perf fix for propagation behavior for equalities in the new core.
The old behavior was not to allow congruence closure on equalities.
The new behavior is to just not allow merging tf with equalities unless they appear somewhere in a foreign context (not under a Boolean operator)
The change re-surfaces merge_tf and enable_cgc distinction from the old core.
They can both be turned on or off.
merge_enabled renamed to cgc_enabled
The change is highly likely to introduce regressions in the new core.
Change propagation of literals from congruence:
- track antecedent enode. There are four ways to propagate
literals from the egraph.
- the literal is an equality and the two arguments are congruent
- the antecedent is merged with node n and the antecedent has a Boolean variable assignment.
- the antecedent is true or false, they are merged.
- the merge_tf flag is toggled to true but the node n has not been merged with true/false
- ensure mk_extract performs simplification to distribute over extract and removing extract if the range is the entire bit-vector
- ensure bool_rewriter simplifeis disjunctions when applicable.
The bug was that axiom generation was not enabled on last_index, so no axioms got created to constrain last-index.
With default settings the solver is now very slow on this example. It is related to that the smallest size of a satisfying assignment is above 24. Pending a good heuristic to find initial seeds and increments for iterative deepening, I am adding another parameter smt.seq.min_unfolding that when set to 30 helps for this example.
The literal "emp" can be true in the current assignment, in which case the clause
cnt or emp or ~postf is true and does not contribute to propagation.
This saves, potentially, for generating lemmas for postf.
Add a lemma a = "" or |s| >= idx when a = tail(s, idx)
The lemma ensures that length bounding on s is enforced
(the branch that expands not-contains for long sequences s is closed).
- add option smt.bv.reduce_size.
- it allows to apply incremental pre-processing of bit-vectors by identifying ranges that are known to be constant.
This rewrite is beneficial, for instance, when bit-vectors are constrained to have many high-level bits set to 0.
