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An Improved Deterministic Parameterized Algorithm for Cactus Vertex Deletion

An improved deterministic parameterized algorithm for cactus vertex deletion
Authors: Yuuki Aoike; Tatsuya Gima; Tesshu Hanaka; Masashi Kiyomi; Yasuaki Kobayashi; Yusuke Kobayashi 0001; Kazuhiro Kurita; +1 Authors

An Improved Deterministic Parameterized Algorithm for Cactus Vertex Deletion

Abstract

A cactus is a connected graph that does not contain $K_4 - e$ as a minor. Given a graph $G = (V, E)$ and integer $k \ge 0$, Cactus Vertex Deletion (also known as Diamond Hitting Set) is the problem of deciding whether $G$ has a vertex set of size at most $k$ whose removal leaves a forest of cacti. The current best deterministic parameterized algorithm for this problem was due to Bonnet et al. [WG 2016], which runs in time $26^kn^{O(1)}$, where $n$ is the number of vertices of $G$. In this paper, we design a deterministic algorithm for Cactus Vertex Deletion, which runs in time $17.64^kn^{O(1)}$. As a straightforward application of our algorithm, we give a $17.64^kn^{O(1)}$-time algorithm for Even Cycle Transversal. The idea behind this improvement is to apply the measure and conquer analysis with a slightly elaborate measure of instances.

11 pages, 1 figure

Keywords

FOS: Computer and information sciences, Connectivity, Analysis of algorithms and problem complexity, even cycle transversal, Approximation algorithms, cactus vertex deletion, measure and conquer analysis, fixed-parameter tractable, Graph algorithms (graph-theoretic aspects), Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
Green