# Finding, Hitting and Packing Cycles in Subexponential Time on Unit Disk Graphs

## TL;DR

This paper introduces subexponential algorithms for finding cycles, paths, and feedback vertex sets in unit disk graphs, achieving faster runtimes than previous methods and establishing near-optimality under the ETH.

## Contribution

The paper presents the first subexponential parameterized algorithms for several cycle and path problems in unit disk graphs, utilizing novel tree decompositions with small separators.

## Key findings

- Algorithms run in $2^{O(\sqrt{k}\log{k})}
n^{O(1)}$ time.
- Outperforms previous algorithms with $2^{O(k^{0.75}\log{k})}
n^{O(1)}$ runtime.
- Algorithms are optimal up to a $\log{k}$ factor assuming ETH.

## Abstract

We give algorithms with running time $2^{O({\sqrt{k}\log{k}})} \cdot n^{O(1)}$ for the following problems. Given an $n$-vertex unit disk graph $G$ and an integer $k$, decide whether $G$ contains (1) a path on exactly/at least $k$ vertices, (2) a cycle on exactly $k$ vertices, (3) a cycle on at least $k$ vertices, (4) a feedback vertex set of size at most $k$, and (5) a set of $k$ pairwise vertex-disjoint cycles. For the first three problems, no subexponential time parameterized algorithms were previously known. For the remaining two problems, our algorithms significantly outperform the previously best known parameterized algorithms that run in time $2^{O(k^{0.75}\log{k})} \cdot n^{O(1)}$. Our algorithms are based on a new kind of tree decompositions of unit disk graphs where the separators can have size up to $k^{O(1)}$ and there exists a solution that crosses every separator at most $O(\sqrt{k})$ times. The running times of our algorithms are optimal up to the $\log{k}$ factor in the exponent, assuming the Exponential Time Hypothesis.