登录 注册

Optimal Bounds for the k-Disjoint Paths Problem

🔗 访问原文
🔗 Access Paper

📝 摘要
Abstract

The Graph Minors Series of Robertson and Seymour forms the foundation of algorithmic structural graph theory, yielding fixed-parameter algorithms for problems such as Disjoint Paths, Rooted Minor Checking, and Folio. A key ingredient behind the fixed-parameter tractability of the $k$-Disjoint Paths problem is the irrelevant-vertex technique. This machinery is governed by the Vital Linkage Theorem and the so-called Linkage Function $\ell$. However, despite its foundational role, the best known bounds on the Linkage Function are enormous and are only implicitly understood. The quantitative bounds behind these results have traditionally been so large that the resulting algorithms are regarded as "galactic". Our main result is a general irrelevant-vertex theorem for a common generalisation of $k$-Disjoint Paths and Rooted Minor Checking for graphs of size at most $d,$ commonly called the $(k,d)$-Folio problem. Specifically, we show that for any graph $G$ in which the $k$ terminals are chosen from some set $R,$ if the treewidth of $G$ exceeds $β(k,b,d)\in$ $2^{{\bf poly}(b + d)}$ $\cdot {\bf poly}(k)$ then we can locate an irrelevant vertex for the $(k,d)$-Folio problem. Here, the quantity $b$ is the bidimensionality of $R,$ that is, the largest $b$ for which a $(b\times b)$-grid minor in $G$ can be rooted on $R$. Thus, the exponential component of the irrelevant-vertex threshold is driven by the bound on the bidimensionality, rather than by the number of terminals, and we argue that this dependence is essentially optimal up to polynomial factors. As a consequence, the Linkage Function satisfies $\ell(k) \in 2^{{\bf poly}(k)}$. Beyond its structural significance, our result yields improved parameter dependencies for algorithms for Disjoint Paths and Rooted Minor Checking}, and provides a quantitative improvement for a broad range of graph-minor-based algorithmic frameworks.

📊 文章统计
Article Statistics

基础数据
Basic Stats

101 浏览
Views
0 下载
Downloads
21 引用
Citations

引用趋势
Citation Trend

阅读国家分布
Country Distribution

阅读机构分布
Institution Distribution

月度浏览趋势
Monthly Views

相关关键词
Related Keywords

影响因子分析
Impact Analysis

4.90 综合评分
Overall Score
引用影响力
Citation Impact
浏览热度
View Popularity
下载频次
Download Frequency

📄 相关文章
Related Articles

海洋智能分析Ocean AI Analysis

正在分析中,请稍候…Analyzing, please wait…
海洋智能体 🌊
海洋智能体
AI科研助手 · 2270篇文献
我看到你正在阅读一篇文献,需要我帮你解读摘要、推荐相关论文,或者分析研究方法论吗?