English

Linear Sketching over $\mathbb F_2$

Data Structures and Algorithms 2016-11-14 v2

Abstract

We initiate a systematic study of linear sketching over F2\mathbb F_2. For a given Boolean function f ⁣:{0,1}n{0,1}f \colon \{0,1\}^n \to \{0,1\} a randomized F2\mathbb F_2-sketch is a distribution M\mathcal M over d×nd \times n matrices with elements over F2\mathbb F_2 such that Mx\mathcal Mx suffices for computing f(x)f(x) with high probability. We study a connection between F2\mathbb F_2-sketching and a two-player one-way communication game for the corresponding XOR-function. Our results show that this communication game characterizes F2\mathbb F_2-sketching under the uniform distribution (up to dependence on error). Implications of this result include: 1) a composition theorem for F2\mathbb F_2-sketching complexity of a recursive majority function, 2) a tight relationship between F2\mathbb F_2-sketching complexity and Fourier sparsity, 3) lower bounds for a certain subclass of symmetric functions. We also fully resolve a conjecture of Montanaro and Osborne regarding one-way communication complexity of linear threshold functions by designing an F2\mathbb F_2-sketch of optimal size. Furthermore, we show that (non-uniform) streaming algorithms that have to process random updates over F2\mathbb F_2 can be constructed as F2\mathbb F_2-sketches for the uniform distribution with only a minor loss. In contrast with the previous work of Li, Nguyen and Woodruff (STOC'14) who show an analogous result for linear sketches over integers in the adversarial setting our result doesn't require the stream length to be triply exponential in nn and holds for streams of length O~(n)\tilde O(n) constructed through uniformly random updates. Finally, we state a conjecture that asks whether optimal one-way communication protocols for XOR-functions can be constructed as F2\mathbb F_2-sketches with only a small loss.

Cite

@article{arxiv.1611.01879,
  title  = {Linear Sketching over $\mathbb F_2$},
  author = {Sampath Kannan and Elchanan Mossel and Grigory Yaroslavtsev},
  journal= {arXiv preprint arXiv:1611.01879},
  year   = {2016}
}
R2 v1 2026-06-22T16:43:41.209Z