On weighted partial triangulations of convex polygons
This provides a direct efficient method for exact sampling in a broad class of weighted geometric partition problems, offering a compelling alternative to Markov chain approaches with suboptimal bounds.
The paper presents a randomized algorithm for exact sampling from the distribution of weighted partial triangulations of a convex polygon, achieving expected time O((n√λ+1) log n) for large n, which is nearly optimal.
We study the problem of sampling weighted partial triangulations of a convex polygon. We consider the distribution where each partial triangulation $σ$ is chosen with probability proportional to $λ^{|σ|}$, where $λ>0$ is a model parameter and $|σ|$ denotes the number of diagonals in $σ$. This model belongs to a broad class of weighted geometric partition problems that include lattice triangulations and dyadic tilings, and is closely related to several classical combinatorial structures, including the full triangulations of a convex polygon and the associated Catalan structures. While prior work has largely focused on Markov chain approaches, often only providing suboptimal mixing time bounds, we provide a direct efficient method for exact sampling. Our main result is a randomized algorithm that outputs an exact sample from the target distribution in expected time $O\big((n\sqrtλ+1)\log n\big)$ for all sufficiently large $n$. This provides a nearly optimal sampling algorithm for weighted partial triangulations, offering a compelling alternative to Markov chain-based techniques.