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https://github.com/Z3Prover/z3
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85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
# RC2 algorithm
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# basic version without optimizations
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# See also https://github.com/pysathq/pysat and papers in CP 2014, JSAT 2015.
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from z3 import *
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def tt(s, f):
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return is_true(s.model().eval(f))
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def add(Ws, f, w):
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Ws[f] = w + (Ws[f] if f in Ws else 0)
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def sub(Ws, f, w):
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w1 = Ws[f]
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if w1 > w:
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Ws[f] = w1 - w
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else:
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del(Ws[f])
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class RC2:
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def __init__(self, s):
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self.bounds = {}
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self.solver = s
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self.solver.set("sat.cardinality.solver", True)
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self.solver.set("sat.core.minimize", True)
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self.solver.set("sat.core.minimize_partial", True)
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def at_most(self, S, k):
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fml = AtMost(S + [k])
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name = Bool("%s" % fml)
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self.solver.add(Implies(name, fml))
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self.bounds[name] = (S, k)
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return name
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# Ws are weighted soft constraints
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# Whenever there is an unsatisfiable core over ws
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# increase the limit of each soft constraint from a bound
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# and create a soft constraint that limits the number of
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# increased bounds to be at most one.
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def maxsat(self, Ws):
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cost = 0
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Ws0 = Ws.copy()
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while unsat == self.solver.check([f for f in Ws]):
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core = list(self.solver.unsat_core())
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print (self.solver.statistics())
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print("Core", core)
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if not core:
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return unsat
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w = min([Ws[c] for c in core])
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cost += w
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for f in core:
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sub(Ws, f, w)
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for f in core:
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if f in self.bounds:
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S, k = self.bounds[f]
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if k + 1 < len(S):
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add(Ws, self.at_most(S, k + 1), w)
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add(Ws, self.at_most([mk_not(f) for f in core], 1), w)
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return cost, { f for f in Ws0 if tt(self.solver, f) }
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def from_file(self, file):
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opt = Optimize()
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opt.from_file(file)
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self.solver.add(opt.assertions())
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obj = opt.objectives()[0]
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Ws = {}
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for f in obj.children():
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assert(f.arg(1).as_long() == 0)
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add(Ws, f.arg(0), f.arg(2).as_long())
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return self.maxsat(Ws)
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def main(file):
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s = SolverFor("QF_FD")
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rc2 = RC2(s)
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set_param(verbose=0)
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cost, trues = rc2.from_file(file)
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print(cost)
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print(s.statistics())
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# main(<myfile>)
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