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On Regularity Lemmas and their Algorithmic Applications

Published online by Cambridge University Press:  28 March 2017

JACOB FOX ,
LÁSZLÓ MIKLÓS LOVÁSZ  and
YUFEI ZHAO
JACOB FOX
Affiliation:
Department of Mathematics, Stanford University, Stanford, CA 94305, USA (e-mail: jacobfox@stanford.edu)
LÁSZLÓ MIKLÓS LOVÁSZ
Affiliation:
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA (e-mail: lmlovasz@math.mit.edu)
YUFEI ZHAO
Affiliation:
Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK (e-mail: yufei.zhao@maths.ox.ac.uk)
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Abstract

Szemerédi's regularity lemma and its variants are some of the most powerful tools in combinatorics. In this paper, we establish several results around the regularity lemma. First, we prove that whether or not we include the condition that the desired vertex partition in the regularity lemma is equitable has a minimal effect on the number of parts of the partition. Second, we use an algorithmic version of the (weak) Frieze–Kannan regularity lemma to give a substantially faster deterministic approximation algorithm for counting subgraphs in a graph. Previously, only an exponential dependence for the running time on the error parameter was known, and we improve it to a polynomial dependence. Third, we revisit the problem of finding an algorithmic regularity lemma, giving approximation algorithms for several co-NP-complete problems. We show how to use the weak Frieze–Kannan regularity lemma to approximate the regularity of a pair of vertex subsets. We also show how to quickly find, for each ε′>ε, an ε′-regular partition with k parts if there exists an ε-regular partition with k parts. Finally, we give a simple proof of the permutation regularity lemma which improves the tower-type bound on the number of parts in the previous proofs to a single exponential bound.

Keywords

Primary 05C85Secondary 05C5005D99

Information

Type
Paper
Information
Combinatorics, Probability and Computing , Volume 26 , Issue 4 , July 2017 , pp. 481 - 505
Copyright
Copyright © Cambridge University Press 2017 

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