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Instance Formats for Mathematical Optimization Models

2011· other· en· W2135362276 on OpenAlexaff
Horand I. Gassmann, Jun Ma, Kipp Martin

Bibliographic record

VenueWiley Encyclopedia of Operations Research and Management Science · 2011
Typeother
Languageen
FieldEngineering
TopicOptimization and Mathematical Programming
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSolverComputer scienceSoftwareSet (abstract data type)Focus (optics)Mathematical softwareSoftware engineeringSoftware packageTheoretical computer scienceProgramming language

Abstract

fetched live from OpenAlex

Abstract In order to use a piece of software—optimization solver, simulation package, model analyzer, and so on—on a mathematical model, it is necessary to communicate a model instance to the software. Additionally, such software typically accepts options to specify tolerances, set algorithmic parameters, and so on. Finally, the software must communicate the result instance back to the user. In this article we focus on optimization solvers and review existing formats for optimization instances, solver option instances and result instances, and describe the research issues involved in designing these instance formats.

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)0.009
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.006
Science and technology studies0.0010.002
Scholarly communication0.0110.015
Open science0.0060.004
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0210.007

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 head0.047
GPT teacher head0.312
Teacher spread0.265 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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