75,953 research outputs found
Fully Dynamic Algorithms for Minimum Weight Cycle and Related Problems
We consider the directed minimum weight cycle problem in the fully dynamic
setting. To the best of our knowledge, so far no fully dynamic algorithms have
been designed specifically for the minimum weight cycle problem in general
digraphs. One can achieve amortized update time by simply
invoking the fully dynamic APSP algorithm of Demetrescu and Italiano [J.
ACM'04]. This bound, however, yields no improvement over the trivial
recompute-from-scratch algorithm for sparse graphs.
Our first contribution is a very simple deterministic
-approximate algorithm supporting vertex updates (i.e., changing
all edges incident to a specified vertex) in conditionally near-optimal
amortized time for digraphs with real edge
weights in . Using known techniques, the algorithm can be implemented on
planar graphs and also gives some new sublinear fully dynamic algorithms
maintaining approximate cuts and flows in planar digraphs.
Additionally, we show a Monte Carlo randomized exact fully dynamic minimum
weight cycle algorithm with worst-case update that works
for real edge weights. To this end, we generalize the exact fully dynamic APSP
data structure of Abraham et al. [SODA'17] to solve the ``multiple-pairs
shortest paths problem'', where one is interested in computing distances for
some (instead of all ) fixed source-target pairs after each update. We
show that in such a scenario, worst-case update time
is possible.Comment: Full version of an ICALP 2021 pape
The Power of Dynamic Distance Oracles: Efficient Dynamic Algorithms for the Steiner Tree
In this paper we study the Steiner tree problem over a dynamic set of
terminals. We consider the model where we are given an -vertex graph
with positive real edge weights, and our goal is to maintain a tree
which is a good approximation of the minimum Steiner tree spanning a terminal
set , which changes over time. The changes applied to the
terminal set are either terminal additions (incremental scenario), terminal
removals (decremental scenario), or both (fully dynamic scenario). Our task
here is twofold. We want to support updates in sublinear time, and keep
the approximation factor of the algorithm as small as possible. We show that we
can maintain a -approximate Steiner tree of a general graph in
time per terminal addition or removal. Here,
denotes the stretch of the metric induced by . For planar graphs we achieve
the same running time and the approximation ratio of .
Moreover, we show faster algorithms for incremental and decremental scenarios.
Finally, we show that if we allow higher approximation ratio, even more
efficient algorithms are possible. In particular we show a polylogarithmic time
-approximate algorithm for planar graphs.
One of the main building blocks of our algorithms are dynamic distance
oracles for vertex-labeled graphs, which are of independent interest. We also
improve and use the online algorithms for the Steiner tree problem.Comment: Full version of the paper accepted to STOC'1
Adsorption of Multi-block and Random Copolymer on a Solid Surface: Critical Behavior and Phase Diagram
The adsorption of a single multi-block -copolymer on a solid planar
substrate is investigated by means of computer simulations and scaling
analysis. It is shown that the problem can be mapped onto an effective
homopolymer adsorption problem. In particular we discuss how the critical
adsorption energy and the fraction of adsorbed monomers depend on the block
length of sticking monomers , and on the total length of the polymer
chains. Also the adsorption of the random copolymers is considered and found to
be well described within the framework of the annealed approximation. For a
better test of our theoretical prediction, two different Monte Carlo (MC)
simulation methods were employed: a) off-lattice dynamic bead-spring model,
based on the standard Metropolis algorithm (MA), and b) coarse-grained lattice
model using the Pruned-enriched Rosenbluth method (PERM) which enables tests
for very long chains. The findings of both methods are fully consistent and in
good agreement with theoretical predictions.Comment: 27 pages, 12 figure
Optimal decremental connectivity in planar graphs
We show an algorithm for dynamic maintenance of connectivity information in
an undirected planar graph subject to edge deletions. Our algorithm may answer
connectivity queries of the form `Are vertices and connected with a
path?' in constant time. The queries can be intermixed with any sequence of
edge deletions, and the algorithm handles all updates in time. This
results improves over previously known time algorithm
Decremental Single-Source Reachability in Planar Digraphs
In this paper we show a new algorithm for the decremental single-source
reachability problem in directed planar graphs. It processes any sequence of
edge deletions in total time and explicitly
maintains the set of vertices reachable from a fixed source vertex. Hence, if
all edges are eventually deleted, the amortized time of processing each edge
deletion is only , which improves upon a previously
known solution. We also show an algorithm for decremental
maintenance of strongly connected components in directed planar graphs with the
same total update time. These results constitute the first almost optimal (up
to polylogarithmic factors) algorithms for both problems.
To the best of our knowledge, these are the first dynamic algorithms with
polylogarithmic update times on general directed planar graphs for non-trivial
reachability-type problems, for which only polynomial bounds are known in
general graphs
The Predicted-Deletion Dynamic Model: Taking Advantage of ML Predictions, for Free
The main bottleneck in designing efficient dynamic algorithms is the unknown
nature of the update sequence. In particular, there are some problems, like
3-vertex connectivity, planar digraph all pairs shortest paths, and others,
where the separation in runtime between the best partially dynamic solutions
and the best fully dynamic solutions is polynomial, sometimes even exponential.
In this paper, we formulate the predicted-deletion dynamic model, motivated
by a recent line of empirical work about predicting edge updates in dynamic
graphs. In this model, edges are inserted and deleted online, and when an edge
is inserted, it is accompanied by a "prediction" of its deletion time. This
models real world settings where services may have access to historical data or
other information about an input and can subsequently use such information make
predictions about user behavior. The model is also of theoretical interest, as
it interpolates between the partially dynamic and fully dynamic settings, and
provides a natural extension of the algorithms with predictions paradigm to the
dynamic setting.
We give a novel framework for this model that "lifts" partially dynamic
algorithms into the fully dynamic setting with little overhead. We use our
framework to obtain improved efficiency bounds over the state-of-the-art
dynamic algorithms for a variety of problems. In particular, we design
algorithms that have amortized update time that scales with a partially dynamic
algorithm, with high probability, when the predictions are of high quality. On
the flip side, our algorithms do no worse than existing fully-dynamic
algorithms when the predictions are of low quality. Furthermore, our algorithms
exhibit a graceful trade-off between the two cases. Thus, we are able to take
advantage of ML predictions asymptotically "for free.'
Incremental and Decremental Maintenance of Planar Width
We present an algorithm for maintaining the width of a planar point set
dynamically, as points are inserted or deleted. Our algorithm takes time
O(kn^epsilon) per update, where k is the amount of change the update causes in
the convex hull, n is the number of points in the set, and epsilon is any
arbitrarily small constant. For incremental or decremental update sequences,
the amortized time per update is O(n^epsilon).Comment: 7 pages; 2 figures. A preliminary version of this paper was presented
at the 10th ACM/SIAM Symp. Discrete Algorithms (SODA '99); this is the
journal version, and will appear in J. Algorithm
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
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