License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CCC.2019.8
URN: urn:nbn:de:0030-drops-108309
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/10830/
O'Donnell, Ryan ;
Schramm, Tselil
Sherali - Adams Strikes Back
Abstract
Let G be any n-vertex graph whose random walk matrix has its nontrivial eigenvalues bounded in magnitude by 1/sqrt{Delta} (for example, a random graph G of average degree Theta(Delta) typically has this property). We show that the exp(c (log n)/(log Delta))-round Sherali - Adams linear programming hierarchy certifies that the maximum cut in such a G is at most 50.1 % (in fact, at most 1/2 + 2^{-Omega(c)}). For example, in random graphs with n^{1.01} edges, O(1) rounds suffice; in random graphs with n * polylog(n) edges, n^{O(1/log log n)} = n^{o(1)} rounds suffice.
Our results stand in contrast to the conventional beliefs that linear programming hierarchies perform poorly for max-cut and other CSPs, and that eigenvalue/SDP methods are needed for effective refutation. Indeed, our results imply that constant-round Sherali - Adams can strongly refute random Boolean k-CSP instances with n^{ceil[k/2] + delta} constraints; previously this had only been done with spectral algorithms or the SOS SDP hierarchy.
BibTeX - Entry
@InProceedings{odonnell_et_al:LIPIcs:2019:10830,
author = {Ryan O'Donnell and Tselil Schramm},
title = {{Sherali - Adams Strikes Back}},
booktitle = {34th Computational Complexity Conference (CCC 2019)},
pages = {8:1--8:30},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-116-0},
ISSN = {1868-8969},
year = {2019},
volume = {137},
editor = {Amir Shpilka},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2019/10830},
URN = {urn:nbn:de:0030-drops-108309},
doi = {10.4230/LIPIcs.CCC.2019.8},
annote = {Keywords: Linear programming, Sherali, Adams, max-cut, graph eigenvalues, Sum-of-Squares}
}
Keywords: |
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Linear programming, Sherali, Adams, max-cut, graph eigenvalues, Sum-of-Squares |
Collection: |
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34th Computational Complexity Conference (CCC 2019) |
Issue Date: |
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2019 |
Date of publication: |
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16.07.2019 |