Literature review on Benders cut selection and a multiple cut generation scheme
Why this work is in the frame
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Bibliographic record
Abstract
One way to improve the efficiency of the Benders Decomposition Method is the generation of high-quality cuts. This paper presents a literature review on Benders cut selection methods, followed by the development of a multiple cut generation scheme that remains effective regardless of whether the Subproblem is decomposable or not. The proposed approach builds upon the work of Brandenberg and Stursberg (Mathematical Methods of Operations Research, 94:383–412, 2021), who developed a unifying framework for generating Benders cuts by identifying appropriate parametrizations for the cost vector of the objective function used to optimize over the alternative polyhedron. Our analytical results provide further insights into the structure of those parametrizations. Experiments are conducted to demonstrate the effectiveness of our method compared to the classical Benders Algorithm.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it