10,153 research outputs found
A Characterization Theorem and An Algorithm for A Convex Hull Problem
Given and , testing if , the convex hull of , is a fundamental
problem in computational geometry and linear programming. First, we prove a
Euclidean {\it distance duality}, distinct from classical separation theorems
such as Farkas Lemma: lies in if and only if for each there exists a {\it pivot}, satisfying . Equivalently, if and only if there exists a
{\it witness}, whose Voronoi cell relative to contains
. A witness separates from and approximate to
within a factor of two. Next, we describe the {\it Triangle Algorithm}: given
, an {\it iterate}, , and , if
, it stops. Otherwise, if there exists a pivot
, it replace with and with the projection of onto the
line . Repeating this process, the algorithm terminates in arithmetic operations, where
is the {\it visibility factor}, a constant satisfying and
, over all iterates . Additionally,
(i) we prove a {\it strict distance duality} and a related minimax theorem,
resulting in more effective pivots; (ii) describe -time algorithms that may compute a witness or a good
approximate solution; (iii) prove {\it generalized distance duality} and
describe a corresponding generalized Triangle Algorithm; (iv) prove a {\it
sensitivity theorem} to analyze the complexity of solving LP feasibility via
the Triangle Algorithm. The Triangle Algorithm is practical and competitive
with the simplex method, sparse greedy approximation and first-order methods.Comment: 42 pages, 17 figures, 2 tables. This revision only corrects minor
typo
The Edgeworth Conjecture with Small Coalitions and Approximate Equilibria in Large Economies
We revisit the connection between bargaining and equilibrium in exchange
economies, and study its algorithmic implications. We consider bargaining
outcomes to be allocations that cannot be blocked (i.e., profitably re-traded)
by coalitions of small size and show that these allocations must be approximate
Walrasian equilibria. Our results imply that deciding whether an allocation is
approximately Walrasian can be done in polynomial time, even in economies for
which finding an equilibrium is known to be computationally hard.Comment: 26 page
Convexity in partial cubes: the hull number
We prove that the combinatorial optimization problem of determining the hull
number of a partial cube is NP-complete. This makes partial cubes the minimal
graph class for which NP-completeness of this problem is known and improves
some earlier results in the literature.
On the other hand we provide a polynomial-time algorithm to determine the
hull number of planar partial cube quadrangulations.
Instances of the hull number problem for partial cubes described include
poset dimension and hitting sets for interiors of curves in the plane.
To obtain the above results, we investigate convexity in partial cubes and
characterize these graphs in terms of their lattice of convex subgraphs,
improving a theorem of Handa. Furthermore we provide a topological
representation theorem for planar partial cubes, generalizing a result of
Fukuda and Handa about rank three oriented matroids.Comment: 19 pages, 4 figure
On k-Convex Polygons
We introduce a notion of -convexity and explore polygons in the plane that
have this property. Polygons which are \mbox{-convex} can be triangulated
with fast yet simple algorithms. However, recognizing them in general is a
3SUM-hard problem. We give a characterization of \mbox{-convex} polygons, a
particularly interesting class, and show how to recognize them in \mbox{} time. A description of their shape is given as well, which leads to
Erd\H{o}s-Szekeres type results regarding subconfigurations of their vertex
sets. Finally, we introduce the concept of generalized geometric permutations,
and show that their number can be exponential in the number of
\mbox{-convex} objects considered.Comment: 23 pages, 19 figure
LP-decodable multipermutation codes
In this paper, we introduce a new way of constructing and decoding
multipermutation codes. Multipermutations are permutations of a multiset that
may consist of duplicate entries. We first introduce a new class of matrices
called multipermutation matrices. We characterize the convex hull of
multipermutation matrices. Based on this characterization, we propose a new
class of codes that we term LP-decodable multipermutation codes. Then, we
derive two LP decoding algorithms. We first formulate an LP decoding problem
for memoryless channels. We then derive an LP algorithm that minimizes the
Chebyshev distance. Finally, we show a numerical example of our algorithm.Comment: This work was supported by NSF and NSERC. To appear at the 2014
Allerton Conferenc
Adaptive, Anisotropic and Hierarchical cones of Discrete Convex functions
We address the discretization of optimization problems posed on the cone of
convex functions, motivated in particular by the principal agent problem in
economics, which models the impact of monopoly on product quality. Consider a
two dimensional domain, sampled on a grid of N points. We show that the cone of
restrictions to the grid of convex functions is in general characterized by N^2
linear inequalities; a direct computational use of this description therefore
has a prohibitive complexity. We thus introduce a hierarchy of sub-cones of
discrete convex functions, associated to stencils which can be adaptively,
locally, and anisotropically refined. Numerical experiments optimize the
accuracy/complexity tradeoff through the use of a-posteriori stencil refinement
strategies.Comment: 35 pages, 11 figures. (Second version fixes a small bug in Lemma 3.2.
Modifications are anecdotic.
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