Efficient Algorithms for Minor Containment and Disjoint Paths in Almost-Linear Time

Основні поняття

The authors present an algorithm that can solve the Rooted Minor Containment problem in almost-linear fixed-parameter time, which implies the existence of an n^(1+o(1))-time algorithm for deciding membership in every minor-closed class of graphs. They also obtain an Ok(m^(1+o(1)))-time algorithm for the Disjoint Paths problem.

Анотація

The paper presents a series of algorithms for solving the Rooted Minor Containment problem and the Disjoint Paths problem in almost-linear fixed-parameter time.

The key ideas are:

1. For apex-minor-free graphs, the authors implement the irrelevant vertex technique efficiently using a dynamic treewidth data structure, allowing them to remove irrelevant vertices one by one in amortized no(1) time.

2. For clique-minor-free graphs, the authors reduce the problem to the apex-minor-free case by employing a version of the "recursive understanding" technique, which is implemented in almost-linear time using recent results on almost-linear time algorithms for flows and cuts.

3. The general case is reduced to the clique-minor-free case using a similar recursive scheme.

The authors also show that their results imply the existence of an n^(1+o(1))-time algorithm for deciding membership in every minor-closed class of graphs, as well as an Ok(m^(1+o(1)))-time algorithm for the Disjoint Paths problem.

Статистика

None.

Цитати

None.

Ключові висновки, отримані з

Minor Containment and Disjoint Paths in almost-linear time

by Tuuk... о arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.03958.pdf

Глибші Запити

What other problems in the theory of graph minors could benefit from the techniques introduced in this paper, such as the almost-linear time implementation of the irrelevant vertex rule and the recursive understanding scheme

The techniques introduced in this paper, such as the almost-linear time implementation of the irrelevant vertex rule and the recursive understanding scheme, could benefit various other problems in the theory of graph minors. One such problem is the Topological Minor Containment, which involves determining if a graph contains a given topological minor. By applying the almost-linear time implementation of the irrelevant vertex rule on graphs excluding a minor, similar to the approach taken in this paper, it may be possible to develop efficient algorithms for Topological Minor Containment. Additionally, problems related to finding forbidden minors in specific graph classes or characterizing graph families based on minor containment could also benefit from these techniques. The ability to efficiently locate and remove irrelevant vertices in larger batches, as demonstrated in this paper, can significantly improve the running time of algorithms for various graph minor-related problems.

How could the running time of the algorithms be further improved, for example, by obtaining a linear Oh,|X|(m) time algorithm for Rooted Minor Containment

To further improve the running time of the algorithms, particularly in the context of Rooted Minor Containment, obtaining a linear Oh,|X|(m) time algorithm would be a significant advancement. This improvement would require developing more efficient techniques for identifying and removing irrelevant vertices in graphs, especially in cases where the number of edges is linear in the number of vertices. By refining the algorithms to handle a larger number of edges while maintaining almost-linear time complexity, it may be possible to achieve a linear time algorithm for Rooted Minor Containment. This could involve optimizing the data structures and algorithms used in the process of locating and eliminating irrelevant vertices, as well as exploring new approaches to streamline the overall computation.

Are there any limitations or assumptions in the current approach that could be relaxed, such as the requirement for the number of edges to be linear in the number of vertices in Theorem 1.2

One limitation in the current approach is the assumption that the number of edges is linear in the number of vertices, as stated in Theorem 1.2. Relaxing this assumption to allow for a more general case where the number of edges may not be linear in the number of vertices could expand the applicability of the algorithms. By developing techniques that can handle graphs with varying edge densities, the algorithms could be adapted to a wider range of graph structures and scenarios. This relaxation of assumptions would require modifications to the existing algorithms to accommodate graphs with different edge-to-vertex ratios, potentially leading to more versatile and robust solutions for graph minor-related problems.