Revised: December 13, 2019

Published: December 23, 2019

**Keywords:**discrepancy, random walks

**ACM Classification:**F.2.2, G.3

**AMS Classification:**68W20

**Abstract:**
[Plain Text Version]

A classic result of Banaszczyk (Random Str. & Algor. 1997) states that given any $n$ vectors in $\mathbb{R}^m$ with $\ell_2$-norm at most $1$ and any convex body $K$ in $\mathbb{R}^m$ of Gaussian measure at least half, there exists a $\pm 1$ combination of these vectors that lies in $5K$. Banaszczyk's proof of this result was non-constructive and it was open how to find such a $\pm 1$ combination in polynomial time. In this paper, we give an efficient randomized algorithm to find a $\pm 1$ combination of the vectors which lies in $cK$ for some fixed constant $c> 0$. This leads to new efficient algorithms for several problems in discrepancy theory.

-----------------

A conference version of this paper appeared in the Proceedings of the 50th ACM Symposium on Theory of Computing, 2018.