Symmetry Handling in Mixed‐Integer Programming
Bibliographic record
Abstract
Abstract This article focuses on solving integer programs whose feasible regions are highly symmetric. Symmetry has long been considered a curse in integer programming, and auxiliary (often extended) formulations are sought to reduce the amount of symmetry in an integer linear programming (ILP) formulation. The approach taken in this article describes methods that seek to exploit the symmetry, not avoid it by reformulation. A standard method for solving integer programs is branch‐and‐bound . In branch‐and‐bound, the set of feasible solutions is partitioned, forming more easily solved subproblems. The presence of symmetry means that many of these subproblems are equivalent in a sense we describe later. Only one member of each collection of equivalent subproblems needs to be solved. Failure to recognize that many subproblems are equivalent results in a waste of computational effort that can render an instance unsolvable by branch‐and‐bound. In this article, we describe methods that use the symmetry of the problem formulation to reduce the size of the feasible region.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".