Revue Des Inégalités Valides Pertinentes Aux Problèmes Des Conception De Réseaux
Bibliographic record
Abstract
The objective of this paper is to present a survey of relevant valid inequalities for the multi-commodity capacitated fixed-charge network design problem. We present first a review of the relevant literature on relaxations and heuristic approaches. We observe that these approaches alone are not sufficient to solve large-scale instances of the problem. However, a combination or these approaches and polyhedral methods appears very promising. Then, we present three classes of relevant valid inequalities for the problem that have been studied for related problems. We present as well experimental results on the general implementations of some of these inequalities in a state-of-the-art commercial software. These experiments point towards the necessity of profound polyhedral studies in order to solve efficiently large-scale instances of the problem.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".