Title
Boltzmann Sampling of Unlabeled Structures.
Abstract
Boltzmann models from statistical physics combined with methods from analytic combinatorics give rise to efficient algorithms for the random generation of unlabelled objects. The resulting algorithms generate in an unbiased manner discrete configurations that may have nontrivial symmetries, and they do so by means of real-arithmetic computations. We present a collection of construction rules for such samplers, which applies to a wide variety of combinatorial classes, including integer partitions, necklaces, unlabelled functional graphs, dictionaries, series-parallel circuits, term trees and acyclic molecules obeying a variety of constraints, and so on. Under an abstract real-arithmetic computation model, the algorithms are, for many classical structures, of linear complexity provided a small tolerance is allowed on the size of the object drawn. As opposed to many of their discrete competitors, the resulting programs routinely make it possible to generate random objects of sizes in the range 10(4)-10(6).
Year
DOI
Venue
2007
10.1137/1.9781611972979.5
SIAM Proceedings Series
Field
DocType
Citations 
Analytic combinatorics,Graph,Algorithm,Sampling (statistics),Linear complexity,Partition (number theory),Boltzmann constant,Homogeneous space,Mathematics,Computation
Conference
3
PageRank 
References 
Authors
0.40
0
3
Name
Order
Citations
PageRank
Philippe Flajolet13466523.08
Éric Fusy219821.95
Carine Pivoteau3244.26