Abstract
We develop a generic framework for deriving linear-size problem kernels for NP-hard problems on planar graphs. We demonstrate the usefulness of our framework in several concrete case studies, giving new kernelization results for CONNECTED VERTEX COVER, MINIMUM EDGE DOMINATING SET, MAXIMUM TRIANGLE PACKING, and EFFICIENT DOMINATING SET on planar graphs. On the route to these results, we present effective, problem-specific data reduction rules that are useful in any approach attacking the computational intractability of these problems. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Guo, J., & Niedermeier, R. (2007). Linear problem kernels for NP-hard problems on planar graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4596 LNCS, pp. 375–386). Springer Verlag. https://doi.org/10.1007/978-3-540-73420-8_34
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