Approximating Hereditary Discrepancy via Small Width Ellipsoids

TL;DR

This paper presents a simple, efficient approximation algorithm for hereditary discrepancy of matrices, linking it to a geometric convex program involving ellipsoids, advancing computational methods in discrepancy theory.

Contribution

It introduces a new $O( ext{log}^{3/2} n)$-approximation algorithm for hereditary discrepancy based on a geometric convex program.

Findings

Provides a simple approximation algorithm with $O( ext{log}^{3/2} n)$ factor.

Characterizes hereditary discrepancy via a convex geometric program.

Links discrepancy measures to ellipsoid containment problems.

Abstract

The Discrepancy of a hypergraph is the minimum attainable value, over two-colorings of its vertices, of the maximum absolute imbalance of any hyperedge. The Hereditary Discrepancy of a hypergraph, defined as the maximum discrepancy of a restriction of the hypergraph to a subset of its vertices, is a measure of its complexity. Lovasz, Spencer and Vesztergombi (1986) related the natural extension of this quantity to matrices to rounding algorithms for linear programs, and gave a determinant based lower bound on the hereditary discrepancy. Matousek (2011) showed that this bound is tight up to a polylogarithmic factor, leaving open the question of actually computing this bound. Recent work by Nikolov, Talwar and Zhang (2013) showed a polynomial time $\tilde{O}(\log^3 n)$-approximation to hereditary discrepancy, as a by-product of their work in differential privacy. In this paper, we give a direct simple $O(\log^{3/2} n)$-approximation algorithm for this problem. We show that up to this approximation factor, the hereditary discrepancy of a matrix $A$ is characterized by the optimal value of simple geometric convex program that seeks to minimize the largest $\ell_{\infty}$ norm of any point in a ellipsoid containing the columns of $A$. This characterization promises to be a useful tool in discrepancy theory.