2,549 research outputs found
A Framework for Algorithm Stability
We say that an algorithm is stable if small changes in the input result in
small changes in the output. This kind of algorithm stability is particularly
relevant when analyzing and visualizing time-varying data. Stability in general
plays an important role in a wide variety of areas, such as numerical analysis,
machine learning, and topology, but is poorly understood in the context of
(combinatorial) algorithms. In this paper we present a framework for analyzing
the stability of algorithms. We focus in particular on the tradeoff between the
stability of an algorithm and the quality of the solution it computes. Our
framework allows for three types of stability analysis with increasing degrees
of complexity: event stability, topological stability, and Lipschitz stability.
We demonstrate the use of our stability framework by applying it to kinetic
Euclidean minimum spanning trees
Topological Stability of Kinetic -Centers
We study the -center problem in a kinetic setting: given a set of
continuously moving points in the plane, determine a set of (moving)
disks that cover at every time step, such that the disks are as small as
possible at any point in time. Whereas the optimal solution over time may
exhibit discontinuous changes, many practical applications require the solution
to be stable: the disks must move smoothly over time. Existing results on this
problem require the disks to move with a bounded speed, but this model is very
hard to work with. Hence, the results are limited and offer little theoretical
insight. Instead, we study the topological stability of -centers.
Topological stability was recently introduced and simply requires the solution
to change continuously, but may do so arbitrarily fast. We prove upper and
lower bounds on the ratio between the radii of an optimal but unstable solution
and the radii of a topologically stable solution---the topological stability
ratio---considering various metrics and various optimization criteria. For we provide tight bounds, and for small we can obtain nontrivial
lower and upper bounds. Finally, we provide an algorithm to compute the
topological stability ratio in polynomial time for constant
Convex Hull of Points Lying on Lines in o(n log n) Time after Preprocessing
Motivated by the desire to cope with data imprecision, we study methods for
taking advantage of preliminary information about point sets in order to speed
up the computation of certain structures associated with them.
In particular, we study the following problem: given a set L of n lines in
the plane, we wish to preprocess L such that later, upon receiving a set P of n
points, each of which lies on a distinct line of L, we can construct the convex
hull of P efficiently. We show that in quadratic time and space it is possible
to construct a data structure on L that enables us to compute the convex hull
of any such point set P in O(n alpha(n) log* n) expected time. If we further
assume that the points are "oblivious" with respect to the data structure, the
running time improves to O(n alpha(n)). The analysis applies almost verbatim
when L is a set of line-segments, and yields similar asymptotic bounds. We
present several extensions, including a trade-off between space and query time
and an output-sensitive algorithm. We also study the "dual problem" where we
show how to efficiently compute the (<= k)-level of n lines in the plane, each
of which lies on a distinct point (given in advance).
We complement our results by Omega(n log n) lower bounds under the algebraic
computation tree model for several related problems, including sorting a set of
points (according to, say, their x-order), each of which lies on a given line
known in advance. Therefore, the convex hull problem under our setting is
easier than sorting, contrary to the "standard" convex hull and sorting
problems, in which the two problems require Theta(n log n) steps in the worst
case (under the algebraic computation tree model).Comment: 26 pages, 5 figures, 1 appendix; a preliminary version appeared at
SoCG 201
Maximizing Maximal Angles for Plane Straight-Line Graphs
Let be a plane straight-line graph on a finite point set
in general position. The incident angles of a vertex
of are the angles between any two edges of that appear consecutively in
the circular order of the edges incident to .
A plane straight-line graph is called -open if each vertex has an
incident angle of size at least . In this paper we study the following
type of question: What is the maximum angle such that for any finite set
of points in general position we can find a graph from a certain
class of graphs on that is -open? In particular, we consider the
classes of triangulations, spanning trees, and paths on and give tight
bounds in most cases.Comment: 15 pages, 14 figures. Apart of minor corrections, some proofs that
were omitted in the previous version are now include
06481 Abstracts Collection -- Geometric Networks and Metric Space Embeddings
The Dagstuhl Seminar 06481 ``Geometric Networks and Metric Space
Embeddings\u27\u27 was held from November~26 to December~1, 2006 in the
International Conference and Research Center (IBFI), Schloss
Dagstuhl. During the seminar, several participants presented their
current research, and ongoing work and open problems were discussed.
In this paper we describe the seminar topics, we have compiled a
list of open questions that were posed during the seminar, there is
a list of all talks and there are abstracts of the presentations
given during the seminar. Links to extended abstracts or full
papers are provided where available
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