11 research outputs found
Data Structures for Halfplane Proximity Queries and Incremental Voronoi Diagrams
We consider preprocessing a set of points in convex position in the
plane into a data structure supporting queries of the following form: given a
point and a directed line in the plane, report the point of that
is farthest from (or, alternatively, nearest to) the point among all points
to the left of line . We present two data structures for this problem.
The first data structure uses space and preprocessing
time, and answers queries in time, for any . The second data structure uses space and
polynomial preprocessing time, and answers queries in time. These
are the first solutions to the problem with query time and
space.
The second data structure uses a new representation of nearest- and
farthest-point Voronoi diagrams of points in convex position. This
representation supports the insertion of new points in clockwise order using
only amortized pointer changes, in addition to -time
point-location queries, even though every such update may make
combinatorial changes to the Voronoi diagram. This data structure is the first
demonstration that deterministically and incrementally constructed Voronoi
diagrams can be maintained in amortized pointer changes per operation
while keeping -time point-location queries.Comment: 17 pages, 6 figures. Various small improvements. To appear in
Algorithmic
Sublinear Explicit Incremental Planar Voronoi Diagrams
A data structure is presented that explicitly maintains the graph of a
Voronoi diagram of point sites in the plane or the dual graph of a convex
hull of points in three dimensions while allowing insertions of new
sites/points. Our structure supports insertions in
expected amortized time, where suppresses polylogarithmic terms.
This is the first result to achieve sublinear time insertions; previously it
was shown by Allen et al. that amortized combinatorial
changes per insertion could occur in the Voronoi diagram but a sublinear-time
algorithm was only presented for the special case of points in convex position.Comment: 14 pages, 10 figures. Presented ant JCDCGGG 201
A Randomized Incremental Algorithm for the Hausdorff Voronoi Diagram of Non-crossing Clusters
In the Hausdorff Voronoi diagram of a family of \emph{clusters of points} in
the plane, the distance between a point and a cluster is measured as
the maximum distance between and any point in , and the diagram is
defined in a nearest-neighbor sense for the input clusters. In this paper we
consider %El."non-crossing" \emph{non-crossing} clusters in the plane, for
which the combinatorial complexity of the Hausdorff Voronoi diagram is linear
in the total number of points, , on the convex hulls of all clusters. We
present a randomized incremental construction, based on point location, that
computes this diagram in expected time and expected
space. Our techniques efficiently handle non-standard characteristics of
generalized Voronoi diagrams, such as sites of non-constant complexity, sites
that are not enclosed in their Voronoi regions, and empty Voronoi regions. The
diagram finds direct applications in VLSI computer-aided design.Comment: arXiv admin note: substantial text overlap with arXiv:1306.583
Searching edges in the overlap of two plane graphs
Consider a pair of plane straight-line graphs, whose edges are colored red
and blue, respectively, and let n be the total complexity of both graphs. We
present a O(n log n)-time O(n)-space technique to preprocess such pair of
graphs, that enables efficient searches among the red-blue intersections along
edges of one of the graphs. Our technique has a number of applications to
geometric problems. This includes: (1) a solution to the batched red-blue
search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an
algorithm to compute the maximum vertical distance between a pair of 3D
polyhedral terrains one of which is convex in O(n log n) time, where n is the
total complexity of both terrains; (3) an algorithm to construct the Hausdorff
Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n)
time and O(n+m) space, where n is the total number of points in all clusters
and m is the number of crossings between all clusters; (4) an algorithm to
construct the farthest-color Voronoi diagram of the corners of n axis-aligned
rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle
problem for n parallel line segments in the plane in optimal O(n log n) time.
All these results are new or improve on the best known algorithms.Comment: 22 pages, 6 figure
Dynamic Geometric Independent Set
We present fully dynamic approximation algorithms for the Maximum Independent
Set problem on several types of geometric objects: intervals on the real line,
arbitrary axis-aligned squares in the plane and axis-aligned -dimensional
hypercubes.
It is known that a maximum independent set of a collection of intervals
can be found in time, while it is already \textsf{NP}-hard for a
set of unit squares. Moreover, the problem is inapproximable on many important
graph families, but admits a \textsf{PTAS} for a set of arbitrary pseudo-disks.
Therefore, a fundamental question in computational geometry is whether it is
possible to maintain an approximate maximum independent set in a set of dynamic
geometric objects, in truly sublinear time per insertion or deletion. In this
work, we answer this question in the affirmative for intervals, squares and
hypercubes.
