A Branch, Price and Cut Approach for Optimal Traffic Grooming in WDM Optical Networks
Bibliographic record
Abstract
The standard approach of using multi-commodity network flow (MCNF) techniques for designing optimal WDM networks often lead to computationally difficult Mixed Integer Linear Programs (MILP) which work only on small networks. Modern Operations Research (OR) techniques may be helpful when developing efficient algorithms for large WDM networks. This paper explores the Branch, Price and Cut techniques for designing optimal WDM optical networks. We have studied a well-known problem in WDM networks - non-bifurcated traffic grooming over a specified logical topology. The standard way to solve this problem is to view it as a MCNF problem and solve the resulting MILP using a commercial MILP solver package, such as the ILOG CPLEX to give us an optimum traffic grooming strategy. The number of binary variables and the number of constraints of the MCNF problems increases with the network size and tools such as the CPLEX solver takes increasingly longer time. We have shown how we can take advantage of the structural properties of this problem and solve it efficiently using modern Operations Research techniques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".