I am using Z3 for proving the robustness of schedules obtained for real time task systems. When I check this script http://www.cs.ru.nl/~georgeta/script.smt2 I get an unsat response. However, when I use the PROOF_MODE=1 option, the response is sat. What could possibly go wrong in the former case?
I downloaded your example. The specified logic is incorrect, command:
(set-logic QF_AUFLIA)
This logic specifies that the script will contain only arrays, uninterpreted functions and integer variables, and no quantifiers. However, it contains Real variables.
If you remove this command, you will get the correct answer (sat) in both cases.
You got a different answer when using PROOF_MODE=1 because some preprocessors in Z3 do not support proof generation, then they are disabled when proof generation is turned on.
That being said, we fixed many bugs in Z3 2.19. The new version 3.0 will be released soon.
You can already use the pre-release version we submitted to SMT-COMP.
Related
Consider the following smt2 file generated with the help of Klee.
I am trying to evaluate it using z3. However, z3 hangs forever. Specifically, when the formula is UNSAT, z3 runs for ever and does not produce any result.
Is formula size is big?
Is there any issue while using logic theory AUFBV?
May I get some suggestions to improve the z3 performance.
Each assert statement having some common subexpression. Is it possible to improve the z3 performance by solving subexpression separately?
This is going to be impossible to answer as the SMT-lib file you've linked is undecipherable for a non-KLEE user. I recommend asking KLEE folks directly using your original program that gave rise to this. Failing that, try to reduce the SMT2Lib to a minimum and see if you can at least hand-annotate to see what it's trying to do.
Regarding your question for common subexpressions: You have to experiment to find out. But the way most such solvers are constructed, they /will/ discover common subexpressions themselves and reuse lemmas about them automatically as they convert your input to an internal representation. So, it'd surprise me if it helped in any significant way to do this by hand; unless the input is really massive. (The example you linked isn't really that big so I doubt that's an issue.)
I am using z3py I have a predicate over two integers that needs to be evaluated using a custom algorithm. I have been trying to get it implemented, without much success. Apparently, what I need is a procedural attachment, which is now deprecated. Could anybody tell me how I might impelement this in z3py? I understand that it involves use of Tactics, but I am afraid I haven't managed to figure out how to use them. I wouldn't mind using the deprecated way either, as long as it works.
There is no procedural attachment tactic. All tactics are implemented inside of Z3;
you can compose tactics from outside.
Previous versions of Z3 exposed a way to register a "user theory".
This was deprecated since (1) the source of Z3 is now available so users can compile with their custom theories directly, (2) the user-theory abstraction lacked proper support
for model generation. You can of course try previous versions of Z3 that have the user theory extension, but it is not supported.
I am using Z3's python api to do some kind of incremental solving. I push constraints to the solver iteratively while checking for unsatisfiability at each step using solver.push() command. I want to understand whether Z3 would use the learned lemmas from previous constraints or the satisfying solution previously obtained when solving with a newly added constraint. I never use the solver.pop() command. Where can I get more details about how the work done in previous iterations is used?
Z3 has multiple solvers, but only one of them really supports incremental solving and reuse work from previous calls. By default, Z3 will automatically switch to the incremental solver whenever you execute a solver.push(). This solver alsos reuse previously learned clauses. The learned clauses are deleted when a solver.pop() is executed. Z3 also support another mechanism for incremental solving that is not based on push and pop. Here are some related posts:
Soft/Hard constraints in Z3
How to use z3 incrementally and model without propositional value ？
Incremental calls to Z3 on UFBV with and without push calls
I am using Z3 to solve the path conditions produced by a symbolic executor, which explores the state space in depth-first order, quite similarly to CUTE, DART or (possibly) SAGE. We are experimenting different ways of using Z3. At one extreme, we send every query to Z3 and (reset) it right after. At the other, we (push) every additional branch constraint, and (pop) (pop) upon backtrack the minimum necessary to correctly weaken the path condition. The problem is, no strategy seems to work better than any other in all the circumstances. Pushing seems to offer the best advantage, but we met a few cases where resetting Z3 after every query is more than one order of magnitude faster than doing push/pop. Note that communication overhead is negligible: almost all the time is spent inside check-sat.
Does anyone have any experience to share, or some indication on the state kept internally by Z3 (lemmas, etc), which can help clarifying its behavior? And what about the behavior of other SMT solvers?
The next release (v4.3.2) will expose a feature that may be useful for you. In Z3, the default solver combines a non-incremental solver and an incremental one. When push/pop are used (or multiple checks are used without invoking reset), Z3 will use the incremental solver. In the next release, we can provide a timeout for the incremental solver. If the incremental solver can't solve the problem in the given timeout, Z3 will automatically switch to the non-incremental one. Perhaps, if you use this feature, you will be able to get the best of "both worlds". To get the source code for the next release candidate, you should use
git clone https://git01.codeplex.com/z3 -b rc
To compile it, we have use
cd z3
python scripts/mk_make.py
cd build
make
To set the timeout for the incremental solver, we have to provide the following command line option:
combined_solver.solver2_timeout=<time in milliseconds>
If you are using the programmatic APIs, you can the new API:
Z3_global_param_set(Z3_string param_id, Z3_string param_value)
Note that, the next release will have a new framework for setting parameters. It allows the user to set parameters for internal Z3 modules.
Is it possible to serialize/deserialize a Z3 context (from C#)?
If not, is this feature planned ?
I think this feature is important for real world applications.
This is not directly supported in the current API. The next release will support multiple solvers, and we will provide commands for copying the assertions from one solver to another, and retrieving the assertions. With these commands, one can implement serialization by dumping the expressions in a file (in SMT 2.0 format). To deserialize, we just read the file back.
Note that, this solution can already be implemented using the current API if you keep track of the assertions you asserted into the logical context.
That being said, I've seen the following approach used in many projects that use Z3. They have their own representation for formulas. When they invoke Z3, they translate their representation into Z3's representation. In most cases the performance overhead is minimal. This approach gives them a lot of flexibility. Serialization is a good example. Some programming environment (e.g., Python) already provide some built-in support for serialization.