Kernelization is a formalization of preprocessing for combinatorially hard problems. We modify the standard definition for kernelization, which allows any polynomial-time algorithm for the preprocessing, by requiring instead that the preprocessing runs in a streaming setting and uses bits of memory on instances (x,k). We obtain several results in this new setting, depending on the number of passes over the input that such a streaming kernelization is allowed to make. Edge Dominating Set turns out as an interesting example because it has no single-pass kernelization but two passes over the input suffice to match the bounds of the best standard kernelization. © 2014 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Fafianie, S., & Kratsch, S. (2014). Streaming kernelization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8635 LNCS, pp. 275–286). Springer Verlag. https://doi.org/10.1007/978-3-662-44465-8_24
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