Distributed Lovász Local Lemma under Bandwidth Limitations

Fuente: arXiv
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Hauptverfasser: Halldórsson, Magnús M., Maus, Yannic, Peltonen, Saku
Format: Preprint
Veröffentlicht: 2024
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author Halldórsson, Magnús M.
Maus, Yannic
Peltonen, Saku
author_facet Halldórsson, Magnús M.
Maus, Yannic
Peltonen, Saku
contents The constructive Lovász Local Lemma has become a central tool for designing efficient distributed algorithms. While it has been extensively studied in the classic LOCAL model that uses unlimited bandwidth, much less is known in the bandwidth-restricted CONGEST model. In this paper, we present bandwidth- and time-efficient algorithms for various subclasses of LLL problems, including a large class of subgraph sampling problems that are naturally formulated as LLLs. Lastly, we use our LLLs to design efficient CONGEST algorithms for coloring sparse and triangle-free graphs with few colors. These coloring algorithms are exponentially faster than previous LOCAL model algorithms.
format Preprint
id arxiv_https___arxiv_org_abs_2405_07353
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Distributed Lovász Local Lemma under Bandwidth Limitations
Halldórsson, Magnús M.
Maus, Yannic
Peltonen, Saku
Data Structures and Algorithms
Distributed, Parallel, and Cluster Computing
The constructive Lovász Local Lemma has become a central tool for designing efficient distributed algorithms. While it has been extensively studied in the classic LOCAL model that uses unlimited bandwidth, much less is known in the bandwidth-restricted CONGEST model. In this paper, we present bandwidth- and time-efficient algorithms for various subclasses of LLL problems, including a large class of subgraph sampling problems that are naturally formulated as LLLs. Lastly, we use our LLLs to design efficient CONGEST algorithms for coloring sparse and triangle-free graphs with few colors. These coloring algorithms are exponentially faster than previous LOCAL model algorithms.
title Distributed Lovász Local Lemma under Bandwidth Limitations
topic Data Structures and Algorithms
Distributed, Parallel, and Cluster Computing
url https://arxiv.org/abs/2405.07353