Data Reductions and Combinatorial Bounds for Improved Approximation Algorithms
Data Structures and Algorithms
2014-09-15 v1
Abstract
Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of reduction rules and combinatorial insights. We will expose in this paper a similar strategy for obtaining polynomial-time approximation algorithms. Our method features the use of approximation-preserving reductions, akin to the notion of parameterized reductions. We exemplify this method to obtain the currently best approximation algorithms for \textsc{Harmless Set}, \textsc{Differential} and \textsc{Multiple Nonblocker}, all of them can be considered in the context of securing networks or information propagation.
Cite
@article{arxiv.1409.3742,
title = {Data Reductions and Combinatorial Bounds for Improved Approximation Algorithms},
author = {Faisal N. Abu-Khzam and Cristina Bazgan and Morgan Chopin and Henning Fernau},
journal= {arXiv preprint arXiv:1409.3742},
year = {2014}
}