  Domain reduction techniques are at the core of any global optimization solver
for NLP or MINLP problems. In this paper, we delve into several of these
techniques and assess the impact they may have in the performance of an
RLT-based algorithm for polynomial optimization problems. These techniques
include i) the use of (nonlinear) conic relaxations for optimality-based bound
tightening, ii) the use of Lagrangian dual information to enhance
feasibility-based bound tightening, and iii) different strategies for branching
point selection. We also explore how a solver equipped with these domain
reduction enhancements can further improve its performance by using machine
learning to better choose the best domain reduction approach to use on a given
instance.
