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Analytic Combinatorics of d-Set Mappings and Their Applications

  • Toma Diaconescu-Grabari
  • Daniel Panario
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Abstract

A d-set mapping is a function acting on a domain X equipped with a partition into d disjoint subsets. While standard functions represent $1$-set mappings, generalizations to arbitrary d-partite structures appear naturally across discrete mathematics. In this paper, we develop an analytic combinatorial framework to quantify the functional graphs of these mappings. By leveraging generating functions and singularity analysis, we derive exact asymptotic expansions for macroscopic graph properties as the cardinality of X tends to infinity, including the expected number of connected components, cyclic nodes, and tail lengths. We demonstrate the efficacy of this framework by recovering the classical bipartite mapping results of Hansen and Jaworski, and successfully generalize these mechanisms to arbitrary d-set mappings, providing the foundational architecture to establish their probabilistic limit laws.

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Cite

BibTeXRIS
BibTeX
@misc{diaconescugrabari2026analytic,
  title = {{Analytic Combinatorics of $d$-Set Mappings and Their Applications}},
  author = {Toma Diaconescu-Grabari and Daniel Panario},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.30191},
  eprint = {2609.30191},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.30191},
}
RIS
TY  - UNPB
TI  - Analytic Combinatorics of d-Set Mappings and Their Applications
AU  - Toma Diaconescu-Grabari
AU  - Daniel Panario
PY  - 2026
DA  - 2026/09/24
PB  - arXiv
DO  - 10.48550/arxiv.2609.30191
UR  - https://arxiv.org/abs/2609.30191
AB  - A d-set mapping is a function acting on a domain X equipped with a partition into d disjoint subsets. While standard functions represent $1$-set mappings, generalizations to arbitrary d-partite structures appear naturally across discrete mathematics. In this paper, we develop an analytic combinatorial framework to quantify the functional graphs of these mappings. By leveraging generating functions and singularity analysis, we derive exact asymptotic expansions for macroscopic graph properties as the cardinality of X tends to infinity, including the expected number of connected components, cyclic nodes, and tail lengths. We demonstrate the efficacy of this framework by recovering the classical bipartite mapping results of Hansen and Jaworski, and successfully generalize these mechanisms to arbitrary d-set mappings, providing the foundational architecture to establish their probabilistic limit laws.
ER  -