Tuning as convex optimisation: a polynomial tuner for multi-parametric combinatorial samplers

02/26/2020 ∙ by Maciej Bendkowski, et al. ∙ 0

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, or even RNA sequences. In their multi-parametric variants, combinatorial samplers are 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 number of certain sub-patterns in generated strings. However, such a flexible control requires an additional and nontrivial tuning procedure. Using techniques of convex optimisation, we present an efficient polynomial tuning algorithm for multi-parametric combinatorial specifications. For a given combinatorial system of description length L with d tuning parameters and target size parameter value n, our algorithm runs in time O(d^3.5 L log n). We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called Pólya specifications. We show how our method can be adapted to a broad range of less typical combinatorial constructions, including symmetric polynomials, labelled sets and cycles with cardinality lower bounds, simple increasing trees or substitutions. Finally, we discuss some practical aspects of our prototype tuner implementation and provide its benchmark results.



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