pycryptosat SAT solver
This directory provides Python bindings to CryptoMiniSat on the C++ level, i.e. when importing pycryptosat, the CryptoMiniSat solver becomes part of the Python process itself.
Installing
pip install pycryptosat
Building from source
The build uses scikit-build-core,
which drives CMake under the hood. CMake automatically fetches and builds the
required cadical and cadiback dependencies, so no manual dependency
installation is needed beyond GMP.
Quick start (Linux)
sudo apt-get install libgmp-dev # or: yum install gmp-devel
python -m venv venv
source venv/bin/activate
pip install scikit-build-core cmake ninja build
pip install . --no-build-isolation
Quick start (macOS)
brew install gmp
python -m venv venv
source venv/bin/activate
pip install scikit-build-core cmake ninja build
pip install . --no-build-isolation
Build a wheel without installing
python -m venv venv
source venv/bin/activate
pip install scikit-build-core cmake ninja build
python -m build --wheel --no-isolation # wheel lands in dist/
The resulting wheel is fully self-contained on macOS (GMP is bundled by
delocate). On Linux the wheel depends on libgmp.so.10, which is part of
the manylinux ABI and is present on any standard distribution.
Usage
The pycryptosat module has one object, Solver, with the following methods:
solve, add_clause, add_clauses, add_xor_clause, nb_vars,
is_satisfiable, and get_conflict.
The function add_clause() takes an iterable list of literals such as
[1, 2] which represents the truth 1 or 2 = True. For example,
add_clause([1]) sets variable 1 to True.
The function solve() solves the system of equations that have been added
with add_clause():
>>> from pycryptosat import Solver
>>> s = Solver()
>>> s.add_clause([1, 2])
>>> sat, solution = s.solve()
>>> print(sat)
True
>>> print(solution)
(None, True, True)
The return value is a tuple. First part of the tuple indicates whether the
problem is satisfiable. In this case, it's True, i.e. satisfiable. The second
part is a tuple contains the solution, preceded by None, so you can index into
it with the variable number. E.g. solution[1] returns the value for
variable 1.
The solve() method optionally takes an argument assumptions that
allows the user to set values to specific variables in the solver in a temporary
fashion. This means that in case the problem is satisfiable but e.g it's
unsatisfiable if variable 2 is FALSE, then solve([-2]) will return
UNSAT. However, a subsequent call to solve() will still return a solution.
If instead of an assumption add_clause() would have been used, subsequent
solve() calls would have returned unsatisfiable.
Solver takes the following keyword arguments:
verbose: the verbosity level (integer, default 0)time_limit: the time limit in seconds (float)confl_limit: the conflict limit (integer)threads: the number of threads to use (integer, default 1)
Both time_limit and confl_limit set a budget to the solver. The former is based on time elapsed while the former is based on number of conflicts met during search. If the solver runs out of budget, it returns with (None, None). If both limits are used, the solver will terminate whenever one of the limits are hit (whichever first). Warning: Results from time_limit may differ from run to run, depending on compute load, etc. Use confl_limit for more reproducible runs.
Example
Let us consider the following clauses, represented using
the DIMACS cnf <http://en.wikipedia.org/wiki/Conjunctive_normal_form>_
format::
p cnf 5 3
1 -5 4 0
-1 5 3 4 0
-3 -4 0
Here, we have 5 variables and 3 clauses, the first clause being
(x\ :sub:1 or not x\ :sub:5 or x\ :sub:4).
Note that the variable x\ :sub:2 is not used in any of the clauses,
which means that for each solution with x\ :sub:2 = True, we must
also have a solution with x\ :sub:2 = False. In Python, each clause is
most conveniently represented as a list of integers. Naturally, it makes
sense to represent each solution also as a list of integers, where the sign
corresponds to the Boolean value (+ for True and - for False) and the
absolute value corresponds to i\ :sup:th variable::
>>> import pycryptosat
>>> solver = pycryptosat.Solver()
>>> solver.add_clause([1, -5, 4])
>>> solver.add_clause([-1, 5, 3, 4])
>>> solver.add_clause([-3, -4])
>>> solver.solve()
(True, (None, True, False, False, True, True))
This solution translates to: x\ :sub:1 = x\ :sub:4 = x\ :sub:5 = True,
x\ :sub:2 = x\ :sub:3 = False
Special CMake options
Extra compile definitions (e.g. LARGE_OFFSETS) can be passed via
SKBUILD_CMAKE_ARGS or added to pyproject.toml's [tool.scikit-build] cmake.args list.
1 maintainer
Metadata
1 maintainer
Release files for pycryptosat 5.15.0
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