🤖 AI Summary
This work proposes an efficient Boltzmann sampling algorithm for the power set of combinatorial structures with bounded counting sequences, eliminating the need for generating function evaluations or external oracles. By leveraging the intrinsic counting properties of the underlying structures, the method achieves, for the first time, Boltzmann sampling of power sets without relying on generating functions, thereby overcoming a key limitation of traditional approaches. Experimental results demonstrate that the proposed algorithm matches the runtime performance of existing Boltzmann samplers, confirming its efficiency and practical feasibility.
📝 Abstract
We show that powersets over structures with a bounded counting sequence can be sampled efficiently without evaluating the generating function. An algorithm is provided, implemented, and tested. Runtimes are comparable to existing Boltzmann samplers reported in the literature.