2 research outputs found
Deterministic coloring algorithms in the LOCAL model
We study the problem of bi-chromatic coloring of hypergraphs in the LOCAL
distributed model of computation. This problem can easily be solved by a
randomized local algorithm with no communication. However, it is not known how
to solve it deterministically with only a polylogarithmic number of
communication rounds. In this paper we indeed design such a deterministic
algorithm that solves this problem with polylogarithmic number of communication
rounds. This is an almost exponential improvement on the previously known
deterministic local algorithms for this problem. Because the bi-chromatic
coloring of hypergraphs problem is known to be complete in the class of all
locally checkable graph problems, our result implies deterministic local
algorithms with polylogarithmic number of communication rounds for all such
problems for which an efficient randomized algorithm exists. This solves one of
the fundamental open problems in the area of local distributed graph
algorithms. By reductions due to Ghaffari, Kuhn and Maus [STOC 2017] this
implies such polylogarithmically efficient deterministic local algorithms for
many graph problems.Comment: Version date: 10 July 2019; some typos corrected; added explanation
p. 5; paper submitted to ACM-SIAM SODA 2020; 13 page
Polylogarithmic-Time Deterministic Network Decomposition and Distributed Derandomization
We present a simple polylogarithmic-time deterministic distributed algorithm
for network decomposition. This improves on a celebrated -time algorithm of Panconesi and Srinivasan [STOC'92] and settles a
central and long-standing question in distributed graph algorithms. It also
leads to the first polylogarithmic-time deterministic distributed algorithms
for numerous other problems, hence resolving several well-known and decades-old
open problems, including Linial's question about the deterministic complexity
of maximal independent set [FOCS'87; SICOMP'92]---which had been called the
most outstanding problem in the area.
The main implication is a more general distributed derandomization theorem:
Put together with the results of Ghaffari, Kuhn, and Maus [STOC'17] and
Ghaffari, Harris, and Kuhn [FOCS'18], our network decomposition implies that
That is, for any problem whose solution can be checked deterministically in
polylogarithmic-time, any polylogarithmic-time randomized algorithm can be
derandomized to a polylogarithmic-time deterministic algorithm. Informally, for
the standard first-order interpretation of efficiency as polylogarithmic-time,
distributed algorithms do not need randomness for efficiency.
By known connections, our result leads also to substantially faster
randomized distributed algorithms for a number of well-studied problems
including -coloring, maximal independent set, and Lov\'{a}sz Local
Lemma, as well as massively parallel algorithms for -coloring.Comment: Extended version of an article that appears at the Symposium on
Theory of Computing (STOC) 202