Routing and wavelength assignment with protection: A quadratic unconstrained binary optimization approach enabled by Digital Annealer technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Routing and wavelength assignment with protection is an important problem in telecommunications. Given an optical network and incoming connection requests, a commonly studied variant of the problem aims to grant a maximum number of requests by assigning lightpaths with minimum network resource usage, while ensuring the provided services remain functional in the case of a single-link failure through dedicated protection paths. We consider a version where alternative lightpaths for requests are assumed to be given as a precomputed set and show that it is NP-hard. We formulate the problem as an Integer Programming (IP) model and also use it as a foundation to develop a Quadratic Unconstrained Binary Optimization (QUBO) model. We present necessary and sufficient conditions on objective function parameters to prioritize request granting objective over wavelength-link usage for both models, and a sufficient condition ensuring the exactness of the QUBO model. Moreover, we implement a problem-specific branch-and-cut algorithm for the IP model and employ a new quantum-inspired technology, Digital Annealer (DA), for the QUBO model. We conduct computational experiments on a large suite of nontrivial instances to assess the efficiency and efficacy of all of these approaches as well as two problem-specific heuristics. Although the objective penalty coefficient values that guarantee the exactness of the QUBO model were outside the acceptable range for DA, it always yielded feasible solutions even with smaller values in practice. The results show that the emerging technology DA outperforms the considered techniques coupled with GUROBI in finding mostly significantly better or as good solutions in two minutes compared to two-hour run time, whereas problem-specific heuristics fail to be competitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it