n 12 --log_level=INFO

2024-11-12 17:24:49,061 - INFO - nucs.solvers.solver - Problem has 3 propagators
2024-11-12 17:24:49,061 - INFO - nucs.solvers.solver - Problem has 12 variables
2024-11-12 17:24:49,061 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses variable heuristic 0
2024-11-12 17:24:49,061 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses domain heuristic 0
2024-11-12 17:24:49,061 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses consistency algorithm 0
2024-11-12 17:24:49,061 - INFO - nucs.solvers.backtrack_solver - Choice points stack has a maximal height of 128
2024-11-12 17:24:49,200 - INFO - nucs.solvers.multiprocessing_solver - MultiprocessingSolver has 1 processors
{
    'OPTIMIZER_SOLUTION_NB': 0,
    'PROBLEM_FILTER_NB': 262011,
    'PROBLEM_SHAVING_NB': 0,
    'PROBLEM_SHAVING_CHANGE_NB': 0,
    'PROBLEM_SHAVING_NO_CHANGE_NB': 0,
    'PROPAGATOR_ENTAILMENT_NB': 0,
    'PROPAGATOR_FILTER_NB': 2269980,
    'PROPAGATOR_FILTER_NO_CHANGE_NB': 990450,
    'PROPAGATOR_INCONSISTENCY_NB': 116806,
    'SOLVER_BACKTRACK_NB': 131005,
    'SOLVER_CHOICE_NB': 131005,
    'SOLVER_CHOICE_DEPTH': 10,
    'SOLVER_SOLUTION_NB': 14200
}

Compute the 92 solutions to the BIBD(8,14,7,4,3) problem

NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.bibd -v 8 -b 14 -r 7 -k 4 -l 3 --symmetry_breaking --log_level=INFO

2024-11-12 17:26:39,734 - INFO - nucs.solvers.solver - Problem has 462 propagators
2024-11-12 17:26:39,734 - INFO - nucs.solvers.solver - Problem has 504 variables
2024-11-12 17:26:39,734 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses variable heuristic 0
2024-11-12 17:26:39,734 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses domain heuristic 1
2024-11-12 17:26:39,734 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses consistency algorithm 0
2024-11-12 17:26:39,734 - INFO - nucs.solvers.backtrack_solver - Choice points stack has a maximal height of 128
{
    'OPTIMIZER_SOLUTION_NB': 0,
    'PROBLEM_FILTER_NB': 1425,
    'PROBLEM_SHAVING_NB': 0,
    'PROBLEM_SHAVING_CHANGE_NB': 0,
    'PROBLEM_SHAVING_NO_CHANGE_NB': 0,
    'PROPAGATOR_ENTAILMENT_NB': 4711,
    'PROPAGATOR_FILTER_NB': 104392,
    'PROPAGATOR_FILTER_NO_CHANGE_NB': 73792,
    'PROPAGATOR_INCONSISTENCY_NB': 621,
    'SOLVER_BACKTRACK_NB': 712,
    'SOLVER_CHOICE_NB': 712,
    'SOLVER_CHOICE_DEPTH': 19,
    'SOLVER_SOLUTION_NB': 92
}

Demonstrate that the optimal 10-marks Golomb ruler length is 55

NUMBA_CACHE_DIR=.numba/cache python -m nucs.examples.golomb -n 10 --symmetry_breaking --log_level=INFO

2024-11-12 17:27:45,110 - INFO - nucs.solvers.solver - Problem has 82 propagators
2024-11-12 17:27:45,110 - INFO - nucs.solvers.solver - Problem has 45 variables
2024-11-12 17:27:45,110 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses variable heuristic 0
2024-11-12 17:27:45,110 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses domain heuristic 0
2024-11-12 17:27:45,110 - INFO - nucs.solvers.backtrack_solver - BacktrackSolver uses consistency algorithm 2
2024-11-12 17:27:45,110 - INFO - nucs.solvers.backtrack_solver - Choice points stack has a maximal height of 128
2024-11-12 17:27:45,172 - INFO - nucs.solvers.backtrack_solver - Minimizing variable 8
2024-11-12 17:27:45,644 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 80
2024-11-12 17:27:45,677 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 75
2024-11-12 17:27:45,677 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 73
2024-11-12 17:27:45,678 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 72
2024-11-12 17:27:45,679 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 70
2024-11-12 17:27:45,682 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 68
2024-11-12 17:27:45,687 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 66
2024-11-12 17:27:45,693 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 62
2024-11-12 17:27:45,717 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 60
2024-11-12 17:27:45,977 - INFO - nucs.solvers.backtrack_solver - Found a (new) solution: 55
{
    'OPTIMIZER_SOLUTION_NB': 10,
    'PROBLEM_FILTER_NB': 22652,
    'PROBLEM_SHAVING_NB': 0,
    'PROBLEM_SHAVING_CHANGE_NB': 0,
    'PROBLEM_SHAVING_NO_CHANGE_NB': 0,
    'PROPAGATOR_ENTAILMENT_NB': 107911,
    'PROPAGATOR_FILTER_NB': 2813035,
    'PROPAGATOR_FILTER_NO_CHANGE_NB': 1745836,
    'PROPAGATOR_INCONSISTENCY_NB': 11289,
    'SOLVER_BACKTRACK_NB': 11288,
    'SOLVER_CHOICE_NB': 11353,
    'SOLVER_CHOICE_DEPTH': 9,
    'SOLVER_SOLUTION_NB': 10
}
[ 1  6 10 23 26 34 41 53 55]