Computer Science > Data Structures and Algorithms
[Submitted on 24 Sep 2025
]
Title: A Better-Than-$5/4$-Approximation for Two-Edge Connectivity
Title: 一种优于$5/4$的二边连通性近似方法
Abstract: The 2-Edge-Connected Spanning Subgraph Problem (2ECSS) is a fundamental problem in survivable network design. Given an undirected $2$-edge-connected graph, the goal is to find a $2$-edge-connected spanning subgraph with the minimum number of edges; a graph is 2-edge-connected if it is connected after the removal of any single edge. 2ECSS is APX-hard and has been extensively studied in the context of approximation algorithms. Very recently, Bosch-Calvo, Garg, Grandoni, Hommelsheim, Jabal Ameli, and Lindermayr showed the currently best-known approximation ratio of $\frac{5}{4}$ [STOC 2025]. This factor is tight for many of their techniques and arguments, and it was not clear whether $\frac{5}{4}$ can be improved. We break this natural barrier and present a $(\frac{5}{4} - \eta)$-approximation algorithm, for some constant $\eta \geq 10^{-6}$. On a high level, we follow the approach of previous works: take a triangle-free $2$-edge cover and transform it into a 2-edge-connected spanning subgraph by adding only a few additional edges. For $\geq \frac{5}{4}$-approximations, one can heavily exploit that a $4$-cycle in the 2-edge cover can ``buy'' one additional edge. This enables simple and nice techniques, but immediately fails for our improved approximation ratio. To overcome this, we design two complementary algorithms that perform well for different scenarios: one for few $4$-cycles and one for many $4$-cycles. Besides this, there appear more obstructions when breaching $\frac54$, which we surpass via new techniques such as colorful bridge covering, rich vertices, and branching gluing paths.
Submission history
From: Alexander Lindermayr [view email][v1] Wed, 24 Sep 2025 00:23:46 UTC (329 KB)
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.