z3py

问题 In constraint solver like Gecode , We can control the exploration of search space with help of branching function. for e.g. branch(home , x , INT_VAL_MIN ) This will start exploring the search space from the minimum possible value of variable x in its domain and try to find solution.(There are many such alternatives .) For z3, do we have this kind of flexibility in-built ?? Any alternative possible?? 回答1: SMT solvers usually do not allow for these sorts of "hints" to be given, they act more

问题 I tried to use bit vectors to get randomized results in model values like suggested by de Moura here but then with Z3Py instead of SMTLIB. I translated his example to: from z3 import * s = Solver() x = BitVec('x', 16) set_option('auto_config', False) set_option('smt.phase_selection',5) s.add(ULT(x,100)) s.check() s.model() s.check() s.model() However, the result seems to always be the same, i.e. - repetitive checking with s.check() does not alter the result. - even after a restart of the

问题 I tried to use bit vectors to get randomized results in model values like suggested by de Moura here but then with Z3Py instead of SMTLIB. I translated his example to: from z3 import * s = Solver() x = BitVec('x', 16) set_option('auto_config', False) set_option('smt.phase_selection',5) s.add(ULT(x,100)) s.check() s.model() s.check() s.model() However, the result seems to always be the same, i.e. - repetitive checking with s.check() does not alter the result. - even after a restart of the

问题 I tried to use bit vectors to get randomized results in model values like suggested by de Moura here but then with Z3Py instead of SMTLIB. I translated his example to: from z3 import * s = Solver() x = BitVec('x', 16) set_option('auto_config', False) set_option('smt.phase_selection',5) s.add(ULT(x,100)) s.check() s.model() s.check() s.model() However, the result seems to always be the same, i.e. - repetitive checking with s.check() does not alter the result. - even after a restart of the

问题 For example, I want to get the existing constraints from s and into the Optimize object. from z3 import * a = Int('a') x = Int('x') b = Array('I', IntSort(), IntSort()) s = Solver() s.add(a >= 0) s.add(x == 0) s.add(Select(b, 0) == 10) s.add(Select(b, x) >= a) opt = Optimize() opt.add(s.constraints) obj1 = opt.maximize(a) obj2 = opt.minimize(a) opt.set('priority', 'box') # Setting Boxed Multi-Objective Optimization is_sat = opt.check() assert is_sat print("Max(a): " + str(obj1.value())) print

问题 For example, I want to get the existing constraints from s and into the Optimize object. from z3 import * a = Int('a') x = Int('x') b = Array('I', IntSort(), IntSort()) s = Solver() s.add(a >= 0) s.add(x == 0) s.add(Select(b, 0) == 10) s.add(Select(b, x) >= a) opt = Optimize() opt.add(s.constraints) obj1 = opt.maximize(a) obj2 = opt.minimize(a) opt.set('priority', 'box') # Setting Boxed Multi-Objective Optimization is_sat = opt.check() assert is_sat print("Max(a): " + str(obj1.value())) print

问题 For example, I want to get the existing constraints from s and into the Optimize object. from z3 import * a = Int('a') x = Int('x') b = Array('I', IntSort(), IntSort()) s = Solver() s.add(a >= 0) s.add(x == 0) s.add(Select(b, 0) == 10) s.add(Select(b, x) >= a) opt = Optimize() opt.add(s.constraints) obj1 = opt.maximize(a) obj2 = opt.minimize(a) opt.set('priority', 'box') # Setting Boxed Multi-Objective Optimization is_sat = opt.check() assert is_sat print("Max(a): " + str(obj1.value())) print

问题 from z3 import * import random a = Int('a') b = Int('b') s = Tactic('qflra').solver() s.add(a > 10) set_option('smt.arith.random_initial_value', True) set_option('smt.random_seed', random.randint(0, 2 ** 8)) while s.check() == sat: m = s.model() print m[a] s.add(a != m[a]) set_option('smt.random_seed', random.randint(0, 2 ** 8)) The result seems to be only randomed for a second... Then it just started to give consecutive numbers. 4294966399 4294966398 4294966397 4294966396 4294966395

问题 from z3 import * import random a = Int('a') b = Int('b') s = Tactic('qflra').solver() s.add(a > 10) set_option('smt.arith.random_initial_value', True) set_option('smt.random_seed', random.randint(0, 2 ** 8)) while s.check() == sat: m = s.model() print m[a] s.add(a != m[a]) set_option('smt.random_seed', random.randint(0, 2 ** 8)) The result seems to be only randomed for a second... Then it just started to give consecutive numbers. 4294966399 4294966398 4294966397 4294966396 4294966395

问题 Suppose I have a list of 10 variables v = [Real('v_%s' % (i+1)) for i in range(10)] and I want to add a simple constraint like this s = Solver() for i in range(10): s.add(v[i] == i) if s.check() == sat: print(s.model()) So a satisfying model is v_1 = 0, v_2 = 1 .... v_10 = 9 . However the output of print(s.model()) is totoally unordered which makes me confused when I have lots of variables in a bigger model. For this example, the output of my computer is v_5, v_7, v_4, v_2, v_1, v_3, v_6, v_8