I am working on a complex mapping problem. To get all Pareto-optimal solutions I used Pareto mod of Z3. Some wired things happened when I deleted some constraints which should be no effect on solutions, but the solutions were one less. When the last one should be found Z3 output "CUT 2" and didn't find the last solution.
So what means of "CUT 2"? Does it have anything to do with solution missing?
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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??
SMT solvers usually do not allow for these sorts of "hints" to be given, they act more as black-boxes.
Having said that, each solver uses a ton of internal heuristics, and z3 itself has a number of settings that you can play with to give it hints. If you run:
z3 -pd
it will display all the options you can provide, and there are literally over 600 of them! Unfortunately, these options are not really well documented, and how they impact the solver is rather cryptic. The only reliable way to find out would be to study the source code and see what they do, which isn't for the faint of heart. But in any case, it will not be as obvious as the branch feature you cite for gecode.
There are, however, other tricks one can use to speed up solving for SMT-solvers, unfortunately, these things are usually very problem-specific. If you post specific instances, you might get better suggestions.
I have some constraints involving Bitvectors that I believe should be sat even though Z3 produces the verdict unsat. I have managed to reduce them to a small example.
I tried tracing the solver by running z3 -tr:sat test.smt but did not get any traces (it just says unsat). Any ideas why this doesn't work, or an alternative to debugging this type of situation?
You might want to label your constraints and then ask for an unsat core. This will allow you to see which (hopefully a small) set of constraints are found conflicting and debug from there. If you post your example, we can help with setting it up for unsat-core production.
I'm having trouble figuring out how to debug z3. Is there a way to see what the SMT engine is "trying" to make it easier to understand why it's failing to see a solution that seems obvious and where it's devoting it's time instead?
As an example in my particular circumstance, I'm working with a recursive function and setting z3 to find inputs where the function has a certain result. SMT was timing out, yadda yadda yadda, turns out the thing I was recursing on had a base case of 0, but if it ever went negative, it'd recurse forever. Z3 didn't know not to pick a negative number, so it'd get stuck. I figured that out by staring at the code, but if I had some output somewhere that said "trying i == -10, trying i == -11, etc" it'd be very obvious what was going wrong.
I'm continuing to have less obvious issues, and I suspect Z3 is still getting stuck in loops. How can I see the loop it's getting stuck in?
It is unfortunately very difficult to find out why exactly Z3 is running forever, but typical culprits are matching loops due to bad patterns (a quantifier instantiations yields new ground terms that trigger another instantiations, and so on) and non-linear arithmetic.
The Z3 axiom profiler, described in this paper can help with identifying problems due to too many quantifier instantiations.
Here x and k are integers
and the formula is: (50<=x+k Ʌ x+k<51)
Can it get simplified to "x+k=50"
I want the right set of tactics to solve this conjunction of inequalities.
Z3 is not a general purpose symbolic engine to simplify such expressions. Even if you got a good combination of tactics to give you what you need today, the results might change in further releases of the tool. You should look at other systems. Even a symbolic-engine like wolfram-alpha may not produce what you exactly want; but it might give you some alternative forms that might be easier to work with. See here: http://www.wolframalpha.com/input/?i=50%3C%3Dx%2Bk+%26%26+x%2Bk%3C51
I'm trying to make z3 (I'm using z3py) to simplify some formulas for me (so that I can have more or less human-readable output). Using ctx-solver-simplify tactic seemed a good choice for me since in a couple of passes it would produce nice compact formulas. But soon I've run into a situation when the output of ctx-solver-simplify does not seem to be equivalent to the original formula (it looks more like being satisfiability-equivalent or so). Also, it might be the case that I'm not dealing with tactics correctly.
Here's what I was trying to do: http://rise4fun.com/Z3Py/g5sX. So, I construct a formula Set2 (everything before the definition of Set2 is just a setup needed to define it) which has a particular satisfying assignment. After applying ctx-solver-simplify, I get a single formula (as a goal) for which this assignment is not satisfying. So what am I dong wrong?
Am I wrong assuming that ctx-solver-simplify would produce an equivalent formula?
Am I handling the tactics and their output in the wrong way?
Anything else?
Thanks.
I have been looking into this, but have so far been unable to reproduce the bug directly
with our current branch. A bug in the context simplifier was fixed a little while ago, and it could
be manifesting itself with the online version of Z3.
There are still a few things I can do to double check if we can reproduce the bug
and I will update this post with what I find.