New Tools and Connections for Exponential-time Approximation
Nikhil Bansal, Parinya Chalermsook, Bundit Laekhanukit, Danupon, Nanongkai, Jesper Nederlof

TL;DR
This paper introduces new exponential-time approximation algorithms for problems like maximum independent set, chromatic number, and vertex cover, surpassing previous bounds and linking PCP parameters to approximation limits.
Contribution
It presents novel algorithms with improved exponential bounds and establishes a connection between PCP parameters and exponential-time approximation complexity.
Findings
Improved exponential bounds for maximum independent set and chromatic number.
New randomized branching rule for approximation algorithms.
Refutation of overly optimistic PCP size reductions based on these results.
Abstract
In this paper, we develop new tools and connections for exponential time approximation. In this setting, we are given a problem instance and a parameter , and the goal is to design an -approximation algorithm with the fastest possible running time. We show the following results: - An -approximation for maximum independent set in time, - An -approximation for chromatic number in time, - A -approximation for minimum vertex cover in time, and - A -approximation for minimum -hypergraph vertex cover in time. (Throughout, and omit and factors polynomial in the input size, respectively.) The best known time bounds for all problems were …
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
