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Record W4414847476 · doi:10.48550/arxiv.2505.08812

An Algorithm to compute the Kronecker cone and other moment cones

2025· preprint· en· W4414847476 on OpenAlexaff
Michaël Bulois, Roland Denis, Nicolas Ressayre

Bibliographic record

VenueArXiv.org · 2025
Typepreprint
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsKronecker deltaCone (formal languages)MorphismConvex coneRepresentation (politics)Algebraic numberAlgebra over a fieldConic sectionComputation

Abstract

fetched live from OpenAlex

We describe a new algorithm that computes the minimal list of inequalities for the moment cone of any representation of a complex reductive group, with implementation details for two fundamental cases: the Kronecker cone (governing the asymptotic support of Kronecker coefficients) and the fermionic cone. These correspond to the actions of ${\mathrm GL}\_{d\_1}({\mathbb C})\times\cdots\times {\mathrm GL}\_{d\_s}({\mathbb C})$ on ${\mathbb C}^{d\_1}\otimes\cdots\otimes {\mathbb C}^{d\_s}$ and ${\mathrm GL}\_d({\mathbb C})$ on $\bigwedge^r{\mathbb C}^d$, respectively. An implementation for these two cases in Python-Sage is available at https://ea-icj.github.io/. Our work overcomes the fundamental limitations that previously restricted such computations to cases like ${\mathbb C}^4\otimes{\mathbb C}^4\otimes{\mathbb C}^4$. The state-of-the-art method by Vergne-Walter faced two major bottlenecks: one from combinatorial geometry in finite-dimensional vector spaces, and another from deciding whether certain dominant morphisms are birational - a problem in effective algebraic geometry that lacked a direct algorithmic solution. We surmount these obstacles by: a novel use of Weyl group actions to master combinatorial complexity, and an original algorithm for deciding birationality that replaces previous workarounds relying on convex geometry. Our approach allow us to tackle problems at a new scale. We compute the minimal list of 5,333 (up to $\mathfrak S\_3$) inequalities for the Kronecker cone ${\mathbb C}^6\otimes{\mathbb C}^6\otimes{\mathbb C}^6$ in 2 hours. Furthermore, a parallel implementation computes the 64,792 (up to $\mathfrak S\_3$) inequalities for ${\mathbb C}^7\otimes{\mathbb C}^7\otimes{\mathbb C}^7$ in 188 hours.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0360.014

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 head0.030
GPT teacher head0.287
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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