Exact-size Sampling of Enriched Trees in Linear Time

Abstract

Various combinatorial classes such as outerplanar graphs and maps, series-parallel graphs, substitution-closed classes of permutations and many more allow bijective encodings by so-called enriched trees, which are rooted trees with additional structure on the offspring of each node. Using this universal description we develop sampling procedures that uniformly generate objects from this classes with a given size n in expected time O(n). The key ingredient is a representation of enriched trees in terms of decorated Bienaymé-Galton-Watson trees, which allows us to develop a novel combination of Devroye's efficient sampler for trees [21] with Boltzmann sampling techniques. Additionally, we construct expected linear time samplers for critical Bienaymé-Galton-Watson trees having exactly n (out of ≥ n total) nodes with outdegree in some fixed set, enabling uniform generation for many combinatorial classes such as dissections of polygons.

Holonomic equations and efficient random generation of binary trees

“…Like Alonso's, the algorithm they propose has a linear expected complexity, due to failures similar to those of our Schröder tree generation algorithm. Let us also mention generic Boltzmann's samplers with exact-size which apply among others to Motzkin trees [7,22] and generic algorithms [11,23] with linear expected complexity.…”