Streaming Kernelization

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8635)


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 \(\mathcal{O}(poly(k)\log|x|)\) 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.


Local Memory Vertex Cover Input Stream Vertex Deletion Problem Kernel 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.TU BerlinGermany

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