122 research outputs found
Approximate Distance Oracles for Planar Graphs with Improved Query Time-Space Tradeoff
We consider approximate distance oracles for edge-weighted n-vertex
undirected planar graphs. Given fixed epsilon > 0, we present a
(1+epsilon)-approximate distance oracle with O(n(loglog n)^2) space and
O((loglog n)^3) query time. This improves the previous best product of query
time and space of the oracles of Thorup (FOCS 2001, J. ACM 2004) and Klein
(SODA 2002) from O(n log n) to O(n(loglog n)^5).Comment: 20 pages, 9 figures of which 2 illustrate pseudo-code. This is the
SODA 2016 version but with the definition of C_i in Phase I fixed and the
analysis slightly modified accordingly. The main change is in the subsection
bounding query time and stretch for Phase
Exact Distance Oracles for Planar Graphs
We present new and improved data structures that answer exact node-to-node
distance queries in planar graphs. Such data structures are also known as
distance oracles. For any directed planar graph on n nodes with non-negative
lengths we obtain the following:
* Given a desired space allocation , we show how to
construct in time a data structure of size that answers
distance queries in time per query.
As a consequence, we obtain an improvement over the fastest algorithm for
k-many distances in planar graphs whenever .
* We provide a linear-space exact distance oracle for planar graphs with
query time for any constant eps>0. This is the first such data
structure with provable sublinear query time.
* For edge lengths at least one, we provide an exact distance oracle of space
such that for any pair of nodes at distance D the query time is
. Comparable query performance had been observed
experimentally but has never been explained theoretically.
Our data structures are based on the following new tool: given a
non-self-crossing cycle C with nodes, we can preprocess G in
time to produce a data structure of size that can
answer the following queries in time: for a query node u, output
the distance from u to all the nodes of C. This data structure builds on and
extends a related data structure of Klein (SODA'05), which reports distances to
the boundary of a face, rather than a cycle.
The best distance oracles for planar graphs until the current work are due to
Cabello (SODA'06), Djidjev (WG'96), and Fakcharoenphol and Rao (FOCS'01). For
and space , we essentially improve the query
time from to .Comment: To appear in the proceedings of the 23rd ACM-SIAM Symposium on
Discrete Algorithms, SODA 201
Better Tradeoffs for Exact Distance Oracles in Planar Graphs
We present an -space distance oracle for directed planar graphs
that answers distance queries in time. Our oracle both
significantly simplifies and significantly improves the recent oracle of
Cohen-Addad, Dahlgaard and Wulff-Nilsen [FOCS 2017], which uses
-space and answers queries in time. We achieve this by
designing an elegant and efficient point location data structure for Voronoi
diagrams on planar graphs.
We further show a smooth tradeoff between space and query-time. For any , we show an oracle of size that answers queries in time. This new tradeoff is currently the best (up to
polylogarithmic factors) for the entire range of and improves by polynomial
factors over all the previously known tradeoffs for the range
Exact Distance Oracles for Planar Graphs with Failing Vertices
We consider exact distance oracles for directed weighted planar graphs in the
presence of failing vertices. Given a source vertex , a target vertex
and a set of failed vertices, such an oracle returns the length of a
shortest -to- path that avoids all vertices in . We propose oracles
that can handle any number of failures. More specifically, for a directed
weighted planar graph with vertices, any constant , and for any , we propose an oracle of size
that answers queries in
time. In particular, we show an
-size, -query-time
oracle for any constant . This matches, up to polylogarithmic factors, the
fastest failure-free distance oracles with nearly linear space. For single
vertex failures (), our -size,
-query-time oracle improves over the previously best
known tradeoff of Baswana et al. [SODA 2012] by polynomial factors for , . For multiple failures, no planarity exploiting
results were previously known
Prioritized Metric Structures and Embedding
Metric data structures (distance oracles, distance labeling schemes, routing
schemes) and low-distortion embeddings provide a powerful algorithmic
methodology, which has been successfully applied for approximation algorithms
\cite{llr}, online algorithms \cite{BBMN11}, distributed algorithms
\cite{KKMPT12} and for computing sparsifiers \cite{ST04}. However, this
methodology appears to have a limitation: the worst-case performance inherently
depends on the cardinality of the metric, and one could not specify in advance
which vertices/points should enjoy a better service (i.e., stretch/distortion,
label size/dimension) than that given by the worst-case guarantee.
In this paper we alleviate this limitation by devising a suit of {\em
prioritized} metric data structures and embeddings. We show that given a
priority ranking of the graph vertices (respectively,
metric points) one can devise a metric data structure (respectively, embedding)
in which the stretch (resp., distortion) incurred by any pair containing a
vertex will depend on the rank of the vertex. We also show that other
important parameters, such as the label size and (in some sense) the dimension,
may depend only on . In some of our metric data structures (resp.,
embeddings) we achieve both prioritized stretch (resp., distortion) and label
size (resp., dimension) {\em simultaneously}. The worst-case performance of our
metric data structures and embeddings is typically asymptotically no worse than
of their non-prioritized counterparts.Comment: To appear at STOC 201
Efficient Dynamic Approximate Distance Oracles for Vertex-Labeled Planar Graphs
Let be a graph where each vertex is associated with a label. A
Vertex-Labeled Approximate Distance Oracle is a data structure that, given a
vertex and a label , returns a -approximation of
the distance from to the closest vertex with label in . Such
an oracle is dynamic if it also supports label changes. In this paper we
present three different dynamic approximate vertex-labeled distance oracles for
planar graphs, all with polylogarithmic query and update times, and nearly
linear space requirements
Linear-Space Approximate Distance Oracles for Planar, Bounded-Genus, and Minor-Free Graphs
A (1 + eps)-approximate distance oracle for a graph is a data structure that
supports approximate point-to-point shortest-path-distance queries. The most
relevant measures for a distance-oracle construction are: space, query time,
and preprocessing time. There are strong distance-oracle constructions known
for planar graphs (Thorup, JACM'04) and, subsequently, minor-excluded graphs
(Abraham and Gavoille, PODC'06). However, these require Omega(eps^{-1} n lg n)
space for n-node graphs. We argue that a very low space requirement is
essential. Since modern computer architectures involve hierarchical memory
(caches, primary memory, secondary memory), a high memory requirement in effect
may greatly increase the actual running time. Moreover, we would like data
structures that can be deployed on small mobile devices, such as handhelds,
which have relatively small primary memory. In this paper, for planar graphs,
bounded-genus graphs, and minor-excluded graphs we give distance-oracle
constructions that require only O(n) space. The big O hides only a fixed
constant, independent of \epsilon and independent of genus or size of an
excluded minor. The preprocessing times for our distance oracle are also faster
than those for the previously known constructions. For planar graphs, the
preprocessing time is O(n lg^2 n). However, our constructions have slower query
times. For planar graphs, the query time is O(eps^{-2} lg^2 n). For our
linear-space results, we can in fact ensure, for any delta > 0, that the space
required is only 1 + delta times the space required just to represent the graph
itself
- …