Integer programming and convex analysis: Intersection cuts from outer polars

Abstract

Recently a new, geometrically motivated approach was proposed [1] for integer programming, based on generating intersection cuts from some convex setS whose interior contains the linear programming optimum$\bar x$ but no feasible integer point. Larger sets tend to produce stronger cuts, and in this paper convex analysis is used to construct sets as large as possible within the above requirements. Information is generated from all problem constraints within a unit cubeK containing$\bar x$ The key concept is that of outer polars, viewed as maximal convex extensions of the ballB circumscribingK, relative to the problem constraints. The outer polarF ^* of the feasible setF overB is shown to be a convex set whose boundary contains all feasible vertices ofK, and whose interior contains no feasible integer point. The existence of a dual correspondence betweenF andF ^*, and the fact that polarity is inclusion-reversing, leads to a dualization of operations onF. As one possible procedure based on this approach, we construct a generalized intersection cut, that can be strengthened whenever some vertex ofF is cut off. This makes it possible to fruitfully combine intersection cuts with implicit enumeration or branch-and-bound. While valid for arbitrary integer programs, the theory developed here is relevant primarily to (pure or mixed-integer) 0–1 problems. Other topics discussed include: generalized polars, intersection cuts from related problems, connections with asymptotic theory.