add rc2 sample

Signed-off-by: Nikolaj Bjorner <nbjorner@microsoft.com>

examples/python/rc2.py

@@ -0,0 +1,84 @@
+# RC2 algorithm
+# basic version without optimizations
+# See also https://github.com/pysathq/pysat and papers in CP 2014, JSAT 2015.
+
+from z3 import *
+
+def tt(s, f):
+    return is_true(s.model().eval(f))
+
+
+def add(Ws, f, w):
+    Ws[f] = w + (Ws[f] if f in Ws else 0)
+
+def sub(Ws, f, w):
+    w1 = Ws[f]
+    if w1 > w:
+        Ws[f] = w1 - w
+    else:
+        del(Ws[f])
+
+class RC2:
+    def __init__(self, s):
+        self.bounds = {}
+        self.solver = s
+        self.solver.set("sat.cardinality.solver", True)
+        self.solver.set("sat.core.minimize", True)
+        self.solver.set("sat.core.minimize_partial", True)
+        
+    def at_most(self, S, k):
+        fml = AtMost(S + [k])
+        name = Bool("%s" % fml)
+        self.solver.add(Implies(name, fml))
+        self.bounds[name] = (S, k)
+        return name
+
+    # Ws are weighted soft constraints
+    # Whenever there is an unsatisfiable core over ws
+    # increase the limit of each soft constraint from a bound
+    # and create a soft constraint that limits the number of
+    # increased bounds to be at most one.
+    def maxsat(self, Ws):
+        cost = 0
+        Ws0 = Ws.copy()
+        while unsat == self.solver.check([f for f in Ws]):
+            core = list(self.solver.unsat_core())
+            print (self.solver.statistics())
+            print("Core", core)
+            if not core:
+                return unsat
+            w = min([Ws[c] for c in core])
+            cost += w
+            for f in core:
+                sub(Ws, f, w)
+            for f in core:
+                if f in self.bounds:
+                    S, k = self.bounds[f]
+                    if k + 1 < len(S):
+                        add(Ws, self.at_most(S, k + 1), w)                
+            add(Ws, self.at_most([mk_not(f) for f in core], 1), w)
+            
+        return cost, { f for f in Ws0 if tt(self.solver, f) }
+
+    def from_file(self, file):
+        opt = Optimize()
+        opt.from_file(file)
+        self.solver.add(opt.assertions())
+        obj = opt.objectives()[0]
+        Ws = {}        
+        for f in obj.children():
+            assert(f.arg(1).as_long() == 0)
+            add(Ws, f.arg(0), f.arg(2).as_long())
+        return self.maxsat(Ws)
+
+
+def main(file):
+    s = SolverFor("QF_FD")
+    rc2 = RC2(s)
+    set_param(verbose=0)
+    cost, trues = rc2.from_file(file)
+    print(cost)
+    print(s.statistics())
+
+# main(<myfile>)
+