A Lagrangean relaxation approach to the maximizing-number-of-connection problem in WDM networks
Bibliographic record
Abstract
The emergence of WDM (wavelength-division multiplexing) technology has provided great convenience in designing a wavelength routing optical network. We study the routing and wavelength assignment (RWA) problem with the objective of maximizing the number of connections in an all-optical WDM network, where the physical topology and network resource, as well as the connection demand matrix, are given. The paper provides an effective approach with reasonable computational complexity and good performance to solve this problem. We employ a decomposition approach using Lagrangean relaxation (LR) to simplify the solution procedure. The overall problem is decomposed into semi-lightpath level subproblems for the decision of the rejection and the wavelength and route selection from source to destination. We propose the MMCSLP (modified minimum cost semi-lightpath) algorithm to solve the specific form of the subproblem. At the higher level, Lagrange multipliers are updated iteratively by a subgradient method. Also, a heuristic algorithm is proposed to generate a feasible RWA scheme based on the dual solution. The performance of this approach on sample networks is compared with other recently proposed methods. The optimization results indicate that our algorithm can achieve a very good near-optimum solution, and it shows a great advantage in computational complexity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".