An efficient constraint based framework forhandling floating point SMT
  problems
  This paper introduces the 2019 version of \us{}, a novel Constraint
Programming framework for floating point verification problems expressed with
the SMT language of SMTLIB. SMT solvers decompose their task by delegating to
specific theories (e.g., floating point, bit vectors, arrays, ...) the task to
reason about combinatorial or otherwise complex constraints for which the SAT
encoding would be cumbersome or ineffective. This decomposition and encoding
processes lead to the obfuscation of the high-level constraints and a loss of
information on the structure of the combinatorial model. In \us{}, constraints
over the floats are first class objects, and the purpose is to expose and
exploit structures of floating point domains to enhance the search process. A
symbolic phase rewrites each SMTLIB instance to elementary constraints, and
eliminates auxiliary variables whose presence is counterproductive. A
diversification technique within the search steers it away from costly
enumerations in unproductive areas of the search space. The empirical
evaluation demonstrates that the 2019 version of \us{} is competitive on
computationally challenging floating point benchmarks that induce significant
search efforts even for other CP solvers. It highlights that the ability to
harness both inference and search is critical. Indeed, it yields a factor 3
improvement over Colibri and is up to 10 times faster than SMT solvers. The
evaluation was conducted over 214 benchmarks (The Griggio suite) which is a
standard within SMTLIB.
