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Record W2335583346 · doi:10.2514/6.2008-6022

Method of Regular Simplexes: A Difference-Assisted Simplex-Based Search Algorithm

2008· article· en· W2335583346 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference · 2008
Typearticle
Languageen
FieldMathematics
TopicAdvanced Optimization Algorithms Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSimplex algorithmSimplexAlgorithmComputationComputer scienceFunction (biology)Path (computing)Set (abstract data type)Gradient descentPattern searchMathematical optimizationMathematicsLinear programmingCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

A new difierence-assisted simplex-based algorithm which has signiflcant advantages in handling optimization problems with large dimensions is introduced. First, fundamental principles are utilized to illustrate a theorem that provides the basis for computation of the simplex-based difierences. Then, a set of new outside-expansion and inside-contraction points are deflned, and with the help of function values at these points, a bi-directional search pattern is constructed. The primary search direction is determined using the blended difierence information that is readily available at the outset of the analysis. In order to compensate for inaccuracies associated with the difierence-assisted descent path, a secondary search direction is also deflned with the help of the available information about function values. Examples are given to demonstrate the accuracy and e‐ciency of the new approach, and the performance of the algorithm in parallel environments is discussed.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.129
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.388
Teacher spread0.295 · 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