1,282 research outputs found
Uncapacitated Flow-based Extended Formulations
An extended formulation of a polytope is a linear description of this
polytope using extra variables besides the variables in which the polytope is
defined. The interest of extended formulations is due to the fact that many
interesting polytopes have extended formulations with a lot fewer inequalities
than any linear description in the original space. This motivates the
development of methods for, on the one hand, constructing extended formulations
and, on the other hand, proving lower bounds on the sizes of extended
formulations.
Network flows are a central paradigm in discrete optimization, and are widely
used to design extended formulations. We prove exponential lower bounds on the
sizes of uncapacitated flow-based extended formulations of several polytopes,
such as the (bipartite and non-bipartite) perfect matching polytope and TSP
polytope. We also give new examples of flow-based extended formulations, e.g.,
for 0/1-polytopes defined from regular languages. Finally, we state a few open
problems
Average case polyhedral complexity of the maximum stable set problem
We study the minimum number of constraints needed to formulate random
instances of the maximum stable set problem via linear programs (LPs), in two
distinct models. In the uniform model, the constraints of the LP are not
allowed to depend on the input graph, which should be encoded solely in the
objective function. There we prove a lower bound with
probability at least for every LP that is exact for a randomly
selected set of instances; each graph on at most n vertices being selected
independently with probability . In the
non-uniform model, the constraints of the LP may depend on the input graph, but
we allow weights on the vertices. The input graph is sampled according to the
G(n, p) model. There we obtain upper and lower bounds holding with high
probability for various ranges of p. We obtain a super-polynomial lower bound
all the way from to . Our upper bound is close to this as there is only an essentially quadratic
gap in the exponent, which currently also exists in the worst-case model.
Finally, we state a conjecture that would close this gap, both in the
average-case and worst-case models
Weighted graphs defining facets: a connection between stable set and linear ordering polytopes
A graph is alpha-critical if its stability number increases whenever an edge
is removed from its edge set. The class of alpha-critical graphs has several
nice structural properties, most of them related to their defect which is the
number of vertices minus two times the stability number. In particular, a
remarkable result of Lov\'asz (1978) is the finite basis theorem for
alpha-critical graphs of a fixed defect. The class of alpha-critical graphs is
also of interest for at least two topics of polyhedral studies. First,
Chv\'atal (1975) shows that each alpha-critical graph induces a rank inequality
which is facet-defining for its stable set polytope. Investigating a weighted
generalization, Lipt\'ak and Lov\'asz (2000, 2001) introduce critical
facet-graphs (which again produce facet-defining inequalities for their stable
set polytopes) and they establish a finite basis theorem. Second, Koppen (1995)
describes a construction that delivers from any alpha-critical graph a
facet-defining inequality for the linear ordering polytope. Doignon, Fiorini
and Joret (2006) handle the weighted case and thus define facet-defining
graphs. Here we investigate relationships between the two weighted
generalizations of alpha-critical graphs. We show that facet-defining graphs
(for the linear ordering polytope) are obtainable from 1-critical facet-graphs
(linked with stable set polytopes). We then use this connection to derive
various results on facet-defining graphs, the most prominent one being derived
from Lipt\'ak and Lov\'asz's finite basis theorem for critical facet-graphs. At
the end of the paper we offer an alternative proof of Lov\'asz's finite basis
theorem for alpha-critical graphs
Small Extended Formulation for Knapsack Cover Inequalities from Monotone Circuits
Initially developed for the min-knapsack problem, the knapsack cover
inequalities are used in the current best relaxations for numerous
combinatorial optimization problems of covering type. In spite of their
widespread use, these inequalities yield linear programming (LP) relaxations of
exponential size, over which it is not known how to optimize exactly in
polynomial time. In this paper we address this issue and obtain LP relaxations
of quasi-polynomial size that are at least as strong as that given by the
knapsack cover inequalities.
For the min-knapsack cover problem, our main result can be stated formally as
follows: for any , there is a -size LP relaxation with an integrality gap of at most ,
where is the number of items. Prior to this work, there was no known
relaxation of subexponential size with a constant upper bound on the
integrality gap.
Our construction is inspired by a connection between extended formulations
and monotone circuit complexity via Karchmer-Wigderson games. In particular,
our LP is based on -depth monotone circuits with fan-in~ for
evaluating weighted threshold functions with inputs, as constructed by
Beimel and Weinreb. We believe that a further understanding of this connection
may lead to more positive results complementing the numerous lower bounds
recently proved for extended formulations.Comment: 21 page
Small Extended Formulations for Cyclic Polytopes
We provide an extended formulation of size O(log n)^{\lfloor d/2 \rfloor} for
the cyclic polytope with dimension d and n vertices (i,i^2,\ldots,i^d), i in
[n]. First, we find an extended formulation of size log(n) for d= 2. Then, we
use this as base case to construct small-rank nonnegative factorizations of the
slack matrices of higher-dimensional cyclic polytopes, by iterated tensor
products. Through Yannakakis's factorization theorem, these factorizations
yield small-size extended formulations for cyclic polytopes of dimension d>2
- …