Multi-parameter complexity analysis for constrained size graph problems: using greediness for parameterization
Abstract
We study the parameterized complexity of a broad class of problems called "local graph partitioning problems" that includes the classical fixed cardinality problems as max k-vertex cover, k-densest subgraph, etc. By developing a technique "greediness-for-parameterization", we obtain fixed parameter algorithms with respect to a pair of parameters k, the size of the solution (but not its value) and \Delta, the maximum degree of the input graph. In particular, greediness-for-parameterization improves asymptotic running times for these problems upon random separation (that is a special case of color coding) and is more intuitive and simple. Then, we show how these results can be easily extended for getting standard-parameterization results (i.e., with parameter the value of the optimal solution) for a well known local graph partitioning problem.
Cite
@article{arxiv.1306.2217,
title = {Multi-parameter complexity analysis for constrained size graph problems: using greediness for parameterization},
author = {Edouard Bonnet and Bruno Escoffier and Vangelis Th. Paschos and Emeric Tourniaire},
journal= {arXiv preprint arXiv:1306.2217},
year = {2013}
}
Comments
16 pages, 4 figures