Add API solve_for(vars).
It takes a list of variables and returns a triangular solved form for the variables.
Currently for arithmetic. The solved form is a list with elements of the form (var, term, guard).
Variables solved in the tail of the list do not occur before in the list.
For example it can return a solution [(x, z, True), (y, x + z, True)] because first x was solved to be z,
then y was solved to be x + z which is the same as 2z.
Add congruent_explain that retuns an explanation for congruent terms.
Terms congruent in the final state after calling SimpleSolver().check() can be queried for
an explanation, i.e., a list of literals that collectively entail the equality under congruence closure.
The literals are asserted in the final state of search.
Adjust smt_context cancellation for the smt.qi.max_instantiations parameter.
It gets checked when qi-queue elements are consumed.
Prior it was checked on insertion time, which didn't allow for processing as many
instantations as there were in the queue. Moreover, it would not cancel the solver.
So it would keep adding instantations to the queue when it was full / depleted the
configuration limit.
A slice solver prunes the set of active assertions based on symbol occurrences in a goal that is tracked as a @query.
Ground assertions that have symbols intersecting with the query are included in the solver state, and quantifiers that with patterns that intersect with the slice are included. The slice is the fixedpoint of including symbols from all included assertions.
Enable the functionality for command-line use by setting solver.slice=true
simplify assumptions and only replay assumptions after constraints are simplified. This allows simplifying assumptions with the current set of constraints independently of whether there is another check-sat.
this will allow copying the solver state within a scope.
The new solver state has its state at level 0. It is not possible to pop scopes from the new solver (you can still pop scopes from the original solver). The reason for this semantics is the relative difficulty of implementing (getting it right) of a state copy that preserves scopes.
Add the ability to customize incremental pre-processing simplification for the SMTLIB2 front-end. The main new capability is to use pre-processing tactics in incremental mode that were previously not available. The main new capabilities are
- solve-eqs
- reduce-args
- elim-unconstrained
There are several more. Documentation and exposed simplifiers are populated incrementally. The current set of supported simplifiers can be inspected by using z3 with the --simplifiers flag or referring to https://microsoft.github.io/z3guide/docs/strategies/simplifiers
Some pending features are:
- add the ability to update parameters to simplifiers similar to how tactics can be controlled using parameters.
- expose simplification solvers over the binary API.
move sat_smt_preprocess to solver
fix bugs in model_reconstruction_trail for dependency replay
This is a preparatory step for exposing pre-processing as tactics.
This update includes an experimental feature to access a congruence closure data-structure after search.
It comes with several caveats as pre-processing is free to eliminate terms. It is therefore necessary to use a solver that does not eliminate the terms you want to track for congruence of. This is partially addressed by using SimpleSolver or incremental mode solving.
```python
from z3 import *
s = SimpleSolver()
x, y, z = Ints('x y z')
s.add(x == y)
s.add(y == z)
s.check()
print(s.root(x), s.root(y), s.root(z))
print(s.next(x), s.next(y), s.next(z))
```
- add sat.smt option to enable the new incremental core (it is not ready for mainstream consumption as cloning and other features are not implemented and it hasn't been tested in any detail yet).
- move "name" into attribute on simplifier so it can be reused for diagnostics by the seq-simplifier.
