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Approximation Algorithms Inspired by Kernelization Methods

Abu-Khzam, Faisal N.; Bazgan, Cristina; Chopin, Morgan; Fernau, Henning (2014), Approximation Algorithms Inspired by Kernelization Methods, in Ahn, Hee-Kap; Shin, Chan-Su, Algorithms and Computation, Springer : Cham, p. 479-490. 10.1007/978-3-319-13075-0_38

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Type
Communication / Conférence
Date
2014
Conference title
25th International Symposium, ISAAC 2014
Conference date
2014-12
Conference city
Jeonju
Conference country
South Korea
Book title
Algorithms and Computation
Book author
Ahn, Hee-Kap; Shin, Chan-Su
Publisher
Springer
Published in
Cham
ISBN
978-3-319-13074-3
Number of pages
781
Pages
479-490
Publication identifier
Metadata
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Author(s)
Abu-Khzam, Faisal N.
Lebanese American University [LAU]
Bazgan, Cristina
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Chopin, Morgan
Institut für Optimierung und Operations Research
Fernau, Henning
Abstract (EN)
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 Harmless Set, Differential and Multiple Nonblocker, all of them can be considered in the context of securing networks or information propagation.
Subjects / Keywords
parameterized complexity; approximation
JEL
C44 - Operations Research; Statistical Decision Theory