On Approximating Partial Set Cover and Generalizations

TL;DR

This paper improves approximation algorithms for Partial Set Cover (PSC), connecting previous results with submodularity to simplify and enhance solutions, especially in geometric and sparse settings.

Contribution

It introduces improved approximation bounds for PSC by leveraging submodularity and extends existing algorithms to sparse set systems.

Findings

Improved approximation ratio to (1-1/e)(β + 1) for PSC.

Extended algorithms to handle sparse set systems.

Simplified analysis connecting PSC results with submodular functions.

Abstract

Partial Set Cover (PSC) is a generalization of the well-studied Set Cover problem (SC). In PSC the input consists of an integer $k$ and a set system $(U,S)$ where $U$ is a finite set, and $S \subseteq 2^U$ is a collection of subsets of $U$. The goal is to find a subcollection $S' \subseteq S$ of smallest cardinality such that sets in $S'$ cover at least $k$ elements of $U$; that is $|\cup_{A \in S'} A| \ge k$. SC is a special case of PSC when $k = |U|$. In the weighted version each set $X \in S$ has a non-negative weight $w(X)$ and the goal is to find a minimum weight subcollection to cover $k$ elements. Approximation algorithms for SC have been adapted to obtain comparable algorithms for PSC in various interesting cases. In recent work Inamdar and Varadarajan, motivated by geometric set systems, obtained a simple and elegant approach to reduce PSC to SC via the natural LP relaxation. They showed that if a deletion-closed family of SC admits a $\beta$-approximation via the natural LP relaxation, then one can obtain a $2(\beta + 1)$-approximation for PSC on the same family. In a subsequent paper, they also considered a generalization of PSC that has multiple partial covering constraints which is partly inspired by and generalizes previous work of Bera et al on the Vertex Cover problem. Our main goal in this paper is to demonstrate some useful connections between the results in previous work and submodularity. This allows us to simplify, and in some cases improve their results. We improve the approximation for PSC to $(1-1/e)(\beta + 1)$. We extend the previous work to the sparse setting.