MétaCan
Menu
Back to cohort

Convex optimization and the smallest ball problem

2023· article· en· W4388827630 on OpenAlexaff
Junchi Yang

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBall (mathematics)Regular polygonMathematicsMathematical optimizationOptimization problemExistential quantificationMathematical problemComputer scienceCombinatoricsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract The Smallest Ball Problem is a famous problem in mathematics that was proposed by James Joseph Sylvester. In the past, many algorithms to solve this problem were founded but there was only a limited number of researches that focused on the rigorous mathematical proof of this problem. Thus, the goal of this essay is to provide a rigorous proof of the claim that the smallest enclosing ball must exist and it is unique. In this essay, the Smallest Ball Problem will be converted into a convex optimization problem and the result that the smallest enclosing ball exists and is unique can be proved by proving the optimal solution of this programming problem exists. The meaning of this research is to give a theoretically mathematical proof of the Smallest Ball Problem so that it can tell the algorithms to solve this problem can always work. Thus, it can also ensure the effectiveness of all the related algorithms.

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: none
Teacher disagreement score0.958
Threshold uncertainty score0.226

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 communication0.0000.001
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 head0.228
Teacher spread0.207 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicComputational Geometry and Mesh GenerationFrench-language works237,207