bindings to lgl (a SAT solver)

## Project description

## Abstract

Lingeling is an efficient SAT solver written by Armin Biere in pure C. This package provides efficient Python bindings to Lingeling on the C level, i.e., when importing pylgl, the lingeling solver becomes part of the Python process itself. For ease of deployment, the lingeling source files are included in this project. These files have been extracted from lingeling version bbe-6fe9691-170131.

## Usage

The `pylgl` module has two functions `solve` and `itersolve`,
both of which take an iterable of clauses as an argument. Each clause
is itself represented as an iterable of (non-zero) integers.

- The function
`solve`returns one of the following: - one solution (a list of integers)
- the string “UNSAT” (when the clauses are unsatisfiable)
- the string “UNKNOWN” (when a solution could not be determined within the propagation limit)

The function `itersolve` returns an iterator over solutions. When the
propagation limit is specified, exhausting the iterator may not yield all
possible solutions.

- Both functions take the following keyword arguments:
`vars`: number of variables (integer)`verbose`: the verbosity level (integer)`simplify`: enable simplification (integer)`seed`: random number generator seed (integer)`randec`: enable random decisions order (integer)`randecint`: random decision order interval (integer)`randphase`: enable random decision phases (integer)`randphaseint`: random decision phases interval (integer)

## Example

Let us consider the following clauses, represented using the DIMACS cnf 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_{1} or not x_{5} or x_{4}).
Note that the variable x_{2} is not used in any of the clauses,
which means that for each solution with x_{2} = True, we must
also have a solution with x_{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^{th} variable:

>>> import pylgl >>> cnf = [[1, -5, 4], [-1, 5, 3, 4], [-3, -4]] >>> pylgl.solve(cnf) [1, -2, -3, -4, 5]

This solution translates to: x_{1} = x_{5} = True,
x_{2} = x_{3} = x_{4} = False

To find all solutions, use `itersolve`:

>>> for sol in pylgl.itersolve(cnf): ... print sol ... [1, -2, -3, -4, 5] [1, -2, -3, 4, -5] [1, -2, -3, 4, 5] ... >>> len(list(pylgl.itersolve(cnf))) 18

In this example, there are a total of 18 possible solutions, which had to
be an even number because x_{2} was left unspecified in the clauses.

The fact that `itersolve` returns an iterator, makes it very elegant
and efficient for many types of operations. For example, using
the `itertools` module from the standard library, here is how one
would construct a list of (up to) 3 solutions:

>>> import itertools >>> list(itertools.islice(pylgl.itersolve(cnf), 3)) [[1, -2, -3, -4, 5], [1, -2, -3, 4, -5], [1, -2, -3, 4, 5]]

## Implementation of itersolve

How does one go from having found one solution to another solution?
The answer is surprisingly simple. One adds the *inverse* of the already
found solution as a new clause. This new clause ensures that another
solution is searched for, as it *excludes* the already found solution.
Here is basically a pure Python implementation of `itersolve` in terms
of `solve`:

def py_itersolve(clauses): # don't use this function! while True: # (it is only here to explain things) sol = pylgl.solve(clauses) if isinstance(sol, list): yield sol clauses.append([-x for x in sol]) else: # no more solutions -- stop iteration return

This implementation has several problems. Firstly, it is quite slow as
`pylgl.solve` has to convert the list of clauses over and over and over
again. Secondly, after calling `py_itersolve` the list of clauses will
be modified. In pylgl, `itersolve` is implemented on the C level,
making use of the lingeling C interface (which makes it much, much faster
than the naive Python implementation above).

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