5,185 research outputs found
Super-Fast 3-Ruling Sets
A -ruling set of a graph is a vertex-subset
that is independent and satisfies the property that every vertex is
at a distance of at most from some vertex in . A \textit{maximal
independent set (MIS)} is a 1-ruling set. The problem of computing an MIS on a
network is a fundamental problem in distributed algorithms and the fastest
algorithm for this problem is the -round algorithm due to Luby
(SICOMP 1986) and Alon et al. (J. Algorithms 1986) from more than 25 years ago.
Since then the problem has resisted all efforts to yield to a sub-logarithmic
algorithm. There has been recent progress on this problem, most importantly an
-round algorithm on graphs with
vertices and maximum degree , due to Barenboim et al. (Barenboim,
Elkin, Pettie, and Schneider, April 2012, arxiv 1202.1983; to appear FOCS
2012).
We approach the MIS problem from a different angle and ask if O(1)-ruling
sets can be computed much more efficiently than an MIS? As an answer to this
question, we show how to compute a 2-ruling set of an -vertex graph in
rounds. We also show that the above result can be improved
for special classes of graphs such as graphs with high girth, trees, and graphs
of bounded arboricity.
Our main technique involves randomized sparsification that rapidly reduces
the graph degree while ensuring that every deleted vertex is close to some
vertex that remains. This technique may have further applications in other
contexts, e.g., in designing sub-logarithmic distributed approximation
algorithms. Our results raise intriguing questions about how quickly an MIS (or
1-ruling sets) can be computed, given that 2-ruling sets can be computed in
sub-logarithmic rounds
Super-Fast Distributed Algorithms for Metric Facility Location
This paper presents a distributed O(1)-approximation algorithm, with
expected- running time, in the model for
the metric facility location problem on a size- clique network. Though
metric facility location has been considered by a number of researchers in
low-diameter settings, this is the first sub-logarithmic-round algorithm for
the problem that yields an O(1)-approximation in the setting of non-uniform
facility opening costs. In order to obtain this result, our paper makes three
main technical contributions. First, we show a new lower bound for metric
facility location, extending the lower bound of B\u{a}doiu et al. (ICALP 2005)
that applies only to the special case of uniform facility opening costs. Next,
we demonstrate a reduction of the distributed metric facility location problem
to the problem of computing an O(1)-ruling set of an appropriate spanning
subgraph. Finally, we present a sub-logarithmic-round (in expectation)
algorithm for computing a 2-ruling set in a spanning subgraph of a clique. Our
algorithm accomplishes this by using a combination of randomized and
deterministic sparsification.Comment: 15 pages, 2 figures. This is the full version of a paper that
appeared in ICALP 201
On Derandomizing Local Distributed Algorithms
The gap between the known randomized and deterministic local distributed
algorithms underlies arguably the most fundamental and central open question in
distributed graph algorithms. In this paper, we develop a generic and clean
recipe for derandomizing LOCAL algorithms. We also exhibit how this simple
recipe leads to significant improvements on a number of problem. Two main
results are:
- An improved distributed hypergraph maximal matching algorithm, improving on
Fischer, Ghaffari, and Kuhn [FOCS'17], and giving improved algorithms for
edge-coloring, maximum matching approximation, and low out-degree edge
orientation. The first gives an improved algorithm for Open Problem 11.4 of the
book of Barenboim and Elkin, and the last gives the first positive resolution
of their Open Problem 11.10.
- An improved distributed algorithm for the Lov\'{a}sz Local Lemma, which
gets closer to a conjecture of Chang and Pettie [FOCS'17], and moreover leads
to improved distributed algorithms for problems such as defective coloring and
-SAT.Comment: 37 page
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