2021
DOI: 10.1017/s0963548321000547
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Tuning as convex optimisation: a polynomial tuner for multi-parametric combinatorial samplers

Abstract: Combinatorial samplers are algorithmic schemes devised for the approximate- and exact-size generation of large random combinatorial structures, such as context-free words, various tree-like data structures, maps, tilings, RNA molecules. They can be adapted to combinatorial specifications with additional parameters, allowing for a more flexible control over the output profile of parametrised combinatorial patterns. One can control, for instance, the number of leaves, profile of node degrees in trees or the numb… Show more

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Cited by 2 publications
(1 citation statement)
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“…Having a specification for the combinatorial class for the shape partitions of the 2 × n grid allows us to use universal random samplers such as the recursive method [4] or Boltzmann samplers [2,1]. From a given specification, these samplers automatically build a uniform draw for the objects of size n.…”
Section: Random Samplingmentioning
confidence: 99%
“…Having a specification for the combinatorial class for the shape partitions of the 2 × n grid allows us to use universal random samplers such as the recursive method [4] or Boltzmann samplers [2,1]. From a given specification, these samplers automatically build a uniform draw for the objects of size n.…”
Section: Random Samplingmentioning
confidence: 99%