Geometry of the Reformulation-Linearization-Technique: Domination of Disjunctions
Hugo A. Hof, Matthias Walter
TL;DR
This paper investigates the geometry and strength of the Reformulation-Linearization Technique (RLT) in binary mixed-integer programs, contrasting it with disjunctive programming (DP) and lift-and-project (L&P). It provides a geometric characterization of points in the RLT closure, showing that they can be described as convex combinations on faces of the unit cube subject to an extra constraint, and demonstrates that RLT can dominate DP approaches based on cardinality equations with RHS $1$, with implications for the quadratic assignment problem (QAP). The results extend to a general dominance framework, illustrating when RLT-based relaxations subsume certain disjunctive hulls, and clarify a boundary where domination may fail (e.g., for $\le 1$ cardinality constraints). The work advances understanding of RLT’s strength and paves the way for refined formulations and open geometric questions, with practical impact on QAP formulations and related discrete optimization problems.
Abstract
The reformulation-linearization-technique (RLT) is a well-known strengthening technique for binary mixed-integer optimization. It is well known to dominate lift-and-project strengthening, which is based on disjunctive programming (DP) for single-variable disjunctions. In contrast to the latter, the geometry of RLT is not understood completely. We provide some insights by characterizing the points in the corresponding RLT closure geometrically. We exploit this insight to show that RLT even dominates DP approaches based on cardinality equations with right-hand side 1. This is in contrast to cardinality inequalities with right-hand side 1, whose DPs are not dominated. Our results have applications in the strength comparison for the quadratic assignment problem.
