Structural Rounding: Approximation Algorithms for Graphs Near an Algorithmically Tractable Class
Erik D. Demaine, Timothy D. Goodrich, Kyle Kloster, Brian Lavallee,, Quanquan C. Liu, Blair D. Sullivan, Ali Vakilian, Andrew van der Poel

TL;DR
This paper introduces a framework for extending approximation algorithms to graphs close to tractable classes by editing them into such classes, with new algorithms and hardness results for various graph problems.
Contribution
It develops a general approach for applying approximation algorithms to near-tractable graphs through graph editing, and provides new algorithms and hardness bounds for multiple graph classes.
Findings
Developed a framework for graph editing to apply existing algorithms.
Provided bicriteria approximation algorithms for bounded degeneracy, treewidth, and pathwidth.
Established hardness of approximation results for the considered problems.
Abstract
We develop a new framework for generalizing approximation algorithms from the structural graph algorithm literature so that they apply to graphs somewhat close to that class (a scenario we expect is common when working with real-world networks) while still guaranteeing approximation ratios. The idea is to a given graph via vertex- or edge-deletions to put the graph into an algorithmically tractable class, apply known approximation algorithms for that class, and then the solution to apply to the original graph. We give a general characterization of when an optimization problem is amenable to this approach, and show that it includes many well-studied graph problems, such as Independent Set, Vertex Cover, Feedback Vertex Set, Minimum Maximal Matching, Chromatic Number, (-)Dominating Set, Edge (-)Dominating Set, and Connected Dominating Set. To…
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