Matchings on trees and the adjacency matrix: A determinantal viewpoint
Andr\'as M\'esz\'aros

TL;DR
This paper investigates the structure of uncovered vertices in maximum matchings on trees, revealing a determinantal process linked to the adjacency matrix and showing local approximation and entropy properties.
Contribution
It establishes that the uncovered vertices form a determinantal process and connects this to the adjacency matrix, providing new insights into maximum matchings on trees.
Findings
U(M_G) is a determinantal process.
Local neighborhoods can approximate U(M_G).
Normalized Shannon entropy is a continuous parameter.
Abstract
Let be a finite tree. For any matching of , let be the set of vertices uncovered by . Let be a uniform random maximum size matching of . In this paper, we analyze the structure of . We first show that is a determinantal process. We also show that for most vertices of , the process in a small neighborhood of that vertex can be well approximated based on a somewhat larger neighborhood of the same vertex. Then we show that the normalized Shannon entropy of can be also well approximated using the local structure of . In other words, in the realm of trees, the normalized Shannon entropy of -- that is, the normalized logarithm of the number of maximum size matchings of -- is a Benjamini-Schramm continuous parameter. We show that …
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.
Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Graph theory and applications · Random Matrices and Applications
