Finding Large Set Covers Faster via the Representation Method

TL;DR

This paper introduces a faster Monte Carlo algorithm for the Set Cover problem that exploits the representation method to outperform the classic dynamic programming approach for certain instance sizes.

Contribution

It presents a novel Monte Carlo algorithm leveraging the representation method to solve specific Set Cover instances faster than the traditional $O^*(2^n)$ algorithm.

Findings

Achieves $O^*(2^{(1- ext{Omega}(\sigma^4))n})$ runtime for certain Set Cover instances.

Applies the approach to graph chromatic number problems via reduction.

Provides probabilistic guarantees for the algorithm's correctness.

Abstract

The worst-case fastest known algorithm for the Set Cover problem on universes with $n$ elements still essentially is the simple $O^*(2^n)$-time dynamic programming algorithm, and no non-trivial consequences of an $O^*(1.01^n)$-time algorithm are known. Motivated by this chasm, we study the following natural question: Which instances of Set Cover can we solve faster than the simple dynamic programming algorithm? Specifically, we give a Monte Carlo algorithm that determines the existence of a set cover of size $\sigma n$ in $O^*(2^{(1-\Omega(\sigma^4))n})$ time. Our approach is also applicable to Set Cover instances with exponentially many sets: By reducing the task of finding the chromatic number $\chi(G)$ of a given $n$-vertex graph $G$ to Set Cover in the natural way, we show there is an $O^*(2^{(1-\Omega(\sigma^4))n})$-time randomized algorithm that given integer $s=\sigma n$, outputs NO if $\chi(G) > s$ and YES with constant probability if $\chi(G)\leq s-1$. On a high level, our results are inspired by the `representation method' of Howgrave-Graham and Joux~[EUROCRYPT'10] and obtained by only evaluating a randomly sampled subset of the table entries of a dynamic programming algorithm.