Suboptimality of local algorithms for a class of max-cut problems

© Institute of Mathematical Statistics, 2019. We show that in random K-uniform hypergraphs of constant average degree, for even K ≥ 4, local algorithms defined as factors of i.i.d. can not find nearly maximal cuts, when the average degree is sufficiently large. These algorithms have been used freque...

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Main Authors: Chen, Wei-Kuo, Gamarnik, David, Panchenko, Dmitry, Rahman, Mustazee
Other Authors: Sloan School of Management
Format: Article
Language:English
Published: Institute of Mathematical Statistics 2021
Online Access:https://hdl.handle.net/1721.1/134436
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author Chen, Wei-Kuo
Gamarnik, David
Panchenko, Dmitry
Rahman, Mustazee
author2 Sloan School of Management
author_facet Sloan School of Management
Chen, Wei-Kuo
Gamarnik, David
Panchenko, Dmitry
Rahman, Mustazee
author_sort Chen, Wei-Kuo
collection MIT
description © Institute of Mathematical Statistics, 2019. We show that in random K-uniform hypergraphs of constant average degree, for even K ≥ 4, local algorithms defined as factors of i.i.d. can not find nearly maximal cuts, when the average degree is sufficiently large. These algorithms have been used frequently to obtain lower bounds for the maxcut problem on random graphs, but it was not known whether they could be successful in finding nearly maximal cuts. This result follows from the fact that the overlap of any two nearly maximal cuts in such hypergraphs does not take values in a certain nontrivial interval-a phenomenon referred to as the overlap gap property-which is proved by comparing diluted models with large average degree with appropriate fully connected spin glass models and showing the overlap gap property in the latter setting.
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spelling mit-1721.1/1344362023-09-28T20:18:01Z Suboptimality of local algorithms for a class of max-cut problems Chen, Wei-Kuo Gamarnik, David Panchenko, Dmitry Rahman, Mustazee Sloan School of Management Massachusetts Institute of Technology. Department of Mathematics © Institute of Mathematical Statistics, 2019. We show that in random K-uniform hypergraphs of constant average degree, for even K ≥ 4, local algorithms defined as factors of i.i.d. can not find nearly maximal cuts, when the average degree is sufficiently large. These algorithms have been used frequently to obtain lower bounds for the maxcut problem on random graphs, but it was not known whether they could be successful in finding nearly maximal cuts. This result follows from the fact that the overlap of any two nearly maximal cuts in such hypergraphs does not take values in a certain nontrivial interval-a phenomenon referred to as the overlap gap property-which is proved by comparing diluted models with large average degree with appropriate fully connected spin glass models and showing the overlap gap property in the latter setting. 2021-10-27T20:05:00Z 2021-10-27T20:05:00Z 2019 2021-04-15T17:41:29Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134436 en 10.1214/18-AOP1291 The Annals of Probability Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Mathematical Statistics arXiv
spellingShingle Chen, Wei-Kuo
Gamarnik, David
Panchenko, Dmitry
Rahman, Mustazee
Suboptimality of local algorithms for a class of max-cut problems
title Suboptimality of local algorithms for a class of max-cut problems
title_full Suboptimality of local algorithms for a class of max-cut problems
title_fullStr Suboptimality of local algorithms for a class of max-cut problems
title_full_unstemmed Suboptimality of local algorithms for a class of max-cut problems
title_short Suboptimality of local algorithms for a class of max-cut problems
title_sort suboptimality of local algorithms for a class of max cut problems
url https://hdl.handle.net/1721.1/134436
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AT rahmanmustazee suboptimalityoflocalalgorithmsforaclassofmaxcutproblems