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.APPROX-RANDOM.2016.34
URN: urn:nbn:de:0030-drops-66571
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2016/6657/
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Hazla, Jan ; Holenstein, Thomas ; Mossel, Elchanan

Lower Bounds on Same-Set Inner Product in Correlated Spaces

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Abstract

Let P be a probability distribution over a finite alphabet Omega^L with all L marginals equal. Let X^(1), ..., X^(L), where X^(j) = (X_1^(j), ..., X_n^(j)) be random vectors such that for every coordinate i in [n] the tuples (X_i^(1), ..., X_i^(L)) are i.i.d. according to P.

The question we address is: does there exist a function c_P independent of n such that for every f: Omega^n -> [0, 1] with E[f(X^(1))] = m > 0 we have E[f(X^(1)) * ... * f(X^(n))] > c_P(m) > 0?

We settle the question for L=2 and when L>2 and P has bounded correlation smaller than 1.

BibTeX - Entry

@InProceedings{hazla_et_al:LIPIcs:2016:6657,
  author =	{Jan Hazla and Thomas Holenstein and Elchanan Mossel},
  title =	{{Lower Bounds on Same-Set Inner Product in Correlated Spaces}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)},
  pages =	{34:1--34:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-018-7},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{60},
  editor =	{Klaus Jansen and Claire Mathieu and Jos{\'e} D. P. Rolim and Chris Umans},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2016/6657},
  URN =		{urn:nbn:de:0030-drops-66571},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2016.34},
  annote =	{Keywords: same set hitting, product spaces, correlation, lower bounds}
}

Keywords: same set hitting, product spaces, correlation, lower bounds
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016)
Issue Date: 2016
Date of publication: 06.09.2016

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