Smoothed Analysis of the Komlós Conjecture

Nikhil Bansal, Haotian Jiang, Raghu Meka, Sahil Singla, Makrand Sinha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The well-known Komlós conjecture states that given n vectors in ℝd with Euclidean norm at most one, there always exists a ±1 coloring such that the ℓ norm of the signed-sum vector is a constant independent of n and d. We prove this conjecture in a smoothed analysis setting where the vectors are perturbed by adding a small Gaussian noise and when the number of vectors n = ω(dlog d). The dependence of n on d is the best possible even in a completely random setting. Our proof relies on a weighted second moment method, where instead of considering uniformly randomly colorings we apply the second moment method on an implicit distribution on colorings obtained by applying the Gram-Schmidt walk algorithm to a suitable set of vectors. The main technical idea is to use various properties of these colorings, including subgaussianity, to control the second moment.

Original languageEnglish (US)
Title of host publication49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022
EditorsMikolaj Bojanczyk, Emanuela Merelli, David P. Woodruff
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772358
DOIs
StatePublished - Jul 1 2022
Externally publishedYes
Event49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022 - Paris, France
Duration: Jul 4 2022Jul 8 2022

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume229
ISSN (Print)1868-8969

Conference

Conference49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022
Country/TerritoryFrance
CityParis
Period7/4/227/8/22

Keywords

  • Komlós conjecture
  • smoothed analysis
  • subgaussian coloring
  • weighted second moment method

ASJC Scopus subject areas

  • Software

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