English

Anticoncentration versus the number of subset sums

Combinatorics 2021-06-16 v2 Data Structures and Algorithms Probability

Abstract

Let w=(w1,,wn)Rn\vec{w} = (w_1,\dots, w_n) \in \mathbb{R}^{n}. We show that for any n2ϵ1n^{-2}\le\epsilon\le 1, if #{ξ{0,1}n:ξ,w=τ}2ϵn2n\#\{\vec{\xi} \in \{0,1\}^{n}: \langle \vec{\xi}, \vec{w} \rangle = \tau\} \ge 2^{-\epsilon n}\cdot 2^{n} for some τR\tau \in \mathbb{R}, then #{ξ,w:ξ{0,1}n}2O(ϵn).\#\{\langle \vec{\xi}, \vec{w} \rangle : \vec{\xi} \in \{0,1\}^{n}\} \le 2^{O(\sqrt{\epsilon}n)}. This exponentially improves the ϵ\epsilon dependence in a recent result of Nederlof, Pawlewicz, Swennenhuis, and W\k{e}grzycki and leads to a similar improvement in the parameterized (by the number of bins) runtime of bin packing.

Keywords

Cite

@article{arxiv.2101.07726,
  title  = {Anticoncentration versus the number of subset sums},
  author = {Vishesh Jain and Ashwin Sah and Mehtaab Sawhney},
  journal= {arXiv preprint arXiv:2101.07726},
  year   = {2021}
}

Comments

10 pages; revised version