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Record W4384575256 · doi:10.23952/asvao.5.2023.2.05

Facial reduction for the Shor SDP relaxation of QCQPs

2023· article· en· W4384575256 on OpenAlexvenueno aff
Hao Hu, Xinxin Li

Bibliographic record

VenueApplied Set-Valued Analysis and Optimization · 2023
Typearticle
Languageen
FieldMathematics
TopicAdvanced Optimization Algorithms Research
Canadian institutionsnot available
Fundersnot available
KeywordsSemidefinite programmingQuadratically constrained quadratic programMathematicsQuadratic programmingRelaxation (psychology)Quadratic growthSecond-order cone programmingSingularityQuadratic equationDimension (graph theory)Class (philosophy)Reduction (mathematics)Mathematical optimizationComputer scienceAlgorithmCombinatoricsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

We propose a special facial reduction algorithm (FRA) for the Shor semidefinite programming (SDP) relaxation of the quadratically constrained quadratic program (QCQP).Under the mild assumption, our special FRA only requires solving a linear programming problem instead of a semidefinite program.In particular, when applied to the binary quadratic program, the proposed special FRA needs fewer assumptions.From a computational perspective, this result improves the scalability and stability of the SDP approach for QCQP problems.In addition, we also discover a new class of semidefinite programs whose singularity degree can be computed easily.This new class complements the limited examples of singularity degrees in the literature.As a by-product, our special FRA can be used to upper bound the dimension of any set defined by quadratic inequalities.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.051
GPT teacher head0.351
Teacher spread0.300 · 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
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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