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Record W4416944779 · doi:10.3390/math13233837

Bidirectional Algorithms for Polygon Triangulations and (m + 2)-Angulations via Fuss–Catalan Numbers

2025· article· en· W4416944779 on OpenAlexaff
Aybeyan Selimi, Muzafer Saračević, Lazar Stоšić, Ömer Aydın, Mahir Zajmović

Bibliographic record

VenueMathematics · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCorrectnessScalabilityPolygon (computer graphics)Bijection, injection and surjectionRegular polygonDecompositionRepresentation (politics)Convex polygon

Abstract

fetched live from OpenAlex

Polygon triangulations and their generalizations to m+2−angulations are fundamental in combinatorics and computational geometry. This paper presents a unified linear-time framework that establishes explicit bijections between m−Dyck words, planted m+1−ary trees, and m+2−angulations of convex polygons. We introduce stack-based and tree-based algorithms that enable reversible conversion between symbolic and geometric representations, prove their correctness and optimal complexity, and demonstrate their scalability through extensive experiments. The approach reveals a hierarchical decomposition encoded by Fuss–Catalan numbers, providing a compact and uniform representation for triangulations, quadrangulations, pentangulations, and higher-arity angulations. Experimental comparisons show clear advantages over rotation-based methods in both runtime and memory usage. The framework offers a general combinatorial foundation that supports efficient enumeration, compressed representation, and extensions to higher-dimensional or non-convex settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.769
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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