* Introduce X-macro-based trace tag definition
- Created trace_tags.def to centralize TRACE tag definitions
- Each tag includes a symbolic name and description
- Set up enum class TraceTag for type-safe usage in TRACE macros
* Add script to generate Markdown documentation from trace_tags.def
- Python script parses trace_tags.def and outputs trace_tags.md
* Refactor TRACE_NEW to prepend TraceTag and pass enum to is_trace_enabled
* trace: improve trace tag handling system with hierarchical tagging
- Introduce hierarchical tag-class structure: enabling a tag class activates all child tags
- Unify TRACE, STRACE, SCTRACE, and CTRACE under enum TraceTag
- Implement initial version of trace_tag.def using X(tag, tag_class, description)
(class names and descriptions to be refined in a future update)
* trace: replace all string-based TRACE tags with enum TraceTag
- Migrated all TRACE, STRACE, SCTRACE, and CTRACE macros to use enum TraceTag values instead of raw string literals
* trace : add cstring header
* trace : Add Markdown documentation generation from trace_tags.def via mk_api_doc.py
* trace : rename macro parameter 'class' to 'tag_class' and remove Unicode comment in trace_tags.h.
* trace : Add TODO comment for future implementation of tag_class activation
* trace : Disable code related to tag_class until implementation is ready (#7663).
- convert reduce-args to a simplifier. Currently exposed as reduce-args2 tactic until the old tactic code gets removed.
- bug fixes in model_reconstruction trail
- allow multiple defs to be added with same pool of removed formulas
- fix tracking of function symbols instead of expressions to filter replay
- add nla_divisions to track (cheap) divisibility lemmas.
-
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))
```
other updates:
- change signature of advance_qhead to simplify call sites
- have model reconstruction replay work on a tail of dependent_expr state, while adding formulas to the tail.
- 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.
