A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
Convergence behavior of the Newton iteration for first order differential equations
EUROSAM '79 Proceedings of the International Symposiumon on Symbolic and Algebraic Computation
CAAP '92 Proceedings of the 17th Colloquium on Trees in Algebra and Programming
Feature extraction from noisy speech signals
Feature extraction from noisy speech signals
Labelled formal languages and their uses
Labelled formal languages and their uses
Boltzmann Samplers for the Random Generation of Combinatorial Structures
Combinatorics, Probability and Computing
Analytic Combinatorics
Boltzmann Samplers, Pólya Theory, and Cycle Pointing
SIAM Journal on Computing
Random generation of combinatorial structures: Boltzmann samplers and beyond
Proceedings of the Winter Simulation Conference
Asymptotic analysis and random sampling of digitally convex polyominoes
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Boltzmann samplers for v-balanced cycles
Theoretical Computer Science
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In the framework of analytic combinatorics, Boltzmann models give rise to efficient algorithms for the random generation of combinatorial objects. This paper proposes an efficient Boltzmann sampler for ordered structures defined by first-order differential specifications. Under an abstract real-arithmetic computation model, our algorithm is of linear complexity for free generation; in addition, for many classical structures, the complexity is also linear when a small tolerance is allowed on the size of the generated object. The resulting implementation makes it possible to generate very large random objects, such as increasing trees, in a few seconds on a standard machine.