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Record W2607241396 · doi:10.5540/03.2017.005.01.0477

MMDA na resolucao da relaxacao por Programacao Semidefinida do Problema Quadratico de Alocacao

2017· article· en· W2607241396 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

VenueProceeding Series of the Brazilian Society of Computational and Applied Mathematics · 2017
Typearticle
Languageen
FieldMathematics
TopicAdvanced Optimization Algorithms Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPhysicsHumanitiesChemistryPhilosophy

Abstract

fetched live from OpenAlex

A relaxação por Programação Semidefinida (PSD) já demonstrou ser extrema- mente útil para muitos problemas difı́ceis da Otimização Discreta. Em especial, para o problema quadrático de alocação (PQA), conhecido por ser um dos problemas mais difı́ceis da classe NP-hard da Otimização Combinatória. Várias são as dificuldades encontradas ao se resolver a relaxação por PSD através dos métodos atuais. Neste trabalho, propomos a utilização do método do multiplicadores com direção alternada (MMDA) para resolver a relaxação por PSD do PQA. Obtemos, assim, iterações mais rápidas; um método rápido para se obter soluções com posto deficiente; e, também, uma forma simples de se adicionar desigualdades de planos de corte. Em nossos experimentos numéricos, obtivemos resultados mais robustos, eficientes e melhores aproximações para as soluções do PQA.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.404
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.323
Teacher spread0.283 · 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