Monoidal strengthening of simple $\mathcal{V}$-polyhedral disjunctive cuts

TL;DR

This paper introduces a method to enhance disjunctive cuts in mixed-integer linear programming by applying monoidal strengthening within a $\

Contribution

It develops a way to compute necessary values for monoidal strengthening directly from $\

Findings

Strengthening can significantly improve cut effectiveness for small disjunctions.

Effectiveness diminishes with larger disjunctions, indicating a potential limitation.

Computational experiments show up to 100% gap closure in some instances.

Abstract

Disjunctive cutting planes can tighten a relaxation of a mixed-integer linear program. Traditionally, such cuts are obtained by solving a higher-dimensional linear program, whose additional variables cause the procedure to be computationally prohibitive. Adopting a $\mathcal{V}$-polyhedral perspective is a practical alternative that enables the separation of disjunctive cuts via a linear program with only as many variables as the original problem. The drawback is that the classical approach of monoidal strengthening cannot be directly employed without the values of the extra variables appearing in the extended formulation. We derive how to compute these values from a solution to the linear program generating $\mathcal{V}$-polyhedral disjunctive cuts. We then present computational experiments with monoidal strengthening of cuts from disjunctions with as many as 64 terms. Some instances are dramatically impacted, with strengthening increasing the gap closed by the cuts from 0 to 100%. However, for larger disjunctions, monoidal strengthening appears to be less effective, for which we identify a potential cause.