Lifting Linear Extension Complexity Bounds to the Mixed-Integer Setting (1712.02176v1)

Abstract: Mixed-integer mathematical programs are among the most commonly used models for a wide set of problems in Operations Research and related fields. However, there is still very little known about what can be expressed by small mixed-integer programs. In particular, prior to this work, it was open whether some classical problems, like the minimum odd-cut problem, can be expressed by a compact mixed-integer program with few (even constantly many) integer variables. This is in stark contrast to linear formulations, where recent breakthroughs in the field of extended formulations have shown that many polytopes associated to classical combinatorial optimization problems do not even admit approximate extended formulations of sub-exponential size. We provide a general framework for lifting inapproximability results of extended formulations to the setting of mixed-integer extended formulations, and obtain almost tight lower bounds on the number of integer variables needed to describe a variety of classical combinatorial optimization problems. Among the implications we obtain, we show that any mixed-integer extended formulation of sub-exponential size for the matching polytope, cut polytope, traveling salesman polytope or dominant of the odd-cut polytope, needs $ \Omega(n/\log n) $ many integer variables, where $ n $ is the number of vertices of the underlying graph. Conversely, the above-mentioned polyhedra admit polynomial-size mixed-integer formulations with only $ O(n) $ or $ O(n \log n) $ (for the traveling salesman polytope) many integer variables. Our results build upon a new decomposition technique that, for any convex set $ C $, allows for approximating any mixed-integer description of $ C $ by the intersection of $ C $ with the union of a small number of affine subspaces.