Lagrangian Dual Decision Rules for Multistage Stochastic Mixed-Integer Programming
Why this work is in the frame
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Bibliographic record
Abstract
On Decision Rules for Multistage Stochastic Programs with Mixed-Integer Decisions Multistage stochastic programming is a field of stochastic optimization for addressing sequential decision-making problems defined over a stochastic process with a given probability distribution. The solution to such a problem is a decision rule (policy) that maps the history of observations to the decisions. Design of the decision rules in the presence of mixed-integer decisions is quite challenging. In “Lagrangian Dual Decision Rules for Multistage Stochastic Mixed-Integer Programming,” Daryalal, Bodur, and Luedtke introduce Lagrangian dual decision rules, where linear decision rules are applied to dual multipliers associated with Lagrangian duals of a multistage stochastic mixed-integer programming (MSMIP) model. The restricted decisions are then used in the development of new primal- and dual-bounding methods. This yields a new general-purpose approximation approach for MSMIP, free of strong assumptions made in the literature, such as stagewise independence or existence of a tractable-sized scenario-tree representation.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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