Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
96 tokens/sec
Gemini 2.5 Pro Premium
44 tokens/sec
GPT-5 Medium
18 tokens/sec
GPT-5 High Premium
18 tokens/sec
GPT-4o
105 tokens/sec
DeepSeek R1 via Azure Premium
83 tokens/sec
GPT OSS 120B via Groq Premium
475 tokens/sec
Kimi K2 via Groq Premium
259 tokens/sec
2000 character limit reached

Convex minorant trees associated with Brownian paths and the continuum limit of the minimum spanning tree (2307.12260v1)

Published 23 Jul 2023 in math.PR and math.CO

Abstract: We give an explicit construction of the scaling limit of the minimum spanning tree of the complete graph. The limit object is described using a recursive construction involving the convex minorants of a Brownian motion with parabolic drift (and countably many i.i.d. uniform random variables); we call it the Brownian parabolic tree. Aside from the new representation, this point of view has multiple consequences. For instance, it permits us to prove that its Hausdorff dimension is almost surely 3. It also intrinsically contains information related to some underlying dynamics: one notable by-product is the construction of a standard metric multiplicative coalescent which couples the scaling limits of random graphs at different points of the critical window in terms of the same simple building blocks. The above results actually fit in a more general framework. They result from the introduction of a new family of continuum random trees associated with functions via their convex minorants, that we call convex minorant trees. We initiate the study of these structures in the case of Brownian-like paths. In passing, we prove that the convex minorant tree of a Brownian excursion is a Brownian continuum ranndom tree, and that it provides a coupling between the Aldous--Pitman fragmentation of the Brownian continuum random tree and its representation by Bertoin.

Citations (3)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube