Simpler and Stronger Approaches for Non-Uniform Hypergraph Matching and the F\"uredi, Kahn, and Seymour Conjecture
Georg Anegg, Haris Angelidakis, Rico Zenklusen

TL;DR
This paper introduces simpler, more effective algorithms for non-uniform hypergraph matching, providing improved bounds and analysis that could advance understanding of the F"uredi, Kahn, and Seymour conjecture.
Contribution
The authors develop a straightforward exponential clocks-based algorithm that improves the sampling bounds and offers a new analysis applicable to hypergraph b-matching.
Findings
Achieves improved bounds with $g(e)=|e|-(|e|-1)x(e)$
Provides a simpler, more analyzable algorithm for hypergraph matching
Extends analysis to hypergraph b-matching problem
Abstract
A well-known conjecture of F\"uredi, Kahn, and Seymour (1993) on non-uniform hypergraph matching states that for any hypergraph with edge weights , there exists a matching such that the inequality holds with , where denotes the optimal value of the canonical LP relaxation. While the conjecture remains open, the strongest result towards it was very recently obtained by Brubach, Sankararaman, Srinivasan, and Xu (2020)---building on and strengthening prior work by Bansal, Gupta, Li, Mestre, Nagarajan, and Rudra (2012)---showing that the aforementioned inequality holds with . Actually, their method works in a more general sampling setting, where, given a point of the canonical LP relaxation, the task is to efficiently sample a matching…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Optimization and Search Problems
