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 combine the method of conditional expectation with network decompositions to obtain a generic and c...

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Published in:2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) pp. 662 - 673
Main Authors: Ghaffari, Mohsen, Harris, David G., Kuhn, Fabian
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2018
Subjects:
Approximation algorithms
Color
Complexity theory
Computational modeling
Computer science
derandomization
Distributed algorithms
hypergraph matching
LOCAL
Lovász Local Lemma
ISSN:2575-8454
Online Access:Get full text
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Abstract 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 combine the method of conditional expectation with network decompositions to obtain a generic and clean recipe for derandomizing LOCAL algorithms. This leads to significant improvements on a number of problems, in cases resolving known open problems. Two main results are: - An improved deterministic distributed algorithm for hypergraph maximal matching, improving on Fischer, Ghaffari, and Kuhn [FOCS '17]. This yields improved algorithms for edge-coloring, maximum matching approximation, and low out-degree edge orientation. The last result gives the first positive resolution in the Open Problem 11.10 in the book of Barenboim and Elkin. - Improved randomized and deterministic distributed algorithms for the Lovász Local Lemma, which get closer to a conjecture of Chang and Pettie [FOCS '17].
AbstractList 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 combine the method of conditional expectation with network decompositions to obtain a generic and clean recipe for derandomizing LOCAL algorithms. This leads to significant improvements on a number of problems, in cases resolving known open problems. Two main results are: - An improved deterministic distributed algorithm for hypergraph maximal matching, improving on Fischer, Ghaffari, and Kuhn [FOCS '17]. This yields improved algorithms for edge-coloring, maximum matching approximation, and low out-degree edge orientation. The last result gives the first positive resolution in the Open Problem 11.10 in the book of Barenboim and Elkin. - Improved randomized and deterministic distributed algorithms for the Lovász Local Lemma, which get closer to a conjecture of Chang and Pettie [FOCS '17].
Author Harris, David G.
Kuhn, Fabian
Ghaffari, Mohsen
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Snippet The gap between the known randomized and deterministic local distributed algorithms underlies arguably the most fundamental and central open question in...
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SubjectTerms Approximation algorithms
Color
Complexity theory
Computational modeling
Computer science
derandomization
Distributed algorithms
hypergraph matching
LOCAL
Lovász Local Lemma
Title On Derandomizing Local Distributed Algorithms
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