I'm aware that there is a simplex solver implemented in z3. Is it possible to use the solver for linear optimization? Where is the interface for the solver located in the z3 source code?
Yes, Z3 has a solver based on the Simplex method. It is implemented in the files src\smt\theory_arith*. The main files are src\smt\theory_arith.h and src\smt\theory_arith_core.h.
This solver has very basic support for optimization in the file src\smt\theory_arith_aux.h. This functionality is not "exposed" by the solver. It is used internally in the extensions/heuristics for integer and nonlinear arithmetic.
BTW, recall that Z3 solver is based on rational (precise) arithmetic. So, it is much slower than solvers based on floating point arithmetic. Moreover, this solver does not use the revised simplex method.
Related
Does z3's SAT solver(s) obtain a complete assignment to the propositional(ized) part of an SMT problem before doing a theory consistency check? In particular, I am curious to know what is done by default for each of the following background theories/combination (if this is theory-dependent): Linear Real Arithmetic (LRA), Linear Integer Real Arithmetic (LIRA), Non-Linear Integer Real Arithmetic (NIRA)? Also, where in the actual code (codeplex stable z3 v4.3.1) is a propositional literal (heuristically) decided by the SAT solver?
No, Z3 does not obtain a complete assignment before doing theory consistency checks.
However, it delays "expensive" checks. "Expensive" checks are performed in a step called final_check that is performed only when a (complete) proprositional assignment is produced. Here the word "expensive" is relative. Linear real arithmetic consistency checks can be quite expensive due to big number arithmetic computations, but they are considered "cheap" in Z3.
Linear real arithmetic checks are done eagerly. Nonlinear and linear integer arithmetic checks are done at the final_check step.
Note that Z3 contains more than one solver. The behavior above is for the one implemented in the directory smt. The nonlinear real arithmetic solver (nlsat directory) works in a completely different way, and it does not use the final_check approach described above.
I am trying to devise ways to improve performance of z3 on my problems. I am aware of the the CAV'06 paper and the tech report . Do relevant parts of z3 v4.3.1 differ from what is described in these documents, and if so in what ways? Also, what is the strategy followed by default in z3 for deciding when to check for consistency in Linear Real Arithmetic, of the theory atoms corresponding to the decided (and propagated) propositional literals?
Linear arithmetic is implemented in the files at src/smt/theory_arith*.
See http://z3.codeplex.com/SourceControl/latest#src/smt/theory_arith_core.h
Regarding the paper you pointed out, the ideas are used in the implementation. However, the actual code contains many extensions for linear integer, nonlinear arithmetic and proof generation. If you only care about linear real arithmetic, you should focus only on theory_arith.h, theory_arith_core.h. The file theory_arith_aux.h also contains useful functionality.
Does Z3py support Linear Temporal logic LTL?
If yes, can you provide an example of simple explain.
Z3 does not support LTL or other temporal or modal logics.
The input accepted by Z3 is first-order logic with theories, such as arithmetic.
Is it possible to ask Z3 to prove satisfiability of a system of integer polynomial inequalities with 2 different variables (or in general case) by approximating the original system with a system of linear inequalities?
By default, Z3 will try to solve a nonlinear integer problem as a linear one. The basic trick is to treat nonlinear terms such as x*y as new "variables". Nonlinear integer arithmetic is not well supported in Z3, the following post has a summary on how Z3 handles nonlinear integer arithmetic:
How does Z3 handle non-linear integer arithmetic?
Can SMT solver efficiently find a solution (or an assignment) for the pseudo-Boolean problem as described as follows:
\sum {i..m} f_i x1 x2.. xn *w_i
where f_i x1 x2 .. xn is a Boolean function, and w_i is a weight of Int type.
For your convenience, I highlight the contents in page 1 and 3, which is enough for specifying
the pseudo-Boolean problem.
SMT solvers typically address the question: given a logical formula, optionally using functions and predicates from underlying theories (such as the theory of arithmetic, the theory of bit-vectors, arrays), is the formula satisfiable or not.
They typically don't expose a way for you specify objective functions
and typically don't have built-in optimization procedures.
Some special cases are formulas that only use Booleans or a combination of Booleans and either bit-vectors or integers. Pseudo Boolean constraints can be formulated with either integers or encoded (with some care taking overflow semantics into account) using bit-vectors, or they can be encoded directly into SAT. For some formulas using bounded integers that fall in the class of psuedo-boolean problems, Z3 will try automatic reductions into bit-vectors. This applies only to benchmkars in the SMT-LIB2 format tagged as QF_LIA or applies if you explicitly invoke a tactic that performs this reduction (the "qflia" tactic should apply).
While Z3 does not directly expose objective functions, the question of augmenting
SMT solvers with objective functions is actively pursued in the research community.
One approach suggested by Nieuwenhuis and Oliveras in SAT 2006 was to build in
solving for the "weighted max SMT" problem as a custom theory. Yices comes with built-in
features for weighted max SMT, Z3 does not, but it is possible to write a custom
theory that performs the backtracking search of a weighted max SMT solver, but nothing
out of the box.
Sometimes people try to specify objective functions using quantified formulas.
In theory one could hope that quantifier elimination procedures then can solve
for the objective.
This is generally pretty bad when it comes to performance. Quantifier elimination
is an overfit and the routines (that we have) will not be efficient.
For your problem, if you want to find an optimized (maximum or minimum) result from the sum, yes Z3 has this ability. You can use the Optimize class of Z3 library instead of Solver class. The class provides two methods for 'maximization' and 'minimization' respectively. You can pass the SMT variable that is needed to be optimized and Optimization class model will give the solution for you. It actually worked with C# API using Microsoft.Z3 library. For your inconvenience, I am attaching a snippet:
Optimize opt; // initializing object
opt.MkMaximize(*your variable*);
opt.MkMinimize(*your variable*);
opt.Assert(*anything you need to do*);