Multi-parameter Complexity Analysis for Constrained Size Graph Problems: Using Greediness for Parameterization

TL;DR: Greediness-for-parameterization improves asymptotic running times for these problems upon random separation and is more intuitive and simple.

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 that we call “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 \(\varDelta\), 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.