Improved approximation algorithms for low-density instances of the Minimum Entropy Set Cover Problem
Data Structures and Algorithms
2012-08-01 v1 Computational Complexity
Abstract
We study the approximability of instances of the minimum entropy set cover problem, parameterized by the average frequency of a random element in the covering sets. We analyze an algorithm combining a greedy approach with another one biased towards large sets. The algorithm is controled by the percentage of elements to which we apply the biased approach. The optimal parameter choice has a phase transition around average density and leads to improved approximation guarantees when average element frequency is less than .
Cite
@article{arxiv.1207.7134,
title = {Improved approximation algorithms for low-density instances of the Minimum Entropy Set Cover Problem},
author = {Cosmin Bonchis and Gabriel Istrate},
journal= {arXiv preprint arXiv:1207.7134},
year = {2012}
}