Hyperbolic Concentration, Anti-concentration, and Discrepancy
Zhao Song, Ruizhe Zhang

TL;DR
This paper introduces new hyperbolic concentration and anti-concentration bounds, extending classical discrepancy results to hyperbolic polynomials and providing novel insights into hyperbolic and discrepancy theories.
Contribution
It presents nearly optimal hyperbolic Chernoff bounds, a hyperbolic anti-concentration bound, and generalizations of Kadison-Singer and Spencer theorems for hyperbolic polynomials.
Findings
Hyperbolic Chernoff bounds for Rademacher sums and random vectors.
A hyperbolic anti-concentration bound.
Generalizations of Kadison-Singer and Spencer theorems in hyperbolic setting.
Abstract
Chernoff bound is a fundamental tool in theoretical computer science. It has been extensively used in randomized algorithm design and stochastic type analysis. Discrepancy theory, which deals with finding a bi-coloring of a set system such that the coloring of each set is balanced, has a huge number of applications in approximation algorithms design. Chernoff bound [Che52] implies that a random bi-coloring of any set system with sets and elements will have discrepancy with high probability, while the famous result by Spencer [Spe85] shows that there exists an discrepancy solution. The study of hyperbolic polynomials dates back to the early 20th century when used to solve PDEs by G{\aa}rding [G{\aa}r59]. In recent years, more applications are found in control theory, optimization, real algebraic geometry, and so on. In particular, the…
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