753 research outputs found
On the Duality of Semiantichains and Unichain Coverings
We study a min-max relation conjectured by Saks and West: For any two posets
and the size of a maximum semiantichain and the size of a minimum
unichain covering in the product are equal. For positive we state
conditions on and that imply the min-max relation. Based on these
conditions we identify some new families of posets where the conjecture holds
and get easy proofs for several instances where the conjecture had been
verified before. However, we also have examples showing that in general the
min-max relation is false, i.e., we disprove the Saks-West conjecture.Comment: 10 pages, 3 figure
Computing symmetry groups of polyhedra
Knowing the symmetries of a polyhedron can be very useful for the analysis of
its structure as well as for practical polyhedral computations. In this note,
we study symmetry groups preserving the linear, projective and combinatorial
structure of a polyhedron. In each case we give algorithmic methods to compute
the corresponding group and discuss some practical experiences. For practical
purposes the linear symmetry group is the most important, as its computation
can be directly translated into a graph automorphism problem. We indicate how
to compute integral subgroups of the linear symmetry group that are used for
instance in integer linear programming.Comment: 20 pages, 1 figure; containing a corrected and improved revisio
Combinatorial Penalties: Which structures are preserved by convex relaxations?
We consider the homogeneous and the non-homogeneous convex relaxations for
combinatorial penalty functions defined on support sets. Our study identifies
key differences in the tightness of the resulting relaxations through the
notion of the lower combinatorial envelope of a set-function along with new
necessary conditions for support identification. We then propose a general
adaptive estimator for convex monotone regularizers, and derive new sufficient
conditions for support recovery in the asymptotic setting
Recent progress on the combinatorial diameter of polytopes and simplicial complexes
The Hirsch conjecture, posed in 1957, stated that the graph of a
-dimensional polytope or polyhedron with facets cannot have diameter
greater than . The conjecture itself has been disproved, but what we
know about the underlying question is quite scarce. Most notably, no polynomial
upper bound is known for the diameters that were conjectured to be linear. In
contrast, no polyhedron violating the conjecture by more than 25% is known.
This paper reviews several recent attempts and progress on the question. Some
work in the world of polyhedra or (more often) bounded polytopes, but some try
to shed light on the question by generalizing it to simplicial complexes. In
particular, we include here our recent and previously unpublished proof that
the maximum diameter of arbitrary simplicial complexes is in and
we summarize the main ideas in the polymath 3 project, a web-based collective
effort trying to prove an upper bound of type nd for the diameters of polyhedra
and of more general objects (including, e. g., simplicial manifolds).Comment: 34 pages. This paper supersedes one cited as "On the maximum diameter
of simplicial complexes and abstractions of them, in preparation
Measured descent: A new embedding method for finite metrics
We devise a new embedding technique, which we call measured descent, based on
decomposing a metric space locally, at varying speeds, according to the density
of some probability measure. This provides a refined and unified framework for
the two primary methods of constructing Frechet embeddings for finite metrics,
due to [Bourgain, 1985] and [Rao, 1999]. We prove that any n-point metric space
(X,d) embeds in Hilbert space with distortion O(sqrt{alpha_X log n}), where
alpha_X is a geometric estimate on the decomposability of X. As an immediate
corollary, we obtain an O(sqrt{(log lambda_X) \log n}) distortion embedding,
where \lambda_X is the doubling constant of X. Since \lambda_X\le n, this
result recovers Bourgain's theorem, but when the metric X is, in a sense,
``low-dimensional,'' improved bounds are achieved.
Our embeddings are volume-respecting for subsets of arbitrary size. One
consequence is the existence of (k, O(log n)) volume-respecting embeddings for
all 1 \leq k \leq n, which is the best possible, and answers positively a
question posed by U. Feige. Our techniques are also used to answer positively a
question of Y. Rabinovich, showing that any weighted n-point planar graph
embeds in l_\infty^{O(log n)} with O(1) distortion. The O(log n) bound on the
dimension is optimal, and improves upon the previously known bound of O((log
n)^2).Comment: 17 pages. No figures. Appeared in FOCS '04. To appeaer in Geometric &
Functional Analysis. This version fixes a subtle error in Section 2.
Structured sparsity-inducing norms through submodular functions
Sparse methods for supervised learning aim at finding good linear predictors
from as few variables as possible, i.e., with small cardinality of their
supports. This combinatorial selection problem is often turned into a convex
optimization problem by replacing the cardinality function by its convex
envelope (tightest convex lower bound), in this case the L1-norm. In this
paper, we investigate more general set-functions than the cardinality, that may
incorporate prior knowledge or structural constraints which are common in many
applications: namely, we show that for nondecreasing submodular set-functions,
the corresponding convex envelope can be obtained from its \lova extension, a
common tool in submodular analysis. This defines a family of polyhedral norms,
for which we provide generic algorithmic tools (subgradients and proximal
operators) and theoretical results (conditions for support recovery or
high-dimensional inference). By selecting specific submodular functions, we can
give a new interpretation to known norms, such as those based on
rank-statistics or grouped norms with potentially overlapping groups; we also
define new norms, in particular ones that can be used as non-factorial priors
for supervised learning
Near-Optimal Algorithms for Online Matrix Prediction
In several online prediction problems of recent interest the comparison class
is composed of matrices with bounded entries. For example, in the online
max-cut problem, the comparison class is matrices which represent cuts of a
given graph and in online gambling the comparison class is matrices which
represent permutations over n teams. Another important example is online
collaborative filtering in which a widely used comparison class is the set of
matrices with a small trace norm. In this paper we isolate a property of
matrices, which we call (beta,tau)-decomposability, and derive an efficient
online learning algorithm, that enjoys a regret bound of O*(sqrt(beta tau T))
for all problems in which the comparison class is composed of
(beta,tau)-decomposable matrices. By analyzing the decomposability of cut
matrices, triangular matrices, and low trace-norm matrices, we derive near
optimal regret bounds for online max-cut, online gambling, and online
collaborative filtering. In particular, this resolves (in the affirmative) an
open problem posed by Abernethy (2010); Kleinberg et al (2010). Finally, we
derive lower bounds for the three problems and show that our upper bounds are
optimal up to logarithmic factors. In particular, our lower bound for the
online collaborative filtering problem resolves another open problem posed by
Shamir and Srebro (2011).Comment: 25 page
On tree decomposability of Henneberg graphs
In this work we describe an algorithm that generates well constrained geometric constraint graphs which are solvable by the tree-decomposition constructive technique. The algorithm is based on Henneberg constructions and would be of help in transforming underconstrained problems into well constrained problems as well as in exploring alternative constructions over a given set of geometric elements.Postprint (published version
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