  Optimization solvers routinely utilize presolve techniques, including model
simplification, reformulation and domain reduction techniques. Domain reduction
techniques are especially important in speeding up convergence to the global
optimum for challenging nonconvex nonlinear programming (NLP) and mixed-integer
nonlinear programming (MINLP) optimization problems. In this work, we survey
the various techniques used for domain reduction of NLP and MINLP optimization
problems. We also present a computational analysis of the impact of these
techniques on the performance of various widely available global solvers on a
collection of 1740 test problems.
