Streaming Kernelization
Data Structures and Algorithms
2014-05-07 v1
Abstract
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 . 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.
Keywords
Cite
@article{arxiv.1405.1356,
title = {Streaming Kernelization},
author = {Stefan Fafianie and Stefan Kratsch},
journal= {arXiv preprint arXiv:1405.1356},
year = {2014}
}