Abstract
Preprocessing or data reductions means reducing the input to something simpler by solving an easy part of the input and this is the type of algorithms used in almost every application. In spite of wide practical applications of preprocessing, a systematic theoretical study of such algorithms remains elusive. The framework of parameterized complexity can be used as an approach to analyse preprocessing algorithms. Input to parameterized algorithms include a parameter (in addition to the input) which is likely to be small, and this resulted in a study of preprocessing algorithms that reduce the size of the input to a pure function of the parameter (independent of the input size). Such type of preprocessing algorithms are called kernelization algorithms.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bodlaender, H.L., Downey, R.G., Fellows, M.R., Hermelin, J.: On problems without polynomial kernels. J. Comput. Syst. Sci. 75, 423–434 (2009)
Chen, J., Kanj, I.A., Jia, W.: Vertex cover: further observations and further improvements. Journal of Algorithms 41, 280–301 (2001)
Downey, R.G., Fellows, M.R.: Parameterized complexity. Springer, New York (1999)
Fellows, M.R.: The Lost Continent of Polynomial Time: Preprocessing and Kernelization. In: Bodlaender, H.L., Langston, M.A. (eds.) IWPEC 2006. LNCS, vol. 4169, pp. 276–277. Springer, Heidelberg (2006)
Flum, J., Grohe, M.: Parameterized Complexity Theory. Texts in Theoretical Computer Science. An EATCS Series. Springer, Berlin (2006)
Guo, J., Niedermeier, R.: Invitation to data reduction and problem kernelization. SIGACT News 38, 31–45 (2007)
Misra, N., Raman, V., Saurabh, S.: Lower bounds on kernelization. Discrete Optim. 8, 110–128 (2011)
Niedermeier, R.: Invitation to fixed-parameter algorithms. Oxford Lecture Series in Mathematics and its Applications, vol. 31. Oxford University Press, Oxford (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fomin, F.V. (2013). Kernelization Algorithms. In: Chang, RS., Jain, L., Peng, SL. (eds) Advances in Intelligent Systems and Applications - Volume 1. Smart Innovation, Systems and Technologies, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35452-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-35452-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35451-9
Online ISBN: 978-3-642-35452-6
eBook Packages: EngineeringEngineering (R0)