Lasserre Integrality Gaps for Graph Spanners and Related Problems

Michael Dinitz, Yasamin Nazari, Zeyu Zhang

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

There has been significant recent progress on algorithms for approximating graph spanners, i.e., algorithms which approximate the best spanner for a given input graph. Essentially all of these algorithms use the same basic LP relaxation, so a variety of papers have studied the limitations of this approach and proved integrality gaps for this LP. We extend these results by showing that even the strongest lift-and-project methods cannot help significantly, by proving polynomial integrality gaps even for nΩ ( ε ) levels of the Lasserre hierarchy, for both the directed and undirected spanner problems. We also extend these integrality gaps to related problems, notably Directed Steiner Network and Shallow-Light Steiner Network.
Original languageEnglish
Title of host publicationApproximation and Online Algorithms
Subtitle of host publication18th International Workshop, WAOA 2020, Virtual Event, September 9–10, 2020, Revised Selected Papers
EditorsChristos Kaklamanis, Asaf Levin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages97-112
Number of pages16
ISBN (Electronic)9783030808792
ISBN (Print)9783030808785
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event18th International Workshop on Approximation and Online Algorithms, WAOA 2019 - Virtual, Online
Duration: 9 Sept 202010 Sept 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12806 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameWAOA: International Workshop on Approximation and Online Algorithms
Volume2020

Conference

Conference18th International Workshop on Approximation and Online Algorithms, WAOA 2019
CityVirtual, Online
Period9/09/2010/09/20

Funding

Supported in part by NSF award CCF-1909111.

FundersFunder number
National Science FoundationCCF-1909111

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