Z3

问题 I'm experimenting with z3 in python. I have the following model: (set-option :produce-models true) (set-logic QF_AUFBV ) (declare-fun a () (Array (_ BitVec 32) (_ BitVec 8) ) ) (declare-fun another () (Array (_ BitVec 32) (_ BitVec 8) ) ) (assert (and (= false (= (_ bv77 32) (concat (select a (_ bv3 32) ) (concat (select a (_ bv2 32) ) (concat (select a (_ bv1 32) ) (select a (_ bv0 32) ) ) ) ) ) ) (= false (= (_ bv12 32) (concat (select another (_ bv3 32) ) (concat (select another (_ bv2 32)

问题 Z3 currently supports the DIMACS format for input. Is there any way to output the DIMACS format for the problem before solution? I mean converting the problem to a system CNFs and output it in a DIMACS format. If not, any ideas towards this direction would be more than helpful. 回答1: The DIMACS format is very primitive, it supports only Boolean variables. Z3 does not reduce every problem into SAT. Some problems are solved using a propositional SAT solver, but this is not the rule. This usually

问题 I am trying to solve a problem similar to the one here (Z3: finding all satisfying models) where I need to find all satisfying assignments of SMT formulae that contain set variables. I understand the idea in the link since it is similar to what some SAT solvers do to iterate through solutions: add the negation of the solution to obtain a different solution. However, when I use the set variables (which are implemented as arrays in Z3) the things get a little complicated or perhaps I miss

问题 In the Z3 tutorial, section 13.2.3, there is a nice example on how to reduce the number of patterns that have to be instantiated when dealing with the axiomatisation of injectivity. In the example, the function f that has to be stated injective, takes an object of type A as input and return an object of type B. As far as I understand the sorts A and B are disjunct. I have an SMT problem (FOL+EUF) on which Z3 seems not to terminate, and I am trying to isolate the cause. I have a function f:A-

问题 In the Z3 tutorial, section 13.2.3, there is a nice example on how to reduce the number of patterns that have to be instantiated when dealing with the axiomatisation of injectivity. In the example, the function f that has to be stated injective, takes an object of type A as input and return an object of type B. As far as I understand the sorts A and B are disjunct. I have an SMT problem (FOL+EUF) on which Z3 seems not to terminate, and I am trying to isolate the cause. I have a function f:A-

问题 The installation problems pointed out in an earlier question are still present. I have tried to install Z3 4.3.0 and 4.1 under Windows XP SP3 32-bit and under Windows 7 64-bit. None of the combinations work! I am able to do the " from z3 import * ", but the init() of the Z3 dll fails. My Python version is 2.7.3. Z3 stand-alone and Python stand-alone do work, but they don't work together without lots of complaints. It would help to get an up-to-date installation recipe which answers the

问题 One way to solve optimisation problems is to use an SMT solver to ask whether a (bad) solution exists, then to progressively add tighter cost constraints until the proposition is no longer satisfiable. This approach is discussed in, for example, http://www.lsi.upc.edu/~oliveras/espai/papers/sat06.pdf and http://isi.uni-bremen.de/agra/doc/konf/08_isvlsi_optprob.pdf. Is this approach efficient , though? i.e. will the solver re-use information from previous solutions when attempting to solve

问题 I have a question regarding how Z3 incrementally solves problems. After reading through some answers here, I found the following: There are two ways to use Z3 for incremental solving: one is push/pop(stack) mode, the other is using assumptions. Soft/Hard constraints in Z3. In stack mode, z3 will forget all learned lemmas in global ( am I right? ) scope even after one local "pop" Efficiency of constraint strengthening in SMT solvers In assumptions mode (I don't know the name, that is the name

问题 Wherefore? The usecase context in which my problem occures I define 3 random item of a triangle. Microsoft Z3 should output: Are the constraints satisfiabe or are there invalid input values? A model for all the other triangle items where all the variables are assigned to concrete values. In order to constrain the items i need to assert triangle equalities - i wanted to start out with the Pythagorean Theorem ( (h_c² + p² = b²) ^ (h_c² + q² = a²) ). The Problem I know that Microsoft Z3 has only

问题 In Z3, the following is clearly evaluated to a maximum of 2, with the model x=true and y=true. (declare-const x Bool) (declare-const y Bool) (declare-const z Bool) (assert(= z false)) (maximize( + (ite (= x true) 1 0) (ite (= y true) 1 0) (ite (= z true) 1 0) ) ) (check-sat) (get-model) How could I implement this using the C/C++ APIs? I've tried simply parsing using this: Z3_ast parsed = Z3_parse_smtlib2_string(c,<The above Z3>,0,0,0,0,0,0); z3::expr simpleExample(c, parsed); s.add