Bibliographic record
Abstract
When the branch and bound method is used to solve a mixed-integer linear program (MIP), the Node selection (NS) and variable and direction selection (VDS) heuristics significantly affect the time to find the integer-optimal solution.Wojtaszek and Chinneck [2010] developed a new node selection heuristic including a modification on the best-projection method, a new backtrack triggering method and the active node search threshold.They suggested that this new node selection heuristic will provide the best branch and bound performance and improve the state of the art when coupled with the variable and direction selection heuristic by Driebeek and Tomlin.An observation in their work also indicated that a feasibilityoriented variable and direction selection method coupled with good node selection method can possibly provide the best MIP problem solution time.There are other works showing that MIP characteristics will influence the branch and bound performance.In this thesis, various variable and direction selection methods are tested coupled with the node selection heuristic by Wojtaszek and Chinneck.Rules are developed to select VDS / NS configuration depending on MIP characteristics.Empirical results show a new VDS / NS configuration outperforms the state of the art as well as the VDS / NS configuration found by Wojtaszek and Chinneck.The hypothesis that a feasibility -oriented VDS coupled with a good NS heuristic will provide the fastest MIP solution time is disproved.A new hybrid VDS/NS selection heuristic is developed and shown to provide better results than any individual VDS/NS configuration or the state-of-the-art default configuration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".