Best of two local models: Centralized local and distributed local algorithms

Guy Even; Moti Medina; Dana Ron

doi:10.1016/j.ic.2018.07.001

Best of two local models: Centralized local and distributed local algorithms

Guy Even, Moti Medina, Dana Ron

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

We consider two models of computation: centralized local algorithms and local distributed algorithms. Algorithms in one model are adapted to the other model to obtain improved algorithms. Distributed vertex coloring is employed to design improved centralized local algorithms for: maximal independent set, maximal matching, and an approximation scheme for maximum (weighted) matching over bounded degree graphs. The improvement is threefold: the algorithms are deterministic, stateless, and the number of probes grows polynomially in log^⁎⁡n, where n is the number of vertices of the input graph. The recursive centralized local improvement technique by Nguyen and Onak (FOCS 2008) is employed to obtain a distributed approximation scheme for maximum (weighted) matching.

Original language	English
Pages (from-to)	69-89
Number of pages	21
Journal	Information and Computation
Volume	262
DOIs	https://doi.org/10.1016/j.ic.2018.07.001
State	Published - 1 Oct 2018

Keywords

Centralized local algorithms
Distributed local algorithms
Graph algorithms
Maximum matching
Maximum weighted matching
Sublinear approximation algorithms

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Computer Science Applications
Computational Theory and Mathematics

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10.1016/j.ic.2018.07.001

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title = "Best of two local models: Centralized local and distributed local algorithms",

abstract = "We consider two models of computation: centralized local algorithms and local distributed algorithms. Algorithms in one model are adapted to the other model to obtain improved algorithms. Distributed vertex coloring is employed to design improved centralized local algorithms for: maximal independent set, maximal matching, and an approximation scheme for maximum (weighted) matching over bounded degree graphs. The improvement is threefold: the algorithms are deterministic, stateless, and the number of probes grows polynomially in log⁎⁡n, where n is the number of vertices of the input graph. The recursive centralized local improvement technique by Nguyen and Onak (FOCS 2008) is employed to obtain a distributed approximation scheme for maximum (weighted) matching.",

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author = "Guy Even and Moti Medina and Dana Ron",

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AU - Medina, Moti

AU - Ron, Dana

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N2 - We consider two models of computation: centralized local algorithms and local distributed algorithms. Algorithms in one model are adapted to the other model to obtain improved algorithms. Distributed vertex coloring is employed to design improved centralized local algorithms for: maximal independent set, maximal matching, and an approximation scheme for maximum (weighted) matching over bounded degree graphs. The improvement is threefold: the algorithms are deterministic, stateless, and the number of probes grows polynomially in log⁎⁡n, where n is the number of vertices of the input graph. The recursive centralized local improvement technique by Nguyen and Onak (FOCS 2008) is employed to obtain a distributed approximation scheme for maximum (weighted) matching.

AB - We consider two models of computation: centralized local algorithms and local distributed algorithms. Algorithms in one model are adapted to the other model to obtain improved algorithms. Distributed vertex coloring is employed to design improved centralized local algorithms for: maximal independent set, maximal matching, and an approximation scheme for maximum (weighted) matching over bounded degree graphs. The improvement is threefold: the algorithms are deterministic, stateless, and the number of probes grows polynomially in log⁎⁡n, where n is the number of vertices of the input graph. The recursive centralized local improvement technique by Nguyen and Onak (FOCS 2008) is employed to obtain a distributed approximation scheme for maximum (weighted) matching.

KW - Centralized local algorithms

KW - Distributed local algorithms

KW - Graph algorithms

KW - Maximum matching

KW - Maximum weighted matching

KW - Sublinear approximation algorithms

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SP - 69

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JO - Information and Computation

JF - Information and Computation

ER -

Best of two local models: Centralized local and distributed local algorithms

Abstract

Keywords

ASJC Scopus subject areas

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