A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
Random Sampling from Boltzmann Principles
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Boltzmann Samplers for the Random Generation of Combinatorial Structures
Combinatorics, Probability and Computing
Analytic Combinatorics
Boltzmann samplers for first-order differential specifications
Discrete Applied Mathematics
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The Boltzmann model for the random generation of "decomposable" combinatorial structures is a set of techniques that allows for efficient random sampling algorithms for a large class of families of discrete objects. The usual requirement of sampling uniformly from the set of objects of a given size is somehow relaxed, though uniformity among objects of each size is still ensured. Generating functions, rather than the enumeration sequences they are based on, are the crucial ingredient. We give a brief description of the general theory, as well as a number of newer developments.