The Gram-Schmidt Walk: A Cure for the Banaszczyk Blues

Abstract

An important result in discrepancy due to Banaszczyk states that for any set of $n$ vectors in ${\mathbb{R}}^m$ of ${\ell }_2$ 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 which lies in $5K$. This result implies the best known bounds for several problems in discrepancy. Banaszczyk's proof of this result is non-constructive and a major open problem has been to give an efficient algorithm to find such a $\pm 1$ combination of the vectors. In this paper, we resolve this question and give an efficient randomized algorithm to find a $\pm 1$ combination of the vectors which lies in $cK$ for $c>0$ an absolute constant. This leads to new efficient algorithms for several problems in discrepancy theory.

References in corpus (1)

• Constructive Discrepancy Minimization by Walking on The Edges