First, we show that for intervals a -approximate maximum
independent set can be maintained with logarithmic worst-case update time. This
is achieved by maintaining a locally optimal solution using a constant number
of constant-size exchanges per update.
We then show how our interval structure can be used to design a data
structure for maintaining an expected constant factor approximate maximum
independent set of axis-aligned squares in the plane, with polylogarithmic
amortized update time. Our approach generalizes to -dimensional hypercubes,
providing a -approximation with polylogarithmic update time.
Those are the first approximation algorithms for any set of dynamic arbitrary
size geometric objects; previous results required bounded size ratios to obtain
polylogarithmic update time. Furthermore, it is known that our results for
squares (and hypercubes) cannot be improved to a
-approximation with the same update time
On the hausdorff and other cluster Voronoi diagrams
The Voronoi diagram is a fundamental geometric structure that encodes proximity information. Given a set of geometric objects, called sites, their Voronoi diagram is a subdivision of the underlying space into maximal regions, such that all points within one region have the same nearest site. Problems in diverse application domains (such as VLSI CAD, robotics, facility location, etc.) demand various generalizations of this simple concept. While many generalized Voronoi diagrams have been well studied, many others still have unsettled questions. An example of the latter are cluster Voronoi diagrams, whose sites are sets (clusters) of objects rather than individual objects. In this dissertation we study certain cluster Voronoi diagrams from the perspective of their construction algorithms and algorithmic applications. Our main focus is the Hausdorff Voronoi diagram; we also study the farthest-segment Voronoi diagram, as well as certain special cases of the farthest-color Voronoi diagram. We establish a connection between cluster Voronoi diagrams and the stabbing circle problem for segments in the plane. Our results are as follows. (1) We investigate the randomized incremental construction of the Hausdorff Voronoi diagram. We consider separately the case of non-crossing clusters, when the combinatorial complexity of the diagram is O(n) where n is the total number of points in all clusters. For this case, we present two construction algorithms that require O(n log2 n) expected time. For the general case of arbitrary clusters, we present an algorithm that requires O((m + n log n) log n) expected time and O(m + n log n) expected space, where m is a parameter reflecting the number of crossings between clusters' convex hulls. (2) We present an O(n) time algorithm to construct the farthest-segment Voronoi diagram of n segments, after the sequence of its faces at infinity is known. This augments the well-known linear-time framework for Voronoi diagram of points in convex position, with the ability to handle disconnected Voronoi regions. (3) We establish a connection between the cluster Voronoi diagrams (the Hausdorff and the farthest-color Voronoi diagram) and the stabbing circle problem. This implies a new method to solve the latter problem. Our method results in a near-optimal O(n log2 n) time algorithm for a set of n parallel segments, and in an optimal O(n log n) time algorithm for a set of n segments satisfying some other special conditions. (4) We study the farthest-color Voronoi diagram in special cases considered by the stabbing circle problem. We prove O(n) bound for its combinatorial complexity and present an O(nlogn) time algorithm to construct it
Tightening curves and graphs on surfaces
Any continuous deformation of closed curves on a surface can be decomposed into a finite sequence of local changes on the structure of the curves; we refer to such local operations as homotopy moves. Tightening is the process of deforming given curves into their minimum position; that is, those with minimum number of self-intersections. While such operations and the tightening process has been studied extensively, surprisingly little is known about the quantitative bounds on the number of homotopy moves required to tighten an arbitrary curve.
An unexpected connection exists between homotopy moves and a set of local operations on graphs called electrical transformations. Electrical transformations have been used to simplify electrical networks since the 19th century; later they have been used for solving various combinatorial problems on graphs, as well as applications in statistical mechanics, robotics, and quantum mechanics. Steinitz, in his study of 3-dimensional polytopes, looked at the electrical transformations through the lens of medial construction, and implicitly established the connection to homotopy moves; later the same observation has been discovered independently in the context of knots.
In this thesis, we study the process of tightening curves on surfaces using homotopy moves and their consequences on electrical transformations from a quantitative perspective. To derive upper and lower bounds we utilize tools like curve invariants, surface theory, combinatorial topology, and hyperbolic geometry. We develop several new tools to construct efficient algorithms on tightening curves and graphs, as well as to present examples where no efficient algorithm exists. We then argue that in order to study electrical transformations, intuitively it is most beneficial to work with monotonic homotopy moves instead, where no new crossings are created throughout the process; ideas and proof techniques that work for monotonic homotopy moves should transfer to those for electrical transformations. We present conjectures and partial evidence supporting the argument