Saved in:
Bibliographic Details
Main Authors: Bhangale, Amey, Zhang, Yezhou
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2605.10031
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910207735496704
author Bhangale, Amey
Zhang, Yezhou
author_facet Bhangale, Amey
Zhang, Yezhou
contents We study the \emph{generalized min-sum set cover} (GMSSC) problem, where given a collection of hyperedges $E$ with arbitrary covering requirements $\{k_e \in \mathbb{Z}^+ : e \in E\}$, the objective is to find an ordering of the vertices that minimizes the total cover time of the hyperedges. A hyperedge $e$ is considered covered at the first time when $k_e$ of its vertices appear in the ordering. We present a $4.509$-approximation algorithm for GMSSC, improving upon the previous best-known guarantee of $4.642$~\cite[SODA'21]{BansalBFT21}. Our approach retains the general LP-based framework of Bansal, Batra, Farhadi, and Tetali~\cite{BansalBFT21} but provides an improved analysis that narrows the gap toward the lower bound of $4$-approximation assuming P$\neq$NP. Our analysis takes advantage of the constraints of the linear program in a nontrivial way, along with new lower-tail bounds for the sums of independent Bernoulli random variables, which could be of independent interest.
format Preprint
id arxiv_https___arxiv_org_abs_2605_10031
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle A 4.509-Approximation Algorithm for Generalized Min Sum Set Cover
Bhangale, Amey
Zhang, Yezhou
Data Structures and Algorithms
We study the \emph{generalized min-sum set cover} (GMSSC) problem, where given a collection of hyperedges $E$ with arbitrary covering requirements $\{k_e \in \mathbb{Z}^+ : e \in E\}$, the objective is to find an ordering of the vertices that minimizes the total cover time of the hyperedges. A hyperedge $e$ is considered covered at the first time when $k_e$ of its vertices appear in the ordering. We present a $4.509$-approximation algorithm for GMSSC, improving upon the previous best-known guarantee of $4.642$~\cite[SODA'21]{BansalBFT21}. Our approach retains the general LP-based framework of Bansal, Batra, Farhadi, and Tetali~\cite{BansalBFT21} but provides an improved analysis that narrows the gap toward the lower bound of $4$-approximation assuming P$\neq$NP. Our analysis takes advantage of the constraints of the linear program in a nontrivial way, along with new lower-tail bounds for the sums of independent Bernoulli random variables, which could be of independent interest.
title A 4.509-Approximation Algorithm for Generalized Min Sum Set Cover
topic Data Structures and Algorithms
url https://arxiv.org/abs/2605.10031