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Data reductions and combinatorial bounds for improved approximation algorithms.
- Source :
-
Journal of Computer & System Sciences . May2016, Vol. 82 Issue 3, p503-520. 18p. - Publication Year :
- 2016
-
Abstract
- Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data 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 Harmless Set , Differential and Multiple Nonblocker , all of them can be considered in the context of securing networks or information propagation. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00220000
- Volume :
- 82
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Journal of Computer & System Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 112056707
- Full Text :
- https://doi.org/10.1016/j.jcss.2015.11.010