Tight Lower Bound for Linear Sketches of Moments

The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the p [superscript th] moment, for p ∈ (0,2] has been settled [KNW10], for p > 2 the exact complexi...

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Main Authors: Andoni, Alexandr, Nguyen, Huy L., Polyanskiy, Yury, Wu, Yihong
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: 2014
Online Access:http://hdl.handle.net/1721.1/90562
https://orcid.org/0000-0002-2109-0979
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author Andoni, Alexandr
Nguyen, Huy L.
Polyanskiy, Yury
Wu, Yihong
author2 Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
author_facet Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Andoni, Alexandr
Nguyen, Huy L.
Polyanskiy, Yury
Wu, Yihong
author_sort Andoni, Alexandr
collection MIT
description The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the p [superscript th] moment, for p ∈ (0,2] has been settled [KNW10], for p > 2 the exact complexity remains open. For p > 2 the current best algorithm uses O(n [superscript 1 − 2/p] logn) words of space [AKO11,BO10], whereas the lower bound is of Ω(n [superscript 1 − 2/p]) [BJKS04]. In this paper, we show a tight lower bound of Ω(n [superscript 1 − 2/p] logn) words for the class of algorithms based on linear sketches, which store only a sketch Ax of input vector x and some (possibly randomized) matrix A. We note that all known algorithms for this problem are linear sketches.
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spelling mit-1721.1/905622022-10-01T13:43:09Z Tight Lower Bound for Linear Sketches of Moments Andoni, Alexandr Nguyen, Huy L. Polyanskiy, Yury Wu, Yihong Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Polyanskiy, Yury The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the p [superscript th] moment, for p ∈ (0,2] has been settled [KNW10], for p > 2 the exact complexity remains open. For p > 2 the current best algorithm uses O(n [superscript 1 − 2/p] logn) words of space [AKO11,BO10], whereas the lower bound is of Ω(n [superscript 1 − 2/p]) [BJKS04]. In this paper, we show a tight lower bound of Ω(n [superscript 1 − 2/p] logn) words for the class of algorithms based on linear sketches, which store only a sketch Ax of input vector x and some (possibly randomized) matrix A. We note that all known algorithms for this problem are linear sketches. National Science Foundation (U.S.). Center for Science of Information (Grant Agreement CCF-0939370) 2014-10-06T18:41:47Z 2014-10-06T18:41:47Z 2013 Article http://purl.org/eprint/type/ConferencePaper 978-3-642-39205-4 978-3-642-39206-1 0302-9743 1611-3349 http://hdl.handle.net/1721.1/90562 Andoni, Alexandr, Huy L. Nguyen, Yury Polyanskiy, and Yihong Wu. “Tight Lower Bound for Linear Sketches of Moments.” Lecture Notes in Computer Science (2013): 25–32. https://orcid.org/0000-0002-2109-0979 en_US http://dx.doi.org/10.1007/978-3-642-39206-1_3 Automata, Languages, and Programming Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf MIT web domain
spellingShingle Andoni, Alexandr
Nguyen, Huy L.
Polyanskiy, Yury
Wu, Yihong
Tight Lower Bound for Linear Sketches of Moments
title Tight Lower Bound for Linear Sketches of Moments
title_full Tight Lower Bound for Linear Sketches of Moments
title_fullStr Tight Lower Bound for Linear Sketches of Moments
title_full_unstemmed Tight Lower Bound for Linear Sketches of Moments
title_short Tight Lower Bound for Linear Sketches of Moments
title_sort tight lower bound for linear sketches of moments
url http://hdl.handle.net/1721.1/90562
https://orcid.org/0000-0002-2109-0979
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