We prove that there are 0 1 polytopes P that do not admit a compact LP formulation. More precisely we show that for every n there is a sets X 0,1 ^n such that conv(X) must have extension complexity at least 2^ n 2 * (1-o(1)) . In other words, every polyhedron Q that can be linearly projected on conv(X) must have exponentially many facets. In fact, the same result also applies if conv(X) is restricted to be a matroid polytope. Conditioning on NP not contained in P_ poly , our result rules out the existence of any compact formulation for the TSP polytope, even if the formulation may contain arbitrary real numbers.
We study the max cut problem in graphs not contractible toK5, and optimum perfect matchings in planar graphs. We prove that both problems can be formulated as polynomial size linear programs. We discuss a new conceptual framework for the convexification of discrete optimization problems, and a general technique for obtaining approximations to the convex hull of the feasible set. The concepts come from disjunctive programming and the key tool is a description of the convex hull of a union of polyhedra in terms of a higher dimensional polyhedron. Although this description was known for several years, only recently was it shown by Jeroslow and Lowe to yield improved representations of discrete optimization problems. We express the feasible set of a discrete optimization problem as the intersection (conjunction) of unions of polyhedra, and define an operation that takes one such expression into another, equivalent one, with fewer conjuncts. We then introduce a class of relaxations based on replacing each conjunct (union of polyhedra) by its convex hull. The strength of the relaxations increases as the number of conjuncts decreases, and the class of relaxations forms a hierarchy that spans the spec... We show that for each 0<@e@?1 there exists an extended formulation for the knapsack problem, of size polynomial in the number of variables, whose value is at most (1+@e) times the value of the integer program. This survey is concerned with the size of perfect formulations for combinatorial optimization problems. By “perfect formulation”, we mean a system of linear inequalities that describes the convex hull of feasible solutions, viewed as vectors. Natural perfect formulations often have a number of inequalities that is exponential in the size of the data needed to describe the problem. Here we are particularly interested in situations where the addition of a polynomial number of extra variables allows a formulation with a polynomial number of inequalities. Such formulations are called “compact extended formulations”. We survey various tools for deriving and studying extended formulations, such as Fourier’s procedure for projection, Minkowski–Weyl’s theorem, Balas’ theorem for the union of polyhedra, Yannakakis’ theorem on the size of an extended formulation, dynamic programming, and variable discretization. For each tool that we introduce, we present one or several examples of how this tool is applied. In particular, we present compact extended formulations for several graph problems involving cuts, trees, cycles and matchings, and for the mixing set. We also present Bienstock’s approximate compact extended formulation for the knapsack problem, Goemans’ result on the size of an extended formulation for the permutahedron, and the Faenza-Kaibel extended formulation for orbitopes.  We extend a result of Barahona, saying that T-join and perfect matching problems for planar graphs can be formulated as linear programming problems using only a polynomial number of constraints and variables, to graphs embeddable on an arbitrary, but fixed, surface. In this paper we characterize the convex hull of feasible points for a disjunctive program, a class of problems which subsumes pure and mixed integer programs and many other nonconvex programming problems. Two representations are given for the convex hull of feasible points, each of which provides linear programming equivalents of the disjunctive program. The first one involves a number of new variables proportional to the number of terms in the disjunctive normal form of the logical constraints; the second one involves only the original variables and the facets of the convex hull. Among other results, we give necessary and sufficient conditions for an inequality to define a facet of the convex hull of feasible points. For the class of disjunctive programs that we call facial, we establish a property which makes it possible to obtain the convex hull of points satisfying n disjunctions, in a sequence of n steps, where each step generates the convex hull of points satisfying one disjunction only. 