Improved Approximations for Flexible Network Design
Dylan Hyatt-Denesik, Afrouz Jabal Ameli, Laura Sanita
TL;DR
The authors address flexible connectivity problems where a graph is partitioned into safe and unsafe elements, focusing on both edge-connectivity (FGC) and vertex-connectivity (FVC) variants and their higher-connectivity generalizations. They introduce two approximation schemes for FVC, achieving a first factor strictly below 2 via APX1 ($\approx$ $5/3$, with a $4/3$-case) and APX2 ($11/7$) that leverages ear-decompositions and rainbow-connection techniques. For FGC, they refine existing analyses to obtain a tighter bound (notably $10/7$) via a refined decomposition of safe and unsafe parts and contraction-based arguments, and they extend to a $1+O\left(\frac{1}{\sqrt{k}}\right)$-approximation for $k$-FGC. The work combines ear-decomposition, block theory, matroid intersection, and rainbow-connection concepts to push closer to optimal resilience with scalable guarantees, impacting design of fault-tolerant networks under partial failure models.
Abstract
Flexible network design deals with building a network that guarantees some connectivity requirements between its vertices, even when some of its elements (like vertices or edges) fail. In particular, the set of edges (resp. vertices) of a given graph are here partitioned into safe and unsafe. The goal is to identify a minimum size subgraph that is 2-edge-connected (resp. 2-vertex-connected), and stay so whenever any of the unsafe elements gets removed. In this paper, we provide improved approximation algorithms for flexible network design problems, considering both edge-connectivity and vertex-connectivity, as well as connectivity values higher than 2. For the vertex-connectivity variant, in particular, our algorithm is the first with approximation factor strictly better than 2.
