I'm trying to influence the randomness of results for model values generated by Z3. As far as I understand, the options for this are very limited: in case of linear arithmetic, the simplex solver does not allow for random results that still satisfy the given constraints. However, there is an option smt.arith.random_initial_value ("use random initial values in the simplex-based procedure for linear arithmetic (default: false)") which I don't seem to get working:
from z3 import *
set_option('smt.arith.random_initial_value',True)
x = Int('x')
y = Int('y')
s = Solver()
s.add( x+y > 0)
s.check()
s.model()
This seems to always produce [y = 0, x = 1] as a result. Even model completion for variables unused in the given constraints seems to produce deterministic results all the time.
Any ideas or hints about how this option works?
Thanks for catching that! There was indeed a bug that caused the random seed not to be passed through to the arithmetic theory. This is now fixed in the unstable branch (fix here).
This example:
(set-option :smt.arith.random_initial_value true)
(declare-const x Int)
(declare-const y Int)
(assert (> (+ x y) 0))
(check-sat-using (using-params qflra :random_seed 1))
(get-model)
(check-sat-using (using-params qflra :random_seed 2))
(get-model)
(check-sat-using (using-params qflra :random_seed 3))
(get-model)
Now produces three different models:
sat
model
(define-fun y () Int
4294966763)
(define-fun x () Int
4294966337)
)
sat
(model
(define-fun y () Int
216)
(define-fun x () Int
4294966341)
)
sat
(model
(define-fun y () Int
196)
(define-fun x () Int
4294966344)
)
It looks like there may be another place where this option isn't passed through correctly (e.g., when using set-logic instead of calling the qflra tactic directly), we're still looking into that.
来源:https://stackoverflow.com/questions/24327987/z3-randomness-of-generated-model-values