700 research outputs found
Embedding into the rectilinear plane in optimal O*(n^2)
We present an optimal O*(n^2) time algorithm for deciding if a metric space
(X,d) on n points can be isometrically embedded into the plane endowed with the
l_1-metric. It improves the O*(n^2 log^2 n) time algorithm of J. Edmonds
(2008). Together with some ingredients introduced by J. Edmonds, our algorithm
uses the concept of tight span and the injectivity of the l_1-plane. A
different O*(n^2) time algorithm was recently proposed by D. Eppstein (2009).Comment: 12 pages, 13 figure
Fast Clustering with Lower Bounds: No Customer too Far, No Shop too Small
We study the \LowerBoundedCenter (\lbc) problem, which is a clustering
problem that can be viewed as a variant of the \kCenter problem. In the \lbc
problem, we are given a set of points P in a metric space and a lower bound
\lambda, and the goal is to select a set C \subseteq P of centers and an
assignment that maps each point in P to a center of C such that each center of
C is assigned at least \lambda points. The price of an assignment is the
maximum distance between a point and the center it is assigned to, and the goal
is to find a set of centers and an assignment of minimum price. We give a
constant factor approximation algorithm for the \lbc problem that runs in O(n
\log n) time when the input points lie in the d-dimensional Euclidean space
R^d, where d is a constant. We also prove that this problem cannot be
approximated within a factor of 1.8-\epsilon unless P = \NP even if the input
points are points in the Euclidean plane R^2.Comment: 14 page
Compact Floor-Planning via Orderly Spanning Trees
Floor-planning is a fundamental step in VLSI chip design. Based upon the
concept of orderly spanning trees, we present a simple O(n)-time algorithm to
construct a floor-plan for any n-node plane triangulation. In comparison with
previous floor-planning algorithms in the literature, our solution is not only
simpler in the algorithm itself, but also produces floor-plans which require
fewer module types. An equally important aspect of our new algorithm lies in
its ability to fit the floor-plan area in a rectangle of size (n-1)x(2n+1)/3.
Lower bounds on the worst-case area for floor-planning any plane triangulation
are also provided in the paper.Comment: 13 pages, 5 figures, An early version of this work was presented at
9th International Symposium on Graph Drawing (GD 2001), Vienna, Austria,
September 2001. Accepted to Journal of Algorithms, 200
Minimum-weight triangulation is NP-hard
A triangulation of a planar point set S is a maximal plane straight-line
graph with vertex set S. In the minimum-weight triangulation (MWT) problem, we
are looking for a triangulation of a given point set that minimizes the sum of
the edge lengths. We prove that the decision version of this problem is
NP-hard. We use a reduction from PLANAR-1-IN-3-SAT. The correct working of the
gadgets is established with computer assistance, using dynamic programming on
polygonal faces, as well as the beta-skeleton heuristic to certify that certain
edges belong to the minimum-weight triangulation.Comment: 45 pages (including a technical appendix of 13 pages), 28 figures.
This revision contains a few improvements in the expositio
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