On the exact space complexity of sketching and streaming small norms

  • Authors:
  • Daniel M. Kane;Jelani Nelson;David P. Woodruff

  • Affiliations:
  • Harvard University;MIT Computer Science and Artificial Intelligence Laboratory;IBM Almaden Research Center, San Jose, CA

  • Venue:
  • SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

We settle the 1-pass space complexity of (1 ± ε)-approximating the Lp norm, for real p with 1 ≤ p ≤ 2, of a length-n vector updated in a length-m stream with updates to its coordinates. We assume the updates are integers in the range [--M, M]. In particular, we show the space required is Θ(ε-2 log(mM) + log log(ns)) bits. Our result also holds for 0 p Lp is not a norm in this case, it remains a well-defined function. Our upper bound improves upon previous algorithms of [Indyk, JACM '06] and [Li, SODA '08]. This improvement comes from showing an improved derandomization of the Lp sketch of Indyk by using k-wise independence for small k, as opposed to using the heavy hammer of a generic pseudorandom generator against space-bounded computation such as Nisan's PRG. Our lower bound improves upon previous work of [Alon-Matias-Szegedy, JCSS '99] and [Woodruff, SODA '04], and is based on showing a direct sum property for the 1-way communication of the gap-Hamming problem.