Polynomial Data Structure Lower Bounds in the Group Model
Speaker: SashaTitle: Polynomial Data Structure Lower Bounds in the Group Model
Date: 11 May 2020 12:00pm-1:00pm
Location: Zoom
Food: Self-prepared
Audience background: Elementary probability theory
Zoom link: https://harvard.zoom.us/j/91580536039
Abstract: Proving super-logarithmic data structure lower bounds in the static group model has been a fundamental challenge in computational geometry since the early 80’s. We prove a polynomial $n^{\Omega(1)}$ lower bound for an explicit range counting problem of $n^3$ convex polygons in $R^2$ (each with $n^{O~(1)}$ facets/semialgebraic-complexity), against linear storage arithmetic data structures in the group model. Our construction and analysis are based on a combination of techniques in Diophantine approximation, pseudorandomness, and compressed sensing—in particular, on the existence and partial derandomization of optimal binary compressed sensing matrices in the polynomial sparsity regime ($k = n^{1-\delta}$). As a byproduct, this establishes a (logarithmic) separation between compressed sensing matrices and the stronger RIP property.
Based on a joint work with Gleb Posobin, Oded Regev, and Omri Weinstein: https://eccc.weizmann.ac.il/report/2020/057/