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Record W2767728188 · doi:10.1145/3338637.3338642

Computing the integer points of a polyhedron

2019· article· en· W2767728188 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

VenueACM communications in computer algebra · 2019
Typearticle
Languageen
FieldComputer Science
TopicPolynomial and algebraic computation
Canadian institutionsWestern University
Fundersnot available
KeywordsPolyhedronInteger (computer science)Integer programmingOmegaComputer scienceDiscrete mathematicsCombinatoricsQuantifier eliminationConstructiveLoop (graph theory)MathematicsAlgorithmTheoretical computer scienceProgramming language

Abstract

fetched live from OpenAlex

The integer points of polyhedral sets are of interest in many areas of mathematical sciences, see for instance the landmark textbooks of A. Schrijver [18] and A. Barvinok [3], as well as the compilation of articles [4]. One of these areas is the analysis and transformation of computer programs. For instance, integer programming [6] is used by P. Feautrier in the scheduling of for-loop nests [7], Barvinok's algorithm [2] for counting integer points in polyhedra is adapted by M. Köppe and S. Verdoolaege in [15] to answer questions like how many memory locations are touched by a for-loop nest. In [16], W. Pugh proposes an algorithm, called the Omega Test , for testing whether a polyhedron has integer points. In the same paper, W. Pugh shows how to use the Omega Test for performing dependence analysis [16] in for-loop nests. In [17], W. Pugh also suggests, without stating a formal algorithm, that the Omega Test could be used for quantifier elimination on Presburger formulas. This observation is a first motivation for the work presented here.

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 categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0080.005
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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