Enhanced RWA Exact Solution with a New Lightpath Decomposition Algorithm
Bibliographic record
Abstract
The Routing and Wavelength Assignment (RWA) problem has been extensively studied, using both heuristic and exact algorithms. It is now possible to solve exactly large practical RWA instances, i.e., networks with up to 100 nodes and 100 wavelengths in reasonable computational times. In this study, we revisit the lightpath decomposition mathematical model and show that it can lead to a simple and extremely efficient decomposition algorithm. Computational results show that very large RWA instances can be solved exactly within a few minutes (up to 42 times faster than the previous best algorithm, and 5 times faster on average), with an improved proven accuracy (less than 1% for nearly all data sets). In addition, we provide an analysis of the characteristics of the optimal RWA provisioning, in terms of the percentage of the number of routes corresponding to shortest paths. Indeed, their number may decrease to about 50% in some data instances, due to a possible short-sighted network dimensioning.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".