English

Structural Parameterizations with Modulator Oblivion

Data Structures and Algorithms 2020-02-25 v1

Abstract

It is known that problems like Vertex Cover, Feedback Vertex Set and Odd Cycle Transversal are polynomial time solvable in the class of chordal graphs. We consider these problems in a graph that has at most kk vertices whose deletion results in a chordal graph, when parameterized by kk. While this investigation fits naturally into the recent trend of what are called `structural parameterizations', here we assume that the deletion set is not given. One method to solve them is to compute a kk-sized or an approximate (f(k)f(k) sized, for a function ff) chordal vertex deletion set and then use the structural properties of the graph to design an algorithm. This method leads to at least kO(k)nO(1)k^{\mathcal{O}(k)}n^{\mathcal{O}(1)} running time when we use the known parameterized or approximation algorithms for finding a kk-sized chordal deletion set on an nn vertex graph. In this work, we design 2O(k)nO(1)2^{\mathcal{O}(k)}n^{\mathcal{O}(1)} time algorithms for these problems. Our algorithms do not compute a chordal vertex deletion set (or even an approximate solution). Instead, we construct a tree decomposition of the given graph in time 2O(k)nO(1)2^{\mathcal{O}(k)}n^{\mathcal{O}(1)} where each bag is a union of four cliques and O(k)\mathcal{O}(k) vertices. We then apply standard dynamic programming algorithms over this special tree decomposition. This special tree decomposition can be of independent interest. Our algorithms are adaptive (robust) in the sense that given an integer kk, they detect whether the graph has a chordal vertex deletion set of size at most kk or output the special tree decomposition and solve the problem. We also show lower bounds for the problems we deal with under the Strong Exponential Time Hypothesis (SETH).

Keywords

Cite

@article{arxiv.2002.09972,
  title  = {Structural Parameterizations with Modulator Oblivion},
  author = {Ashwin Jacob and Fahad Panolan and Venkatesh Raman and Vibha Sahlot},
  journal= {arXiv preprint arXiv:2002.09972},
  year   = {2020}
}