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RANDOM
Streaming Algorithms with Large Approximation Factors

Authors: Yi Li, Honghao Lin, David P. Woodruff, and Yuheng Zhang

Published in: LIPIcs, Volume 245, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)


Abstract
We initiate a broad study of classical problems in the streaming model with insertions and deletions in the setting where we allow the approximation factor α to be much larger than 1. Such algorithms can use significantly less memory than the usual setting for which α = 1+ε for an ε ∈ (0,1). We study large approximations for a number of problems in sketching and streaming, assuming that the underlying n-dimensional vector has all coordinates bounded by M throughout the data stream: 1) For the 𝓁_p norm/quasi-norm, 0 < p ≤ 2, we show that obtaining a poly(n)-approximation requires the same amount of memory as obtaining an O(1)-approximation for any M = n^Θ(1), which holds even for randomly ordered streams or for streams in the bounded deletion model. 2) For estimating the 𝓁_p norm, p > 2, we show an upper bound of O(n^{1-2/p} (log n log M)/α²) bits for an α-approximation, and give a matching lower bound for linear sketches. 3) For the 𝓁₂-heavy hitters problem, we show that the known lower bound of Ω(k log nlog M) bits for identifying (1/k)-heavy hitters holds even if we are allowed to output items that are 1/(α k)-heavy, provided the algorithm succeeds with probability 1-O(1/n). We also obtain a lower bound for linear sketches that is tight even for constant failure probability algorithms. 4) For estimating the number 𝓁₀ of distinct elements, we give an n^{1/t}-approximation algorithm using O(tlog log M) bits of space, as well as a lower bound of Ω(t) bits, both excluding the storage of random bits, where n is the dimension of the underlying frequency vector and M is an upper bound on the magnitude of its coordinates. 5) For α-approximation to the Schatten-p norm, we give near-optimal Õ(n^{2-4/p}/α⁴) sketching dimension for every even integer p and every α ≥ 1, while for p not an even integer we obtain near-optimal sketching dimension once α = Ω(n^{1/q-1/p}), where q is the largest even integer less than p. The latter is surprising as it is unknown what the complexity of Schatten-p norm estimation is for constant approximation; we show once the approximation factor is at least n^{1/q-1/p}, we can obtain near-optimal sketching bounds.

Cite as

Yi Li, Honghao Lin, David P. Woodruff, and Yuheng Zhang. Streaming Algorithms with Large Approximation Factors. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 13:1-13:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{li_et_al:LIPIcs.APPROX/RANDOM.2022.13,
  author =	{Li, Yi and Lin, Honghao and Woodruff, David P. and Zhang, Yuheng},
  title =	{{Streaming Algorithms with Large Approximation Factors}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
  pages =	{13:1--13:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-249-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{245},
  editor =	{Chakrabarti, Amit and Swamy, Chaitanya},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2022.13},
  URN =		{urn:nbn:de:0030-drops-171354},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2022.13},
  annote =	{Keywords: streaming algorithms, 𝓁\underlinep norm, heavy hitters, distinct elements}
}
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