smt

I am not sure that this is possible using SMT-LIB, if it is not possible does an alternative solver exist that can do it? Consider the equations a < 10 and a > 5 b < 5 and b > 0 b < c < a with a , b and c integers The values for a and b where the maximum number of model exist that satisfy the equations when a=9 and b=1 . Do SMT-LIB support the following: For each values of a and b count the number of models that satisfy the formulas and give the value for a and b that maximize the count. Let's break down your goals: You want to enumerate all possible ways in which a and b (...and more) can be

In fact, does the SMT-LIB standard have a rational (not just real) sort? Going by its website , it does not. If x is a rational and we have a constraint x^2 = 2, then we should get back ``unsatisfiable''. The closest I could get to encoding that constraint is the following: ;;(set-logic QF_NRA) ;; intentionally commented out (declare-const x Real) (assert (= (* x x) 2.0)) (check-sat) (get-model) for which z3 returns a solution, as there is a solution (irrational) in the reals. I do understand that z3 has its own rational library, which it uses, for instance, when solving QF_LRA constraints

I'm looking at doing some verification work where I've got regular tree grammars as an underlying theory. Z3 lets you define your own stuff with uninterpreted functions, but that doesn't tend to work well any time your decision procedures are recursive. They used to allow for plugins but that has been depricated, I think. I'm wondering, does anybody have a recommendation of a decent SMT solver that allows you to write decision procedures for custom theories? There are several options given that most reasonable SMT solvers are open source you can integrate theory solvers in any detail depending

Let's say I have a z3 solver with a certain number of asserted constraints that are satisfiable. Let S be a set of constraints, I would like to verify for every constraint in S whether the formula is still satisfiable when adding the constraint to the solver. This can be easily done sequentially in such a fashion: results = [] for constraint in S: solver.push() solver.add(constraint) results.append(solver.check() == z3.sat) solver.pop() print all(results) Now, I would like to parallelize this to speed things up, but I'm not sure how to do it properly with z3. Here is an attempt. Consider the

I am experimenting with Z3 where I combine the theories of arithmetic, quantifiers and equality. This does not seem to be very efficient, in fact it seems to be more efficient to replace the quantifiers with all instantiated ground instances when possible. Consider the following example, in which I have encoded the unique names axiom for a function f that takes two arguments of sort Obj and returns an interpreted sort S . This axiom states that each unique list of arguments to f returns a unique object: (declare-datatypes () ((Obj o1 o2 o3 o4 o5 o6 o7 o8))) (declare-sort S 0) (declare-fun f