Convexification of classes of mixed-integer sets with L$^\natural$-convexity

TL;DR

This paper develops convexification techniques for various classes of mixed-integer L$^ atural$-convex functions, providing new inequalities, hull descriptions, and revealing hidden convexity in known structures, advancing optimization methods.

Contribution

It introduces valid inequalities and convex hull descriptions for mixed-integer L$^ atural$-convex functions and uncovers hidden convexity in classical mixed-integer sets.

Findings

Derived convex hull descriptions for mixed-integer L$^ atural$-convex functions.

Identified hidden L$^ atural$-convexity in mixing sets.

Extended polyhedral results to multi-capacity mixing sets.

Abstract

L$^\natural$ (natural)-convex functions encompass a large class of nonlinear functions over general integer domains and arise in a wide range of real-world applications. We explore the minimization of L$^\natural$-convex functions, of multiple L$^\natural$-convex functions with common variables, and of a mixed-integer extension of L$^\natural$-convex functions -- functions defined over a mixed-integer domain with properties that resemble L$^\natural$-convexity. For each of these families of minimization problems, we propose valid linear inequalities and provide convex hull descriptions for the corresponding epigraphs. For all classes of proposed inequalities, we discuss their facet conditions, develop exact separation methods, and analyze the complexity of the separation problem. We discover hidden L$^\natural$-convexity in well-known mixed-integer structures in the integer programming literature, namely the (general integer) mixing set and the continuous mixing set. We show that our findings subsume the existing polyhedral results for these sets and establish new results for the multi-capacity variant of the continuous mixing set.