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))
```
- remove reduce_invertible. It is subsumed by reduce_uncstr(2)
- introduce a simplifier for reduce_unconstrained. It uses reference counting to deal with inefficiency bug of legacy reduce_uncstr. It decomposes theory plugins into expr_inverter.
reduce_invertible is a tactic used in most built-in scenarios. It is useful for removing subterms that can be eliminated using "cheap" quantifier elimination. Specifically variables that occur only once can be removed in many cases by computing an expression that represents the effect computing a value for the eliminated occurrence.
The theory plugins for variable elimination are very partial and should be augmented by extensions, esp. for the case of bit-vectors where the invertibility conditions are thoroughly documented by Niemetz and Preiner.
